librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 138	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-04-20 06:26:59	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 429 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-04-20 06:26:36	card stringlengths 11 1.01M
glif-loradex-trainer/swapagrawal14_anamika_unknown_reincarnation	glif-loradex-trainer	"2024-11-18T14:26:59Z"	16	1	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	"2024-11-18T14:26:22Z"	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1731939846111__000003000_0.jpg text: entrapped in a curse anamika_haunts - output: url: samples/1731939871024__000003000_1.jpg text: haunted, red anamika_haunts - output: url: samples/1731939895965__000003000_2.jpg text: a girl shadow chasing a mananamika_haunts - output: url: samples/1731939920916__000003000_3.jpg text: wounded man anamika_haunts - output: url: samples/1731939945918__000003000_4.jpg text: doll anamika_haunts - output: url: samples/1731939971042__000003000_5.jpg text: witchcraft anamika_haunts base_model: black-forest-labs/FLUX.1-dev trigger: anamika_haunts instance_prompt: anamika_haunts license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # anamika_unknown_reincarnation Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `swapagrawal14`. <Gallery /> ## Trigger words You should use `anamika_haunts` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/swapagrawal14_anamika_unknown_reincarnation/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
alitolga/electra-base-generator-rank4	alitolga	"2024-02-12T13:36:31Z"	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "region:us" ]	null	"2024-02-12T13:35:29Z"	--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer model-index: - name: electra-base-generator-rank4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank4 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.3543 \| 1.0 \| 179 \| 3.9048 \| \| 3.7115 \| 2.0 \| 358 \| 3.3385 \| \| 3.4042 \| 3.0 \| 537 \| 3.2603 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
davidschulte/ESM_kuroneko5943__snap21_Pet_Supplies_5	davidschulte	"2025-03-28T12:57:55Z"	24	0	null	[ "safetensors", "embedding_space_map", "BaseLM:bert-base-multilingual-uncased", "dataset:kuroneko5943/snap21", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "region:us" ]	null	"2024-12-06T11:06:48Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
ramyanjn/ChemBERTaFTTox	ramyanjn	"2024-05-27T07:27:47Z"	163	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-05-27T07:27:46Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ali2066/finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58	ali2066	"2022-02-27T16:44:27Z"	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2022-03-02T23:29:05Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Accuracy: 0.8263 - F1: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3685 \| 0.8293 \| 0.8911 \| \| No log \| 2.0 \| 390 \| 0.3495 \| 0.8415 \| 0.8992 \| \| 0.4065 \| 3.0 \| 585 \| 0.3744 \| 0.8463 \| 0.9014 \| \| 0.4065 \| 4.0 \| 780 \| 0.4260 \| 0.8427 \| 0.8980 \| \| 0.4065 \| 5.0 \| 975 \| 0.4548 \| 0.8366 \| 0.8940 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
UmerHA/ConrolNetXS-SDXL-canny	UmerHA	"2023-12-04T15:58:41Z"	21	0	diffusers	[ "diffusers", "safetensors", "arxiv:2302.05543", "license:openrail", "region:us" ]	null	"2023-11-13T17:27:59Z"	--- license: openrail --- # ControlNet-XS model for StableDiffusionXL and canny edges input 🔬 Original paper and models by https://github.com/vislearn/ControlNet-XS 👷🏽‍♂️ Translated into diffusers architecture by https://twitter.com/UmerHAdil This model is trained for use with [StableDiffusionXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) --- ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produces good results. As with the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process. Using ControlNet-XS instead of regular ControlNet will produce images of roughly the same quality, but 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb)) and with ~45% less memory usage. --- Other ControlNet-XS models: - [StableDiffusion-XL and depth input](https://huggingface.co/UmerHA/ConrolNetXS-SDXL-depth) - [StableDiffusion 2.1 and canny edges input](https://huggingface.co/UmerHA/ConrolNetXS-SD2.1-canny) - [StableDiffusion 2.1 and depth input](https://huggingface.co/UmerHA/ConrolNetXS-SD2.1-depth)
Falafelki/MyshkinMix	Falafelki	"2025-03-02T09:43:49Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-03-02T09:27:41Z"	--- license: apache-2.0 ---
CLMBR/full-transformer-3	CLMBR	"2024-02-03T08:28:16Z"	4	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-01-26T10:06:44Z"	--- tags: - generated_from_trainer model-index: - name: full2-transformer-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # full2-transformer-3 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.2206 \| 0.03 \| 76320 \| 4.1916 \| \| 4.0169 \| 1.03 \| 152640 \| 4.0236 \| \| 3.9099 \| 0.03 \| 228960 \| 3.9506 \| \| 3.8437 \| 1.03 \| 305280 \| 3.9106 \| \| 3.7918 \| 0.03 \| 381600 \| 3.8857 \| \| 3.7519 \| 1.03 \| 457920 \| 3.8689 \| \| 3.7218 \| 0.03 \| 534240 \| 3.8581 \| \| 3.6904 \| 1.03 \| 610560 \| 3.8518 \| \| 3.6603 \| 0.03 \| 686880 \| 3.8468 \| \| 3.6377 \| 1.03 \| 763200 \| 3.8447 \| \| 3.6135 \| 0.03 \| 839520 \| 3.8432 \| \| 3.5916 \| 1.03 \| 915840 \| 3.8415 \| \| 3.5781 \| 0.03 \| 992160 \| 3.8417 \| \| 3.5586 \| 1.03 \| 1068480 \| 3.8418 \| \| 3.5407 \| 0.03 \| 1144800 \| 3.8439 \| \| 3.525 \| 1.03 \| 1221120 \| 3.8447 \| \| 3.5057 \| 0.03 \| 1297440 \| 3.8447 \| \| 3.4938 \| 1.03 \| 1373760 \| 3.8463 \| \| 3.4784 \| 0.03 \| 1450080 \| 3.8474 \| \| 3.4732 \| 1.03 \| 1526400 \| 3.8485 \| \| 3.4634 \| 0.03 \| 1602720 \| 3.8501 \| \| 3.4544 \| 1.03 \| 1679040 \| 3.8525 \| \| 3.448 \| 0.03 \| 1755360 \| 3.8527 \| \| 3.4382 \| 0.03 \| 1831680 \| 3.8545 \| \| 3.4259 \| 0.03 \| 1908000 \| 3.8566 \| \| 3.4159 \| 1.03 \| 1984320 \| 3.8575 \| \| 3.4029 \| 0.03 \| 2060640 \| 3.8589 \| \| 3.3911 \| 0.03 \| 2136960 \| 3.8601 \| \| 3.3832 \| 0.03 \| 2213280 \| 3.8616 \| \| 3.3725 \| 0.03 \| 2289600 \| 3.8614 \| \| 3.3585 \| 1.03 \| 2365920 \| 3.8622 \| \| 3.3487 \| 0.03 \| 2442240 \| 3.8639 \| \| 3.3357 \| 1.03 \| 2518560 \| 3.8639 \| \| 3.3261 \| 0.03 \| 2594880 \| 3.8644 \| \| 3.3146 \| 0.03 \| 2671200 \| 3.8653 \| \| 3.3102 \| 1.03 \| 2747520 \| 3.8654 \| \| 3.3041 \| 0.03 \| 2823840 \| 3.8652 \| \| 3.2998 \| 1.03 \| 2900160 \| 3.8649 \| \| 3.2998 \| 0.03 \| 2976480 \| 3.8644 \| \| 3.2926 \| 1.02 \| 3052726 \| 3.8634 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
eugeneware/ddpm-butterflies-128	eugeneware	"2022-08-13T16:14:52Z"	4	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	"2022-08-13T15:45:43Z"	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/eugeneware/ddpm-butterflies-128/tensorboard?#scalars)
satyanshu404/long-t5-local-base-finetuned-justification-v09	satyanshu404	"2024-04-13T05:24:14Z"	31	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:google/long-t5-local-base", "base_model:finetune:google/long-t5-local-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-04-12T03:22:05Z"	--- license: apache-2.0 base_model: google/long-t5-local-base tags: - generated_from_trainer model-index: - name: long-t5-local-base-finetuned-justification-v09 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # long-t5-local-base-finetuned-justification-v09 This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| No log \| 1.0 \| 338 \| 20.0779 \| \| 26.617 \| 2.0 \| 676 \| 17.6054 \| \| 22.6857 \| 3.0 \| 1014 \| 15.1205 \| \| 22.6857 \| 4.0 \| 1352 \| 12.4837 \| \| 18.639 \| 5.0 \| 1690 \| 9.9114 \| \| 14.4577 \| 6.0 \| 2028 \| 8.0629 \| \| 14.4577 \| 7.0 \| 2366 \| 7.5255 \| \| 10.7004 \| 8.0 \| 2704 \| 7.4006 \| \| 7.8669 \| 9.0 \| 3042 \| 7.2827 \| \| 7.8669 \| 10.0 \| 3380 \| 7.1306 \| \| 6.3058 \| 11.0 \| 3718 \| 6.9313 \| \| 5.3507 \| 12.0 \| 4056 \| 6.6880 \| \| 5.3507 \| 13.0 \| 4394 \| 6.3980 \| \| 5.0661 \| 14.0 \| 4732 \| 6.1019 \| \| 4.6576 \| 15.0 \| 5070 \| 5.7985 \| \| 4.6576 \| 16.0 \| 5408 \| 5.4902 \| \| 4.374 \| 17.0 \| 5746 \| 5.2013 \| \| 4.1022 \| 18.0 \| 6084 \| 4.9162 \| \| 4.1022 \| 19.0 \| 6422 \| 4.6802 \| \| 3.9773 \| 20.0 \| 6760 \| 4.4889 \| \| 3.7391 \| 21.0 \| 7098 \| 4.3299 \| \| 3.7391 \| 22.0 \| 7436 \| 4.2127 \| \| 3.6007 \| 23.0 \| 7774 \| 4.1193 \| \| 3.472 \| 24.0 \| 8112 \| 4.0468 \| \| 3.472 \| 25.0 \| 8450 \| 3.9895 \| \| 3.3327 \| 26.0 \| 8788 \| 3.9357 \| \| 3.3196 \| 27.0 \| 9126 \| 3.8895 \| \| 3.3196 \| 28.0 \| 9464 \| 3.8449 \| \| 3.229 \| 29.0 \| 9802 \| 3.8026 \| \| 3.1795 \| 30.0 \| 10140 \| 3.7613 \| \| 3.1795 \| 31.0 \| 10478 \| 3.7200 \| \| 3.0775 \| 32.0 \| 10816 \| 3.6811 \| \| 3.065 \| 33.0 \| 11154 \| 3.6424 \| \| 3.065 \| 34.0 \| 11492 \| 3.6048 \| \| 3.0145 \| 35.0 \| 11830 \| 3.5750 \| \| 2.9987 \| 36.0 \| 12168 \| 3.5381 \| \| 2.9096 \| 37.0 \| 12506 \| 3.5031 \| \| 2.9096 \| 38.0 \| 12844 \| 3.4699 \| \| 2.8816 \| 39.0 \| 13182 \| 3.4402 \| \| 2.8767 \| 40.0 \| 13520 \| 3.4116 \| \| 2.8767 \| 41.0 \| 13858 \| 3.3847 \| \| 2.8189 \| 42.0 \| 14196 \| 3.3540 \| \| 2.8297 \| 43.0 \| 14534 \| 3.3275 \| \| 2.8297 \| 44.0 \| 14872 \| 3.3008 \| \| 2.7376 \| 45.0 \| 15210 \| 3.2745 \| \| 2.7519 \| 46.0 \| 15548 \| 3.2521 \| \| 2.7519 \| 47.0 \| 15886 \| 3.2273 \| \| 2.7207 \| 48.0 \| 16224 \| 3.2038 \| \| 2.7056 \| 49.0 \| 16562 \| 3.1822 \| \| 2.7056 \| 50.0 \| 16900 \| 3.1619 \| \| 2.6539 \| 51.0 \| 17238 \| 3.1426 \| \| 2.6393 \| 52.0 \| 17576 \| 3.1219 \| \| 2.6393 \| 53.0 \| 17914 \| 3.1015 \| \| 2.6396 \| 54.0 \| 18252 \| 3.0818 \| \| 2.6029 \| 55.0 \| 18590 \| 3.0604 \| \| 2.6029 \| 56.0 \| 18928 \| 3.0448 \| \| 2.5527 \| 57.0 \| 19266 \| 3.0251 \| \| 2.5793 \| 58.0 \| 19604 \| 3.0069 \| \| 2.5793 \| 59.0 \| 19942 \| 2.9911 \| \| 2.5443 \| 60.0 \| 20280 \| 2.9724 \| \| 2.5083 \| 61.0 \| 20618 \| 2.9560 \| \| 2.5083 \| 62.0 \| 20956 \| 2.9387 \| \| 2.5368 \| 63.0 \| 21294 \| 2.9205 \| \| 2.4771 \| 64.0 \| 21632 \| 2.9040 \| \| 2.4771 \| 65.0 \| 21970 \| 2.8895 \| \| 2.4875 \| 66.0 \| 22308 \| 2.8701 \| \| 2.4532 \| 67.0 \| 22646 \| 2.8570 \| \| 2.4532 \| 68.0 \| 22984 \| 2.8397 \| \| 2.4276 \| 69.0 \| 23322 \| 2.8243 \| \| 2.4279 \| 70.0 \| 23660 \| 2.8110 \| \| 2.4279 \| 71.0 \| 23998 \| 2.7950 \| \| 2.3944 \| 72.0 \| 24336 \| 2.7816 \| \| 2.3907 \| 73.0 \| 24674 \| 2.7704 \| \| 2.4014 \| 74.0 \| 25012 \| 2.7564 \| \| 2.4014 \| 75.0 \| 25350 \| 2.7423 \| \| 2.3698 \| 76.0 \| 25688 \| 2.7295 \| \| 2.3408 \| 77.0 \| 26026 \| 2.7172 \| \| 2.3408 \| 78.0 \| 26364 \| 2.7046 \| \| 2.3404 \| 79.0 \| 26702 \| 2.6916 \| \| 2.316 \| 80.0 \| 27040 \| 2.6827 \| \| 2.316 \| 81.0 \| 27378 \| 2.6706 \| \| 2.3322 \| 82.0 \| 27716 \| 2.6607 \| \| 2.3005 \| 83.0 \| 28054 \| 2.6500 \| \| 2.3005 \| 84.0 \| 28392 \| 2.6408 \| \| 2.2661 \| 85.0 \| 28730 \| 2.6315 \| \| 2.2946 \| 86.0 \| 29068 \| 2.6231 \| \| 2.2946 \| 87.0 \| 29406 \| 2.6131 \| \| 2.2493 \| 88.0 \| 29744 \| 2.6034 \| \| 2.2623 \| 89.0 \| 30082 \| 2.5940 \| \| 2.2623 \| 90.0 \| 30420 \| 2.5857 \| \| 2.2464 \| 91.0 \| 30758 \| 2.5777 \| \| 2.2203 \| 92.0 \| 31096 \| 2.5714 \| \| 2.2203 \| 93.0 \| 31434 \| 2.5641 \| \| 2.233 \| 94.0 \| 31772 \| 2.5562 \| \| 2.2101 \| 95.0 \| 32110 \| 2.5493 \| \| 2.2101 \| 96.0 \| 32448 \| 2.5435 \| \| 2.2321 \| 97.0 \| 32786 \| 2.5376 \| \| 2.1743 \| 98.0 \| 33124 \| 2.5304 \| \| 2.1743 \| 99.0 \| 33462 \| 2.5253 \| \| 2.2033 \| 100.0 \| 33800 \| 2.5202 \| \| 2.1874 \| 101.0 \| 34138 \| 2.5154 \| \| 2.1874 \| 102.0 \| 34476 \| 2.5092 \| \| 2.1615 \| 103.0 \| 34814 \| 2.5054 \| \| 2.1565 \| 104.0 \| 35152 \| 2.5001 \| \| 2.1565 \| 105.0 \| 35490 \| 2.4950 \| \| 2.152 \| 106.0 \| 35828 \| 2.4897 \| \| 2.1398 \| 107.0 \| 36166 \| 2.4851 \| \| 2.1424 \| 108.0 \| 36504 \| 2.4812 \| \| 2.1424 \| 109.0 \| 36842 \| 2.4767 \| \| 2.1272 \| 110.0 \| 37180 \| 2.4734 \| \| 2.1171 \| 111.0 \| 37518 \| 2.4686 \| \| 2.1171 \| 112.0 \| 37856 \| 2.4649 \| \| 2.1325 \| 113.0 \| 38194 \| 2.4597 \| \| 2.0975 \| 114.0 \| 38532 \| 2.4567 \| \| 2.0975 \| 115.0 \| 38870 \| 2.4523 \| \| 2.1156 \| 116.0 \| 39208 \| 2.4487 \| \| 2.0628 \| 117.0 \| 39546 \| 2.4452 \| \| 2.0628 \| 118.0 \| 39884 \| 2.4417 \| \| 2.1061 \| 119.0 \| 40222 \| 2.4385 \| \| 2.0897 \| 120.0 \| 40560 \| 2.4343 \| \| 2.0897 \| 121.0 \| 40898 \| 2.4316 \| \| 2.083 \| 122.0 \| 41236 \| 2.4271 \| \| 2.0693 \| 123.0 \| 41574 \| 2.4241 \| \| 2.0693 \| 124.0 \| 41912 \| 2.4212 \| \| 2.0748 \| 125.0 \| 42250 \| 2.4180 \| \| 2.0497 \| 126.0 \| 42588 \| 2.4152 \| \| 2.0497 \| 127.0 \| 42926 \| 2.4128 \| \| 2.0803 \| 128.0 \| 43264 \| 2.4098 \| \| 2.0701 \| 129.0 \| 43602 \| 2.4060 \| \| 2.0701 \| 130.0 \| 43940 \| 2.4032 \| \| 2.0358 \| 131.0 \| 44278 \| 2.4010 \| \| 2.0487 \| 132.0 \| 44616 \| 2.3981 \| \| 2.0487 \| 133.0 \| 44954 \| 2.3956 \| \| 2.0402 \| 134.0 \| 45292 \| 2.3927 \| \| 2.0425 \| 135.0 \| 45630 \| 2.3895 \| \| 2.0425 \| 136.0 \| 45968 \| 2.3873 \| \| 2.0379 \| 137.0 \| 46306 \| 2.3844 \| \| 2.0297 \| 138.0 \| 46644 \| 2.3818 \| \| 2.0297 \| 139.0 \| 46982 \| 2.3785 \| \| 2.046 \| 140.0 \| 47320 \| 2.3766 \| \| 2.0066 \| 141.0 \| 47658 \| 2.3739 \| \| 2.0066 \| 142.0 \| 47996 \| 2.3712 \| \| 2.0186 \| 143.0 \| 48334 \| 2.3696 \| \| 2.0474 \| 144.0 \| 48672 \| 2.3669 \| \| 1.9858 \| 145.0 \| 49010 \| 2.3652 \| \| 1.9858 \| 146.0 \| 49348 \| 2.3631 \| \| 2.0216 \| 147.0 \| 49686 \| 2.3609 \| \| 1.9961 \| 148.0 \| 50024 \| 2.3588 \| \| 1.9961 \| 149.0 \| 50362 \| 2.3573 \| \| 1.9873 \| 150.0 \| 50700 \| 2.3554 \| \| 2.0043 \| 151.0 \| 51038 \| 2.3530 \| \| 2.0043 \| 152.0 \| 51376 \| 2.3508 \| \| 2.0045 \| 153.0 \| 51714 \| 2.3490 \| \| 1.9951 \| 154.0 \| 52052 \| 2.3475 \| \| 1.9951 \| 155.0 \| 52390 \| 2.3458 \| \| 2.02 \| 156.0 \| 52728 \| 2.3448 \| \| 1.9924 \| 157.0 \| 53066 \| 2.3429 \| \| 1.9924 \| 158.0 \| 53404 \| 2.3410 \| \| 1.9757 \| 159.0 \| 53742 \| 2.3398 \| \| 1.9882 \| 160.0 \| 54080 \| 2.3383 \| \| 1.9882 \| 161.0 \| 54418 \| 2.3368 \| \| 2.0006 \| 162.0 \| 54756 \| 2.3355 \| \| 1.9984 \| 163.0 \| 55094 \| 2.3341 \| \| 1.9984 \| 164.0 \| 55432 \| 2.3331 \| \| 1.9823 \| 165.0 \| 55770 \| 2.3318 \| \| 1.9548 \| 166.0 \| 56108 \| 2.3309 \| \| 1.9548 \| 167.0 \| 56446 \| 2.3297 \| \| 1.9812 \| 168.0 \| 56784 \| 2.3288 \| \| 1.9793 \| 169.0 \| 57122 \| 2.3276 \| \| 1.9793 \| 170.0 \| 57460 \| 2.3264 \| \| 2.0022 \| 171.0 \| 57798 \| 2.3255 \| \| 1.9593 \| 172.0 \| 58136 \| 2.3248 \| \| 1.9593 \| 173.0 \| 58474 \| 2.3236 \| \| 1.9756 \| 174.0 \| 58812 \| 2.3228 \| \| 1.9835 \| 175.0 \| 59150 \| 2.3221 \| \| 1.9835 \| 176.0 \| 59488 \| 2.3214 \| \| 1.9655 \| 177.0 \| 59826 \| 2.3208 \| \| 1.9712 \| 178.0 \| 60164 \| 2.3202 \| \| 1.9658 \| 179.0 \| 60502 \| 2.3195 \| \| 1.9658 \| 180.0 \| 60840 \| 2.3188 \| \| 1.9501 \| 181.0 \| 61178 \| 2.3185 \| \| 1.992 \| 182.0 \| 61516 \| 2.3180 \| \| 1.992 \| 183.0 \| 61854 \| 2.3176 \| \| 1.9784 \| 184.0 \| 62192 \| 2.3172 \| \| 1.968 \| 185.0 \| 62530 \| 2.3169 \| \| 1.968 \| 186.0 \| 62868 \| 2.3165 \| \| 1.9746 \| 187.0 \| 63206 \| 2.3161 \| \| 1.9615 \| 188.0 \| 63544 \| 2.3159 \| \| 1.9615 \| 189.0 \| 63882 \| 2.3157 \| \| 1.9405 \| 190.0 \| 64220 \| 2.3155 \| \| 1.9869 \| 191.0 \| 64558 \| 2.3153 \| \| 1.9869 \| 192.0 \| 64896 \| 2.3152 \| \| 1.9614 \| 193.0 \| 65234 \| 2.3150 \| \| 1.9641 \| 194.0 \| 65572 \| 2.3149 \| \| 1.9641 \| 195.0 \| 65910 \| 2.3148 \| \| 1.9813 \| 196.0 \| 66248 \| 2.3148 \| \| 1.9676 \| 197.0 \| 66586 \| 2.3147 \| \| 1.9676 \| 198.0 \| 66924 \| 2.3147 \| \| 1.9302 \| 199.0 \| 67262 \| 2.3147 \| \| 1.99 \| 200.0 \| 67600 \| 2.3147 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
thewordsmiths/Mistral-7B-v0.3_sft_LoRA_100000_dpo_LoRA	thewordsmiths	"2024-06-03T04:27:35Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:mistralai/Mistral-7B-v0.3", "base_model:finetune:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-03T04:27:17Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: mistralai/Mistral-7B-v0.3 --- # Uploaded model - Developed by: thewordsmiths - License: apache-2.0 - Finetuned from model : mistralai/Mistral-7B-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF	mradermacher	"2025-02-05T10:50:47Z"	261	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "dpo", "en", "base_model:AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo", "base_model:quantized:AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-05T09:57:28Z"	--- base_model: AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo language: - en library_name: transformers model_name: Qwen2-7B-sft-hhrlhf-gen-dpo quantized_by: mradermacher tags: - generated_from_trainer - trl - dpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/AlphaHitchhiker-7B-i1-GGUF	mradermacher	"2024-12-22T10:28:14Z"	75	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "dataset:migtissera/Hitchhiker", "base_model:macadeliccc/AlphaHitchhiker-7B", "base_model:quantized:macadeliccc/AlphaHitchhiker-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2024-12-22T09:51:04Z"	--- base_model: macadeliccc/AlphaHitchhiker-7B datasets: migtissera/Hitchhiker language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/macadeliccc/AlphaHitchhiker-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AlphaHitchhiker-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl	nota-ai	"2024-07-23T01:03:23Z"	5	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "arxiv:2402.02834", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-07-04T07:00:09Z"	# Shortened LLM Model Card Shortened LLM is a depth-pruned version of large language models for efficient text generation. - Developed by: [Nota AI](https://www.nota.ai/) - License: Non-commercial license - Repository: https://github.com/Nota-NetsPresso/shortened-llm - Paper: https://arxiv.org/abs/2402.02834 ## Compression Method * After identifying unimportant Transformer blocks, we perform one-shot pruning. * In retraining pruned models for quality recovery, we leverage continued pretraining (CPT), which involves updating all parameters, on a large-scale pretraining corpus. * Once CPT is completed, the model in this card is further finetuned with low-rank adaptation (LoRA) on an instruction tuning dataset. ## Models from Aggressive Pruning & CPT Retraining (arXiv-v2): \| Source<br>Model \| Pruning<br>Ratio \| Pruning<br>Criterion \| Retraining<br>Method \| HF Models<br>Link \| \|:---:\|:---:\|:---:\|:---:\| :---:\| \| Vicuna-v1.3-7B \| 20% \| PPL \| CPT \| [nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl) \| \| Vicuna-v1.3-7B \| 45% \| PPL \| CPT \| [nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl) \| \| Vicuna-v1.3-7B \| 60% \| PPL \| CPT \| [nota-ai/cpt_st-vicuna-v1.3-2.7b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-2.7b-ppl) \| \| Vicuna-v1.3-7B \| 80% \| PPL \| CPT \| [nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl) \| \| Vicuna-v1.3-7B \| 20% \| PPL \| CPT⇒LoRA \| [nota-ai/cpt-lora_st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-5.5b-ppl) \| \| Vicuna-v1.3-7B \| 45% \| PPL \| CPT⇒LoRA \| [nota-ai/cpt-lora_st-vicuna-v1.3-3.7b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-3.7b-ppl) \| \| Vicuna-v1.3-7B \| 60% \| PPL \| CPT⇒LoRA \| [nota-ai/cpt-lora_st-vicuna-v1.3-2.7b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-2.7b-ppl) \| \| Vicuna-v1.3-7B \| 80% \| PPL \| CPT⇒LoRA \| [nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl) \| <details> <summary> Click to see the results: </summary> - EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c) <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_results.png" width="100%"> </details> #### Experimental Setup for CPT of Pruned Vicuna-7B * Dataset: [SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B) * Training using 8 NVIDIA H100 GPUs. * 5.5B parameters: 37B training tokens (for 6 days) * 3.7B parameters: 74B tokens (for 8 days) * 2.7B parameters: 150B tokens (for 12 days) * 1.5B parameters: 271B tokens (for 11 days) * AdamW optimizer with (β1, β2)=(0.9, 0.95); a learning rate of 0.0001; a weight decay of 0.1. * Global batch size: 512 (micro-batch size of 2 × 32 gradient accumulation steps × 8 GPUs). <details> <summary> Click to see the learning curve: </summary> Zero-shot performance over the course of training for models from Vicuna-7B-v1.3 at different pruning ratios. For each model size, the CPT duration was limited to a two-week period, but additional training could further improve the quality. <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_learning-curve.png" width="100%"> </details> #### Experimental Setup for LoRA Instruction Tuning * Dataset: [Refined Alpaca](https://huggingface.co/datasets/yahma/alpaca-cleaned) * Training using 1 NVIDIA A100 GPU. * The retraining costs are low, with the entire process being executed on a single GPU. * For example, LoRA retraining of a 20%-pruned model from 7B parameters requires about 2 hours and 22GB VRAM. * A LoRA rank of 8; AdamW optimizer with a learning rate of 0.0001. * A batch size of 64 over 2 epochs. ## Models from Moderate Pruning & LoRA Retraining (arXiv-v1): \| Source<br>Model \| Pruning<br>Ratio \| Pruning<br>Criterion \| HF Models<br>Link \| \|:---:\|:---:\|:---:\|:---:\| \| LLaMA-1-7B \| 20% \| PPL \| [nota-ai/st-llama-1-5.5b-ppl](https://huggingface.co/nota-ai/st-llama-1-5.5b-ppl) \| \| LLaMA-1-7B \| 20% \| Taylor+ \| [nota-ai/st-llama-1-5.5b-taylor](https://huggingface.co/nota-ai/st-llama-1-5.5b-taylor) \| \| Vicuna-v1.3-7B \| 20% \| PPL \| [nota-ai/st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-ppl) \| \| Vicuna-v1.3-7B \| 20% \| Taylor+ \| [nota-ai/st-vicuna-v1.3-5.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-taylor) \| \| Vicuna-v1.3-13B \| 21% \| PPL \| [nota-ai/st-vicuna-v1.3-10.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-ppl) \| \| Vicuna-v1.3-13B \| 21% \| Taylor+ \| [nota-ai/st-vicuna-v1.3-10.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-taylor) \| <details> <summary> Click to see the results: </summary> - EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c) <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st-llama_zero-shot_scores.png" width="100%"> </details> ## License - All rights related to this repository and the compressed models are reserved by Nota Inc. - The intended use is strictly limited to research and non-commercial projects. ## Acknowledgments - [Microsoft for Startups Founders Hub](https://www.microsoft.com/en-us/startups) and [Gwangju AICA](http://www.aica-gj.kr/main.php) for generously providing GPU resources. - [LLM-Pruner](https://github.com/horseee/LLM-Pruner), which utilizes [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness), [PEFT](https://github.com/huggingface/peft), and [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). Thanks for the pioneering work on structured pruning of LLMs! - [LLaMA](https://github.com/facebookresearch/llama), [Vicuna](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md), [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), and [Alpaca-Cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). Thanks for the open-source LLMs and data! ## Citation ```bibtex @article{kim2024shortened, title={Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods}, author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu}, journal={arXiv preprint arXiv:2402.02834}, year={2024}, url={https://arxiv.org/abs/2402.02834} } ``` ```bibtex @article{kim2024mefomo, title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models}, author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu}, journal={ICLR Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)}, year={2024}, url={https://openreview.net/forum?id=18VGxuOdpu} } ```
migtissera/Tess-XS-v1.2	migtissera	"2023-11-25T18:15:20Z"	1,474	2	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-11-23T21:49:34Z"	--- license: apache-2.0 --- # Note: This version is experimental and have been depracated. Please use the stable release Tess-XS-v1.3-yarn-128K: https://huggingface.co/migtissera/Tess-XS-v1-3-yarn-128K # Tess ![Tess](https://huggingface.co/migtissera/Tess-M-v1.0/resolve/main/Tess.png) Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-XS-v1.1 was trained on the Mistral-7B base. # Prompt Format: ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ```
asenella/mhd128_JNFDcca_beta_25_scale_True_seed_0	asenella	"2023-08-17T00:23:53Z"	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	"2023-08-17T00:23:33Z"	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1	cleanrl	"2023-02-05T18:24:43Z"	0	0	cleanrl	[ "cleanrl", "tensorboard", "Kangaroo-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-02-05T18:24:42Z"	--- tags: - Kangaroo-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Kangaroo-v5 type: Kangaroo-v5 metrics: - type: mean_reward value: 1800.00 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) PPO Agent Playing Kangaroo-v5 This is a trained model of a PPO agent playing Kangaroo-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sebulba_ppo_envpool.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Kangaroo-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Kangaroo-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 16, 'async_update': 4, 'batch_size': 8192, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Kangaroo-v5', 'exp_name': 'sebulba_ppo_envpool', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 2048, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 64, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6103, 'params_queue_timeout': 0.02, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
mradermacher/L3-78b-Large-v1-i1-GGUF	mradermacher	"2025-03-27T08:54:21Z"	236	0	transformers	[ "transformers", "gguf", "en", "base_model:FINGU-AI/L3-78b-Large-v1", "base_model:quantized:FINGU-AI/L3-78b-Large-v1", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-03-25T22:37:06Z"	--- base_model: FINGU-AI/L3-78b-Large-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/FINGU-AI/L3-78b-Large-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-78b-Large-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 24.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 25.5 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 27.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 29.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 31.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 31.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 31.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 34.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 35.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 36.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 37.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 40.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 42.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 42.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 44.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 47.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_1.gguf) \| i1-Q4_1 \| 49.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 50.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 55.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 58.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 69.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
rhyliieee/LLAMA3-MED-v2.2	rhyliieee	"2024-11-02T21:18:37Z"	75	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dataset:rhyliieee/notes-completion-set", "base_model:aaditya/Llama3-OpenBioLLM-8B", "base_model:quantized:aaditya/Llama3-OpenBioLLM-8B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-11-02T20:55:27Z"	--- license: mit datasets: - rhyliieee/notes-completion-set base_model: - aaditya/Llama3-OpenBioLLM-8B pipeline_tag: text-generation library_name: transformers --- Finetuned a pretrained Model with Lora, resize the base model's embeddings, then load Peft Model with the resized base model. """ # add special tokens to the tokenizer and base model before merging peft with base open_tokenizer.add_special_tokens({ "additional_special_tokens": ["<\|start_header_id\|>", "<\|end_header_id\|>", "<\|eot_id\|>"] }) base_model.resize_token_embeddings(len(open_tokenizer)) # reload the peft model with resized token embedding of base model peft_model = PeftModel.from_pretrained(base_model, "rhyliieee/LLaMA3-8Bit-Lora-Med-v2",) # perform merging merged_peft_base_with_special_tokens = peft_model.merge_and_unload() """
Jonjew/PastelGothStyle	Jonjew	"2025-02-09T07:05:05Z"	7	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	"2025-02-09T07:05:00Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- On a distant, surreal planet, an alien couture collection is displayed in an otherworldly marketplace. The setting features glowing, crystalline structures and bioluminescent flora, creating a fantastical, ethereal environment. Models showcase intricate, alien garments crafted from translucent, luminous materials with delicate, pulsating patterns. The attire blends organic, flowing shapes with futuristic, metallic accents, reflecting a harmonious fusion of natural and advanced aesthetics. The vibrant colors and unique silhouettes highlight the avant-garde nature of extraterrestrial fashion in hud_pstl_gth_styl, output: url: images/MarkuryFLUX_01099_.png - text: >- On a distant, surreal planet, an alien couture collection is displayed in an otherworldly marketplace. The setting features glowing, crystalline structures and bioluminescent flora, creating a fantastical, ethereal environment. Models showcase intricate, alien garments crafted from translucent, luminous materials with delicate, pulsating patterns. The attire blends organic, flowing shapes with futuristic, metallic accents, reflecting a harmonious fusion of natural and advanced aesthetics. The vibrant colors and unique silhouettes highlight the avant-garde nature of extraterrestrial fashion in hud_pstl_gth_styl, output: url: images/MarkuryFLUX_01097_.png - text: >- Energetic Harajuku shopping street filled with cheerful, bubbly fashionistas showcasing their unique, eclectic styles. The street is lined with quirky, pastel-colored shops and stalls offering an array of colorful, whimsical clothing and accessories. The shoppers are dressed in charming, layered outfits with playful patterns, oversized bows, and bright, cheerful colors. The scene includes colorful murals, twinkling fairy lights, and festive decorations, creating a lively, vibrant atmosphere of playful fashion and youthful exuberancein hud_pstl_gth_styl, pastel, goth, psychedelic output: url: images/MarkuryFLUX_01113_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: unknown --- # Pastel Goth Style FLUX <Gallery /> ## Model description FROM: https://civitai.com/models/682692/pastel-goth-style-flux Triggers: hud_pstl_gth_styl, pastel, gothic, dark, fantasy ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/PastelGothStyle/tree/main) them in the Files & versions tab.
AdapterHub/bert-base-multilingual-cased-sw-wiki_pfeiffer	AdapterHub	"2024-05-05T21:05:01Z"	3	0	adapter-transformers	[ "adapter-transformers", "bert", "adapterhub:sw/wiki", "sw", "arxiv:2005.00052", "license:apache-2.0", "region:us" ]	null	"2024-05-05T21:04:58Z"	--- tags: - bert - adapter-transformers - adapterhub:sw/wiki language: - sw license: "apache-2.0" --- # Adapter `bert-base-multilingual-cased-sw-wiki_pfeiffer` for bert-base-multilingual-cased Pfeiffer Adapter trained with Masked Language Modelling on Swahili Wikipedia Articles for 100k steps and a batch size of 64. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-multilingual-cased") adapter_name = model.load_adapter("AdapterHub/bert-base-multilingual-cased-sw-wiki_pfeiffer") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: pfeiffer - Prediction head: None - Dataset: [sw/wiki](https://adapterhub.ml/explore/sw/wiki/) ## Author Information - Author name(s): Jonas Pfeiffer - Author email: [email protected] - Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/jopfeiff), [Twitter](https://twitter.com/@PfeiffJo) ## Versions - `nd` (main) - `wd` ## Citation ```bibtex @article{pfeiffer20madx, title={{MAD-X}: An {A}dapter-based {F}ramework for {M}ulti-task {C}ross-lingual {T}ransfer}, author={Pfeiffer, Jonas and Vuli\'{c}, Ivan and Gurevych, Iryna and Ruder, Sebastian}, journal={arXiv preprint}, year={2020}, url={https://arxiv.org/pdf/2005.00052.pdf}, } ``` This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-multilingual-cased-sw-wiki_pfeiffer.yaml.
Bronsn/llama3.2-1B-translatev1	Bronsn	"2024-09-29T21:30:18Z"	57	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "trl", "sft", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-09-29T18:28:57Z"	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
memeviss/IQ_1	memeviss	"2025-04-17T18:31:31Z"	0	0	null	[ "safetensors", "region:us" ]	null	"2025-04-17T18:26:30Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Narkantak/mistral-7b-Intent-Classifier-Ashu	Narkantak	"2024-04-02T09:11:49Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-02T09:11:35Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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marisabatalla/autotrain-4lx4k-qa52n	marisabatalla	"2024-02-29T18:27:03Z"	192	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:autotrain-4lx4k-qa52n/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-02-29T18:26:46Z"	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - autotrain-4lx4k-qa52n/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 0.09857235848903656 f1: 1.0 precision: 1.0 recall: 1.0 auc: 1.0 accuracy: 1.0
kaierlong/gemma-chinese	kaierlong	"2024-04-15T07:45:17Z"	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	"2024-04-15T03:47:59Z"	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: gemma-chinese results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-chinese This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
hlumin/speecht5_finetuned_voxpopuli_nl	hlumin	"2023-08-30T23:31:37Z"	82	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "lt", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	"2023-08-30T23:25:39Z"	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] language: - lt pipeline_tag: text-to-speech --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.6484 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.52 \| 5 \| 0.6706 \| \| No log \| 1.04 \| 10 \| 0.6484 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
CocoRoF/KoModernBERT-large-mlm-v17	CocoRoF	"2025-03-24T04:45:39Z"	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "generated_from_trainer", "base_model:CocoRoF/KoModernBERT-large-mlm-v16", "base_model:finetune:CocoRoF/KoModernBERT-large-mlm-v16", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2025-03-23T10:01:20Z"	--- library_name: transformers license: apache-2.0 base_model: CocoRoF/KoModernBERT-large-mlm-v16 tags: - generated_from_trainer model-index: - name: KoModernBERT-large-mlm-v17 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KoModernBERT-large-mlm-v17 This model is a fine-tuned version of [CocoRoF/KoModernBERT-large-mlm-v16](https://huggingface.co/CocoRoF/KoModernBERT-large-mlm-v16) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 32 - total_train_batch_size: 1024 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 90.5064 \| 0.1156 \| 500 \| nan \| \| 86.5566 \| 0.2312 \| 1000 \| nan \| \| 83.8022 \| 0.3468 \| 1500 \| nan \| \| 83.7241 \| 0.4624 \| 2000 \| nan \| \| 82.7234 \| 0.5779 \| 2500 \| nan \| \| 79.0157 \| 0.6935 \| 3000 \| nan \| \| 77.4688 \| 0.8091 \| 3500 \| nan \| \| 74.9247 \| 0.9247 \| 4000 \| nan \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.1
sd-concepts-library/singsing-doll	sd-concepts-library	"2022-09-19T16:14:12Z"	0	0	null	[ "license:mit", "region:us" ]	null	"2022-09-19T16:14:06Z"	--- license: mit --- ### Singsing doll on Stable Diffusion This is the `<singsing>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<singsing> 0](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/3.jpeg) ![<singsing> 1](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/0.jpeg) ![<singsing> 2](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/1.jpeg) ![<singsing> 3](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/2.jpeg)
memorysaver/q-FrozenLake-v1-4x4-noSlippery	memorysaver	"2022-05-29T11:08:08Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2022-05-29T11:07:58Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
devmoon732/mayen	devmoon732	"2025-04-06T10:03:28Z"	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-04-06T09:32:47Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: mayen --- # Mayen <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mayen` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mayen", "lora_weights": "https://huggingface.co/devmoon732/mayen/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('devmoon732/mayen', weight_name='lora.safetensors') image = pipeline('mayen').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/devmoon732/mayen/discussions) to add images that show off what you’ve made with this LoRA.
Grayx/fiufiu_476	Grayx	"2025-01-16T22:21:18Z"	44	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-16T22:18:10Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_monkeypox_top3_2_2e-5_16_undersampling_0.4	isspek	"2025-03-23T13:27:43Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-03-23T13:27:28Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Ekhlass/phi2-flutter-questions	Ekhlass	"2024-04-08T08:38:47Z"	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-08T08:36:06Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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e1113633/roomifai_sd15_ft_diningroom	e1113633	"2023-11-02T10:03:30Z"	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-11-02T09:36:37Z"	# Roomifai Dining Room Stable Diffusion v1-5 Fine Tuning Model Card This model is a fine tuned model from stable diffusion 1.5 for our school project, it is capable of generating dining room design given specific dining room promopts. # Uses You have to install all the dependencies, python 3.10.x There is no GUI for testing the mode. the test.py script is used for testing the model, the command is as such: > python test.py <model> <output folder> <prompts file> <prefix for output file> for e.g > python test.py "./unet/diffusion_pytorch_model.safetensors" "output" "dingingroom_prompt.txt" "test1"
The-Masters-Golf-Reddit/LIVE	The-Masters-Golf-Reddit	"2025-04-13T19:02:49Z"	0	0	null	[ "region:us" ]	null	"2025-04-13T18:56:19Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
xshubhamx/InLegal-legal-merge-ties-d-0-InLegal-w-1	xshubhamx	"2024-04-20T17:16:32Z"	107	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "merge", "mergekit", "lazymergekit", "xshubhamx/InLegalBERT", "xshubhamx/legal-bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-04-20T17:14:08Z"	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - xshubhamx/InLegalBERT - xshubhamx/legal-bert-base-uncased --- ## Metrics - loss: 0.9470 - accuracy: 0.8366 - precision: 0.8360 - recall: 0.8366 - precision_macro: 0.8141 - recall_macro: 0.7899 - macro_fpr: 0.0143 - weighted_fpr: 0.0138 - weighted_specificity: 0.9781 - macro_specificity: 0.9876 - weighted_sensitivity: 0.8366 - macro_sensitivity: 0.7899 - f1_micro: 0.8366 - f1_macro: 0.7978 - f1_weighted: 0.8350 - runtime: 21.6449 - samples_per_second: 59.6450 - steps_per_second: 7.4840 # InLegal-legal-merge-ties-d-0-InLegal-w-1 InLegal-legal-merge-ties-d-0-InLegal-w-1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [xshubhamx/InLegalBERT](https://huggingface.co/xshubhamx/InLegalBERT) * [xshubhamx/legal-bert-base-uncased](https://huggingface.co/xshubhamx/legal-bert-base-uncased) ## 🧩 Configuration ```yaml models: - model: xshubhamx/InLegalBERT parameters: density: 0.53 weight: 0 - model: xshubhamx/legal-bert-base-uncased parameters: density: 0.53 weight: 1 merge_method: ties base_model: xshubhamx/InLegalBERT parameters: normalize: false int8_mask: true dtype: float16 ```
weijie210/zephyr-7b-UFB-0	weijie210	"2024-02-07T03:49:39Z"	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-07T01:25:02Z"	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - trl - dpo - generated_from_trainer model-index: - name: zephyr-7b-UFB-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-UFB-0 This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1492 - Rewards/chosen: -1.5452 - Rewards/rejected: -7.2115 - Rewards/accuracies: 0.8359 - Rewards/margins: 5.6663 - Logps/rejected: -171.0846 - Logps/chosen: -143.6666 - Logits/rejected: -2.3237 - Logits/chosen: -2.3692 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0
AdapterHub/xmod-base-be_BY	AdapterHub	"2023-08-31T12:59:06Z"	4	0	adapter-transformers	[ "adapter-transformers", "xmod", "adapterhub:be/cc100", "be", "license:mit", "region:us" ]	null	"2023-08-31T12:47:02Z"	--- tags: - adapter-transformers - xmod - adapterhub:be/cc100 language: - be license: "mit" --- # Adapter `AdapterHub/xmod-base-be_BY` for AdapterHub/xmod-base An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [be/cc100](https://adapterhub.ml/explore/be/cc100/) dataset. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base") adapter_name = model.load_adapter("AdapterHub/xmod-base-be_BY", source="hf", set_active=True) ``` ## Architecture & Training This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library. For more information on architecture and training, please refer to the original model card. ## Evaluation results <!-- Add some description here --> ## Citation [Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) ``` @inproceedings{pfeiffer-etal-2022-lifting, title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers", author = "Pfeiffer, Jonas and Goyal, Naman and Lin, Xi and Li, Xian and Cross, James and Riedel, Sebastian and Artetxe, Mikel", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.255", doi = "10.18653/v1/2022.naacl-main.255", pages = "3479--3495" } ```
dar5654/segformer-b0-scene-parse-150-MASKED	dar5654	"2023-04-29T20:03:23Z"	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	null	"2023-04-29T15:34:13Z"	--- license: other tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150-MASKED results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-scene-parse-150-MASKED This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1526 - Mean Iou: 0.0217 - Mean Accuracy: 0.0580 - Overall Accuracy: 0.2746 - Per Category Iou: [0.2638779780993535, 0.24032657224553952, 0.28498201974847515, 0.1075812162299665, 0.14745268628426467, 0.048342869965219346, 0.0, 0.007290688724806103, 0.04780558672261605, 0.06559620777139805, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] - Per Category Accuracy: [0.5551073389128427, 0.47540841261768607, 0.4280130098767642, 0.6449145007547091, 0.4263212952616438, 0.051559171951657295, 0.0, 0.008099600657740192, 0.06573971674217831, 0.0695452132365953, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| 4.7792 \| 1.0 \| 20 \| 4.7294 \| 0.0082 \| 0.0454 \| 0.1893 \| [0.2263585397369742, 0.13770136142176356, 0.08295638586455376, 0.08510788870213735, 0.12573291455024074, 0.02435003944847278, 0.0, 0.004065480375718896, 0.0017733053903393038, 0.09547671544063606, 0.0, 0.0, 0.00046794942973620344, 0.0, 0.0, 0.0, 0.0, 0.0003653809493550232, 0.0, 0.0, nan, 0.0, 0.0, 0.008303859757035214, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0] \| [0.4716825785763388, 0.2136232639104242, 0.09227762360874885, 0.6465273039306908, 0.5643826822947624, 0.024817518248175182, 0.0, 0.0042377260981912145, 0.0018640077057543434, 0.10115023889577066, 0.0, 0.0, 0.0004903142166191589, nan, 0.0, 0.0, nan, 0.001218026796589525, 0.0, 0.0, nan, nan, 0.0, 0.010582425335110135, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 4.6816 \| 2.0 \| 40 \| 4.3777 \| 0.0172 \| 0.0508 \| 0.2436 \| [0.2348784161662965, 0.1780159659740713, 0.1725553209314372, 0.11519214696920146, 0.1519642591474354, 0.05501165920088421, 0.008008356545961003, 0.003268125562869637, 0.06320147075839194, 0.033278833708018256, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.49203944919252063, 0.5863196761642498, 0.24353236057649272, 0.45883216508487895, 0.4128408739687597, 0.05860476247457221, 0.010855884203901826, 0.0033074935400516795, 0.08768863044486462, 0.03457795080516723, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 4.3571 \| 3.0 \| 60 \| 4.2442 \| 0.0166 \| 0.0524 \| 0.2571 \| [0.25585106151712383, 0.22004710007836228, 0.22139639459642338, 0.10209920082512318, 0.1575995748489595, 0.017118189937481394, 0.0, 0.007236489870641267, 0.03938333712881877, 0.008957958671236131, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.5795454321624768, 0.44004779802440347, 0.3099841852415481, 0.6467961044600211, 0.40188060198283443, 0.017613976307287303, 0.0, 0.00787408973455485, 0.05089900467339731, 0.009343479030260131, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 3.9658 \| 4.0 \| 80 \| 4.1981 \| 0.0207 \| 0.0555 \| 0.2731 \| [0.26863872743436906, 0.26573623577278954, 0.2321627542307547, 0.10446031518997217, 0.16009038296656186, 0.046391399460182774, 0.0, 0.004261499526016889, 0.04043589899112432, 0.01742889012827663, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.6207791747342379, 0.47583015989183425, 0.2976531495240653, 0.6644438930587433, 0.4261667578041416, 0.04862031829603925, 0.0, 0.0046041813483673946, 0.05351218294031608, 0.01769598301185631, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 4.0603 \| 5.0 \| 100 \| 4.1526 \| 0.0217 \| 0.0580 \| 0.2746 \| [0.2638779780993535, 0.24032657224553952, 0.28498201974847515, 0.1075812162299665, 0.14745268628426467, 0.048342869965219346, 0.0, 0.007290688724806103, 0.04780558672261605, 0.06559620777139805, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.5551073389128427, 0.47540841261768607, 0.4280130098767642, 0.6449145007547091, 0.4263212952616438, 0.051559171951657295, 0.0, 0.008099600657740192, 0.06573971674217831, 0.0695452132365953, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
ctoxz/GPU	ctoxz	"2025-04-18T01:02:17Z"	0	0	null	[ "region:us" ]	null	"2025-04-18T01:02:07Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
albertus-sussex/veriscrape-fixed-simcse-nbaplayer-reference_1_to_verify_9-fold-8	albertus-sussex	"2025-04-04T14:29:49Z"	0	0	transformers	[ "transformers", "safetensors", "roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	"2025-04-04T14:29:21Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Jadsalameh31/finetuning-sentiment-model-3000-samples	Jadsalameh31	"2023-03-06T18:49:57Z"	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-03-06T18:46:13Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7012 - eval_accuracy: 0.4933 - eval_f1: 0.6607 - eval_runtime: 7.7289 - eval_samples_per_second: 38.816 - eval_steps_per_second: 2.458 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
klemiec/unit0	klemiec	"2023-03-02T19:48:34Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-03-02T19:43:25Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.18 +/- 18.94 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
second-state/phi-4-GGUF	second-state	"2025-01-11T13:58:41Z"	669	0	transformers	[ "transformers", "gguf", "phi3", "text-generation", "custom_code", "en", "base_model:microsoft/phi-4", "base_model:quantized:microsoft/phi-4", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-01-09T02:17:27Z"	--- base_model: microsoft/phi-4 license: mit license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers model_creator: Microsoft model_name: phi-4 quantized_by: Second State Inc. --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Phi-4-GGUF ## Original Model [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) ## Run with LlamaEdge - LlamaEdge version: [v0.16.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.16.0) and above - Prompt template - Prompt type: `phi-4-chat` - Prompt string ```text <\|im_start\|>system<\|im_sep\|> {system_message}<\|im_end\|> <\|im_start\|>user<\|im_sep\|> {user_message}<\|im_end\|> <\|im_start\|>assistant<\|im_sep\|> ``` - Context size: `16000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:phi-4-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template phi-4-chat \ --ctx-size 16000 \ --model-name phi-3-mini ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:phi-4-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template phi-4-chat \ --ctx-size 16000 ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [phi-4-Q2_K.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q2_K.gguf) \| Q2_K \| 2 \| 5.55 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [phi-4-Q3_K_L.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 7.93 GB\| small, substantial quality loss \| \| [phi-4-Q3_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 7.36 GB\| very small, high quality loss \| \| [phi-4-Q3_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 6.50 GB\| very small, high quality loss \| \| [phi-4-Q4_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_0.gguf) \| Q4_0 \| 4 \| 8.38 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [phi-4-Q4_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 9.05 GB\| medium, balanced quality - recommended \| \| [phi-4-Q4_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 8.44 GB\| small, greater quality loss \| \| [phi-4-Q5_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_0.gguf) \| Q5_0 \| 5 \| 10.2 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [phi-4-Q5_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 10.6 GB\| large, very low quality loss - recommended \| \| [phi-4-Q5_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 10.2 GB\| large, low quality loss - recommended \| \| [phi-4-Q6_K.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q6_K.gguf) \| Q6_K \| 6 \| 12.0 GB\| very large, extremely low quality loss \| \| [phi-4-Q8_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q8_0.gguf) \| Q8_0 \| 8 \| 15.6 GB\| very large, extremely low quality loss - not recommended \| \| [phi-4-f16.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-f16.gguf) \| f16 \| 16 \| 29.3 GB\| \| Quantized with llama.cpp b4450.
BEASTBOYJAY/my-fine-tuned-summarizer	BEASTBOYJAY	"2024-11-16T05:53:44Z"	103	0	transformers	[ "transformers", "safetensors", "encoder-decoder", "text2text-generation", "en", "dataset:ccdv/cnn_dailymail", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-11-16T05:36:03Z"	--- library_name: transformers datasets: - ccdv/cnn_dailymail language: - en base_model: - google-bert/bert-base-uncased --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model is used for making or generating summary of the provided paragraph. - Developed by: BEASTBOYJAY - Model type: Transformer(encoder) - Language(s) (NLP): English - Finetuned from model: Bert-base-uncased ## Uses - For the summarization purpose only ## Bias, Risks, and Limitations This model is fine-tuned on very small dataset can need more fine-tuning for better results.(Fine-tuned this model only for eductional purposes) ## How to Get Started with the Model Use the code below to get started with the model. ``` from transformers import EncoderDecoderModel, BertTokenizer class TextSummarizer: def __init__(self, model_path, tokenizer_name="bert-base-uncased"): self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name) self.model = EncoderDecoderModel.from_pretrained(model_path) def summarize(self, text, max_input_length=512): inputs = self.tokenizer( text, return_tensors="pt", truncation=True, padding="max_length", max_length=max_input_length, ) summary_ids = self.model.generate( inputs["input_ids"], attention_mask=inputs["attention_mask"], decoder_start_token_id=self.tokenizer.cls_token_id, max_length=128, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=1, early_stopping=True, ) summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary if __name__ == "__main__": summarizer = TextSummarizer(model_path="BEASTBOYJAY/my-fine-tuned-summarizer") test_article = "Your article or paragraph" summary = summarizer.summarize(test_article) print("Generated Summary:", summary) ```
Ppoyaa/L3-Inca-8B-v0.8	Ppoyaa	"2024-06-25T03:16:46Z"	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Nitral-AI/Hathor_Stable-v0.2-L3-8B", "base_model:merge:Nitral-AI/Hathor_Stable-v0.2-L3-8B", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:grimjim/Llama-3-Luminurse-v0.2-OAS-8B", "base_model:merge:grimjim/Llama-3-Luminurse-v0.2-OAS-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-22T15:15:22Z"	--- base_model: - NurtureAI/Meta-Llama-3-8B-Instruct-32k - Nitral-AI/Hathor-L3-8B-v.02 - grimjim/Llama-3-Luminurse-v0.2-OAS-8B - Sao10K/L3-8B-Stheno-v3.2 library_name: transformers tags: - mergekit - merge --- ![1718719796324.png](https://cdn-uploads.huggingface.co/production/uploads/65f158693196560d34495d54/6SuBtoTMP5Svgl0fmT9vt.png) *** ### L3-Inca-8B-v0.8 [L3-Inca-8B-v0.8](https://huggingface.co/Ppoyaa/L3-Inca-8B-v0.8) is a merge of the following models: * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [Nitral-AI/Hathor-L3-8B-v.02](https://huggingface.co/Nitral-AI/Hathor-L3-8B-v.02) * [grimjim/Llama-3-Luminurse-v0.2-OAS-8B](https://huggingface.co/grimjim/Llama-3-Luminurse-v0.2-OAS-8B) using [NurtureAI/Meta-Llama-3-8B-Instruct-32k](https://huggingface.co/NurtureAI/Meta-Llama-3-8B-Instruct-32k) as the base. >[!IMPORTANT] > UPDATE: > Changed the merging method from model_stock to ties and made Stheno have the most weight and density. * ### Quantized Models by [mradermacher](https://huggingface.co/mradermacher) • Static [L3-Inca-8B-v0.8-GGUF](https://huggingface.co/mradermacher/L3-Inca-8B-v0.8-GGUF) • Imatrix [L3-Inca-8B-v0.8-i1-GGUF](https://huggingface.co/mradermacher/L3-Inca-8B-v0.8-i1-GGUF) * ### Configuration ```yaml models: - model: Sao10K/L3-8B-Stheno-v3.2 parameters: density: 0.85 weight: 0.5 - model: Nitral-AI/Hathor-L3-8B-v.02 parameters: density: 0.75 weight: 0.3 - model: grimjim/Llama-3-Luminurse-v0.2-OAS-8B parameters: density: 0.75 weight: 0.2 merge_method: ties base_model: NurtureAI/Meta-Llama-3-8B-Instruct-32k parameters: normalize: false int8_mask: true dtype: bfloat16 ```
abhijeet007/t5-Large_FineTunned	abhijeet007	"2024-03-27T07:57:09Z"	63	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-03-27T07:55:48Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
apparaomulpuri/alpaca-custom-model	apparaomulpuri	"2023-06-26T13:54:37Z"	5	0	peft	[ "peft", "region:us" ]	null	"2023-06-26T10:53:07Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
isspek/roberta-base_covid_chatgpt_3_2e-5_16_undersampling_0.2	isspek	"2025-01-01T16:20:24Z"	200	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-12-28T23:28:28Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jysssacc/bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01	jysssacc	"2024-01-13T22:30:46Z"	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:bigscience/bloomz-1b1", "base_model:adapter:bigscience/bloomz-1b1", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	"2024-01-13T19:41:57Z"	--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: bigscience/bloomz-1b1 model-index: - name: bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01 This model is a fine-tuned version of [bigscience/bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.0291 \| 1.0 \| 157 \| 4.5495 \| \| 4.582 \| 2.0 \| 314 \| 4.0083 \| \| 4.1412 \| 3.0 \| 471 \| 3.5810 \| \| 3.6919 \| 4.0 \| 628 \| 3.3076 \| \| 3.5125 \| 5.0 \| 785 \| 3.2440 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0
yesj1234/xlsr_cycle0_ko	yesj1234	"2023-09-07T04:20:40Z"	77	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "./sample_speech.py", "generated_from_trainer", "dataset:sample_speech", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2023-09-07T04:16:24Z"	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - automatic-speech-recognition - ./sample_speech.py - generated_from_trainer datasets: - sample_speech model-index: - name: ko-xlsr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ko-xlsr This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./SAMPLE_SPEECH.PY - NA dataset. It achieves the following results on the evaluation set: - Loss: 1.5649 - Cer: 0.3569 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.5849 \| 22.22 \| 1000 \| 2.5846 \| 0.5985 \| \| 0.7224 \| 44.44 \| 2000 \| 1.5880 \| 0.3664 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
mradermacher/Pathfinder-RP-12B-RU-i1-GGUF	mradermacher	"2025-01-30T09:16:40Z"	874	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Aleteian/Pathfinder-RP-12B-RU", "base_model:quantized:Aleteian/Pathfinder-RP-12B-RU", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-01-30T04:31:32Z"	--- base_model: Aleteian/Pathfinder-RP-12B-RU language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Aleteian/Pathfinder-RP-12B-RU <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
MinaMila/gemma2_GermanCredit_cfda_14ep_42	MinaMila	"2025-03-19T01:12:22Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:unsloth/gemma-2-9b", "base_model:finetune:unsloth/gemma-2-9b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-03-19T01:12:12Z"	--- base_model: unsloth/gemma-2-9b tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
schoonhovenra/20240502	schoonhovenra	"2024-05-06T00:21:15Z"	191	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	"2024-05-06T00:21:06Z"	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: '20240502' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20240502 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1
mradermacher/ThoughtStream-4B-v0.2-GGUF	mradermacher	"2025-03-31T14:32:33Z"	0	0	transformers	[ "transformers", "gguf", "en", "dataset:SkunkworksAI/reasoning-0.01", "dataset:trollek/ThoughtfulAssistant-v01", "dataset:trollek/ThoughtfulAssistant-v02", "base_model:trollek/ThoughtStream-4B-v0.2", "base_model:quantized:trollek/ThoughtStream-4B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-31T14:18:32Z"	--- base_model: trollek/ThoughtStream-4B-v0.2 datasets: - SkunkworksAI/reasoning-0.01 - trollek/ThoughtfulAssistant-v01 - trollek/ThoughtfulAssistant-v02 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/trollek/ThoughtStream-4B-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q2_K.gguf) \| Q2_K \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 2.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 2.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q8_0.gguf) \| Q8_0 \| 4.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.f16.gguf) \| f16 \| 8.0 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
huggingtweets/coronavid19	huggingtweets	"2021-05-21T23:30:27Z"	5	0	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2022-03-02T23:29:05Z"	--- language: en thumbnail: https://www.huggingtweets.com/coronavid19/1608807621950/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1232060545626497024/ltc63x4__400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Coronavirus 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@coronavid19 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@coronavid19's tweets](https://twitter.com/coronavid19). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>1618</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>12</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>96</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1510</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lgjd18p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @coronavid19's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ki9s94y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ki9s94y/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/coronavid19'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
wenge-research/yayi-7b-llama2	wenge-research	"2023-09-13T02:25:50Z"	1,507	10	transformers	[ "transformers", "pytorch", "llama", "text-generation", "yayi", "zh", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-07-21T10:10:18Z"	--- language: - zh - en pipeline_tag: text-generation tags: - yayi --- # 雅意大模型 ## 介绍 [雅意大模型](https://www.wenge.com/yayi/index.html)在百万级人工构造的高质量领域数据上进行指令微调得到，训练数据覆盖媒体宣传、舆情分析、公共安全、金融风控、城市治理等五大领域，上百种自然语言指令任务。雅意大模型从预训练初始化权重到领域模型的迭代过程中，我们逐步增强了它的中文基础能力和领域分析能力，并增加了多轮对话和部分插件能力。同时，经过数百名用户内测过程中持续不断的人工反馈优化，我们进一步提升了模型性能和安全性。通过雅意大模型的开源为促进中文预训练大模型开源社区的发展，贡献自己的一份力量，通过开源，与每一位合作伙伴共建雅意大模型生态。 News: 🔥 雅意大模型已开源基于 LLaMA 2 的中文优化模型版本，探索适用于中文多领域任务的最新实践。 ## 模型地址 \| 模型名称 \| 🤗HF模型标识 \| 下载地址 \| \| --------- \| --------- \| --------- \| \| YaYi-7B \| wenge-research/yayi-7b \| [模型下载](https://huggingface.co/wenge-research/yayi-7b) \| \| YaYi-7B-Llama2 \| wenge-research/yayi-7b-llama2 \| [模型下载](https://huggingface.co/wenge-research/yayi-7b-llama2) \| \| YaYi-13B-Llama2 \| wenge-research/yayi-13b-llama2 \| [模型下载](https://huggingface.co/wenge-research/yayi-13b-llama2) \| \| YaYi-70B-Llama2 \| wenge-research/yayi-70b-llama2 \| [模型下载](https://huggingface.co/wenge-research/yayi-70b-llama2) \| 详情请参考我们的 [💻Github Repo](https://github.com/wenge-research/YaYi)。 ## 运行方式 ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig from transformers import StoppingCriteria, StoppingCriteriaList pretrained_model_name_or_path = "wenge-research/yayi-7b-llama2" tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path) model = LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=False) # Define the stopping criteria class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords_ids:list): self.keywords = keywords_ids def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, kwargs) -> bool: if input_ids[0][-1] in self.keywords: return True return False stop_words = ["<\|End\|>", "<\|YaYi\|>", "<\|Human\|>", "</s>"] stop_ids = [tokenizer.encode(w)[-1] for w in stop_words] stop_criteria = KeywordsStoppingCriteria(stop_ids) # inference prompt = "你是谁？" formatted_prompt = f"""<\|System\|>: You are a helpful, respectful and honest assistant named YaYi developed by Beijing Wenge Technology Co.,Ltd. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <\|Human\|>: {prompt} <\|YaYi\|>: """ inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) eos_token_id = tokenizer("<\|End\|>").input_ids[0] generation_config = GenerationConfig( eos_token_id=eos_token_id, pad_token_id=eos_token_id, do_sample=True, max_new_tokens=256, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=0 ) response = model.generate(inputs, generation_config=generation_config, stopping_criteria=StoppingCriteriaList([stop_criteria])) response = [response[0][len(inputs.input_ids[0]):]] response_str = tokenizer.batch_decode(response, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] print(response_str) ``` --- # YaYi ## Introduction [YaYi](https://www.wenge.com/yayi/index.html) was fine-tuned on millions of artificially constructed high-quality domain data. This training data covers five key domains: media publicity, public opinion analysis, public safety, financial risk control, and urban governance, encompassing over a hundred natural language instruction tasks. Throughout the iterative development process of the YaYi, starting from pre-training initialization weights and progressing to domain-specific model, we have steadily enhanced its foundational Chinese language capabilities and domain analysis capabilities. We've also introduced multi-turn conversation enhancements and integrated various plug-in capabilities. Furthermore, through continuous manual feedback and optimization from hundreds of users during the internal testing phase, we've meticulously refined the model's performance and security. By open-sourcing the YaYi model, we will contribute our own efforts to the development of the Chinese pre-trained large language model open-source community. Through this open-source initiative, we seek to collaborate with every partner to build the YaYi model ecosystem together. News: 🔥 YaYi has open sourced the Chinese optimization model version based on LLaMA 2 to explore the latest practices suitable for Chinese multi-domain tasks. ## Model download \| Model \| 🤗HF Model Name \| Download Links \| \| --------- \| --------- \| --------- \| \| YaYi-7B \| wenge-research/yayi-7b \| [Download](https://huggingface.co/wenge-research/yayi-7b) \| \| YaYi-7B-Llama2 \| wenge-research/yayi-7b-llama2 \| [Download](https://huggingface.co/wenge-research/yayi-7b-llama2) \| \| YaYi-13B-Llama2 \| wenge-research/yayi-13b-llama2 \| [Download](https://huggingface.co/wenge-research/yayi-13b-llama2) \| \| YaYi-70B-Llama2 \| wenge-research/yayi-70b-llama2 \| [Download](https://huggingface.co/wenge-research/yayi-70b-llama2) \| For more details, please refer to our [💻Github Repo](https://github.com/wenge-research/YaYi)。 ## Run ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig from transformers import StoppingCriteria, StoppingCriteriaList pretrained_model_name_or_path = "wenge-research/yayi-7b-llama2" tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path) model = LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=False) # Define the stopping criteria class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords_ids:list): self.keywords = keywords_ids def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, kwargs) -> bool: if input_ids[0][-1] in self.keywords: return True return False stop_words = ["<\|End\|>", "<\|YaYi\|>", "<\|Human\|>", "</s>"] stop_ids = [tokenizer.encode(w)[-1] for w in stop_words] stop_criteria = KeywordsStoppingCriteria(stop_ids) # inference prompt = "你是谁？" formatted_prompt = f"""<\|System\|>: You are a helpful, respectful and honest assistant named YaYi developed by Beijing Wenge Technology Co.,Ltd. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <\|Human\|>: {prompt} <\|YaYi\|>: """ inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) eos_token_id = tokenizer("<\|End\|>").input_ids[0] generation_config = GenerationConfig( eos_token_id=eos_token_id, pad_token_id=eos_token_id, do_sample=True, max_new_tokens=256, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=0 ) response = model.generate(inputs, generation_config=generation_config, stopping_criteria=StoppingCriteriaList([stop_criteria])) response = [response[0][len(inputs.input_ids[0]):]] response_str = tokenizer.batch_decode(response, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] print(response_str) ```
baddii/23_baddii_20_06	baddii	"2025-02-09T06:34:01Z"	14	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-02-09T06:32:04Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Beaflm/dlr_unit1_LunarLander	Beaflm	"2023-11-28T23:42:30Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-11-28T23:41:26Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.63 +/- 26.94 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
NewEden/adv-ckpt-480	NewEden	"2025-03-30T12:58:45Z"	0	0	null	[ "region:us" ]	null	"2025-03-30T12:58:45Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
huggingtweets/elsasingular-michellexotter-nyxxx696	huggingtweets	"2023-03-11T22:30:48Z"	115	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-03-11T22:29:29Z"	--- language: en thumbnail: http://www.huggingtweets.com/elsasingular-michellexotter-nyxxx696/1678573843308/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1599666828602740737/xkaxWudG_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1616081980856340480/AVkhh3Fo_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1592255638394077184/ugsW8sO4_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">❄️Elsa🏳️‍⚧️ & Nyx 🇧🇷🌸🏳️‍⚧️ & Michelle Otter 🏳️‍⚧️🦦</div> <div style="text-align: center; font-size: 14px;">@elsasingular-michellexotter-nyxxx696</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ❄️Elsa🏳️‍⚧️ & Nyx 🇧🇷🌸🏳️‍⚧️ & Michelle Otter 🏳️‍⚧️🦦. \| Data \| ❄️Elsa🏳️‍⚧️ \| Nyx 🇧🇷🌸🏳️‍⚧️ \| Michelle Otter 🏳️‍⚧️🦦 \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3226 \| 1504 \| 3194 \| \| Retweets \| 68 \| 24 \| 37 \| \| Short tweets \| 1246 \| 524 \| 702 \| \| Tweets kept \| 1912 \| 956 \| 2455 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/or027l6u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elsasingular-michellexotter-nyxxx696's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elsasingular-michellexotter-nyxxx696') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2	tyzhu	"2024-06-06T11:50:46Z"	5	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	"2024-06-04T14:01:33Z"	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - name: Accuracy type: accuracy value: 0.5108253968253968 library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: - Loss: 3.6298 - Accuracy: 0.5108 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:--------:\|:---------------:\| \| 1.7619 \| 0.9998 \| 1089 \| 0.5172 \| 2.2994 \| \| 1.648 \| 1.9995 \| 2178 \| 0.5210 \| 2.2683 \| \| 1.4941 \| 2.9993 \| 3267 \| 0.5214 \| 2.3185 \| \| 1.3627 \| 4.0 \| 4357 \| 0.5190 \| 2.4249 \| \| 1.2234 \| 4.9998 \| 5446 \| 0.5152 \| 2.5963 \| \| 1.1107 \| 5.9995 \| 6535 \| 0.5130 \| 2.7933 \| \| 0.9891 \| 6.9993 \| 7624 \| 0.5119 \| 2.9422 \| \| 0.919 \| 8.0 \| 8714 \| 0.5077 \| 3.1141 \| \| 0.833 \| 8.9998 \| 9803 \| 0.5084 \| 3.1755 \| \| 0.7635 \| 9.9977 \| 10890 \| 0.5085 \| 3.3117 \| \| 0.6899 \| 10.9998 \| 11979 \| 3.3147 \| 0.5072 \| \| 0.6427 \| 11.9995 \| 13068 \| 3.4025 \| 0.5101 \| \| 0.604 \| 12.9993 \| 14157 \| 3.3905 \| 0.5103 \| \| 0.5507 \| 14.0 \| 15247 \| 3.4740 \| 0.5088 \| \| 0.5099 \| 14.9998 \| 16336 \| 3.4772 \| 0.5085 \| \| 0.478 \| 15.9995 \| 17425 \| 3.5259 \| 0.5088 \| \| 0.4545 \| 16.9993 \| 18514 \| 3.5391 \| 0.5094 \| \| 0.427 \| 18.0 \| 19604 \| 3.5887 \| 0.5095 \| \| 0.4083 \| 18.9998 \| 20693 \| 3.5945 \| 0.5097 \| \| 0.3818 \| 19.9977 \| 21780 \| 3.6298 \| 0.5108 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
hugo-albert/roberta-large-pos	hugo-albert	"2024-10-28T10:43:27Z"	126	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/roberta-large-bne", "base_model:finetune:PlanTL-GOB-ES/roberta-large-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-10-11T15:13:40Z"	--- library_name: transformers license: apache-2.0 base_model: PlanTL-GOB-ES/roberta-large-bne tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-large-pos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-pos This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 - Precision: 0.9821 - Recall: 0.9856 - F1: 0.9838 - Accuracy: 0.9845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2213 \| 1.0 \| 603 \| 0.0835 \| 0.9761 \| 0.9807 \| 0.9784 \| 0.9800 \| \| 0.0336 \| 2.0 \| 1206 \| 0.0756 \| 0.9794 \| 0.9832 \| 0.9813 \| 0.9808 \| \| 0.0147 \| 3.0 \| 1809 \| 0.0721 \| 0.9821 \| 0.9856 \| 0.9838 \| 0.9845 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.19.1
zelk12/MT1-GB-gemma-2-9B	zelk12	"2024-10-12T13:40:14Z"	12	1	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B", "base_model:merge:zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B", "base_model:zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B", "base_model:merge:zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-10-12T13:34:00Z"	--- base_model: - zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B - zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B](https://huggingface.co/zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B) * [zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B](https://huggingface.co/zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B - model: zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B merge_method: slerp base_model: zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B dtype: bfloat16 parameters: t: 0.5 ```
stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2	stefan-it	"2023-10-17T21:30:16Z"	8	0	flair	[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "fr", "base_model:hmteams/teams-base-historic-multilingual-discriminator", "base_model:finetune:hmteams/teams-base-historic-multilingual-discriminator", "license:mit", "region:us" ]	token-classification	"2023-10-17T10:39:11Z"	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmteams/teams-base-historic-multilingual-discriminator widget: - text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi , 719 , 826 , 4496 . --- # Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) NER Dataset using hmTEAMS as backbone LM. The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics, and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/) project. The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.8432][1] \| [0.8432][2] \| [0.8596][3] \| [0.8615][4] \| [0.8525][5] \| 85.2 ± 0.78 \| \| bs4-e10-lr5e-05 \| [0.8398][6] \| [0.8564][7] \| [0.8377][8] \| [0.8579][9] \| [0.8536][10] \| 84.91 ± 0.86 \| \| bs8-e10-lr3e-05 \| [0.8396][11] \| [0.8416][12] \| [0.8511][13] \| [0.8542][14] \| [0.8454][15] \| 84.64 ± 0.55 \| \| bs8-e10-lr5e-05 \| [0.8375][16] \| [0.8428][17] \| [0.85][18] \| [0.8471][19] \| [0.8413][20] \| 84.37 ± 0.44 \| [1]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
iccv2025submission/finetuned-caption-embedding	iccv2025submission	"2025-03-01T19:39:12Z"	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10000", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	"2025-03-01T19:38:55Z"	--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:MultipleNegativesRankingLoss widget: - source_sentence: an elephant with a leaf on its back sentences: - an elephant is walking through the woods - a white truck with a white sign on it - a bathroom with a tub and sink - source_sentence: a man and woman hugging sentences: - a couple hugging in the street - a 3d model of a robot in purple and silver - a woman jumping in the air on a field - source_sentence: a silhouette of a man holding a sword in the sky sentences: - strawberry ice cream on a plate with strawberries - a banana sitting on a chair - a silhouette of a man holding a sword in the sky - source_sentence: a girl in a chinese costume holding a spear sentences: - a young girl in a traditional asian dress holding a stick - a man is chopping a piece of wood on a cutting board - a surfer riding a large wave on a surfboard - source_sentence: a bathroom with a bathtub and toilet sentences: - a bathroom with a white tub and sink - a kitchen with stainless steel appliances and wood cabinets - a woman in pink lingerie with a flower crown --- # SentenceTransformer based on sentence-transformers/paraphrase-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) <!-- at revision bef3689366be4ad4b62c8e1cec013639bea3c86a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("iccv2025submission/finetuned-caption-embedding") # Run inference sentences = [ 'a bathroom with a bathtub and toilet', 'a bathroom with a white tub and sink', 'a woman in pink lingerie with a flower crown', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 17 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.65 tokens</li><li>max: 17 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:----------------------------------------------------\|:-----------------------------------------------------------------------\| \| <code>two women cutting a cake</code> \| <code>two women cutting a cake</code> \| \| <code>a man with long white hair and a beard</code> \| <code>a man with a long white beard</code> \| \| <code>a bench is sitting on the sidewalk</code> \| <code>a bench is sitting on the sidewalk in front of a building</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 140 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 140 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:--------:\|:-----:\|:-------------:\| \| 3.1847 \| 500 \| 0.1576 \| \| 6.3694 \| 1000 \| 0.1099 \| \| 9.5541 \| 1500 \| 0.0799 \| \| 12.7389 \| 2000 \| 0.0627 \| \| 15.9236 \| 2500 \| 0.0569 \| \| 19.1083 \| 3000 \| 0.0503 \| \| 22.2930 \| 3500 \| 0.043 \| \| 25.4777 \| 4000 \| 0.041 \| \| 28.6624 \| 4500 \| 0.0357 \| \| 31.8471 \| 5000 \| 0.0338 \| \| 35.0318 \| 5500 \| 0.0326 \| \| 38.2166 \| 6000 \| 0.0299 \| \| 41.4013 \| 6500 \| 0.0319 \| \| 44.5860 \| 7000 \| 0.0286 \| \| 47.7707 \| 7500 \| 0.0266 \| \| 50.9554 \| 8000 \| 0.0269 \| \| 54.1401 \| 8500 \| 0.0253 \| \| 57.3248 \| 9000 \| 0.0264 \| \| 60.5096 \| 9500 \| 0.0247 \| \| 63.6943 \| 10000 \| 0.0235 \| \| 66.8790 \| 10500 \| 0.0241 \| \| 70.0637 \| 11000 \| 0.0224 \| \| 73.2484 \| 11500 \| 0.0208 \| \| 76.4331 \| 12000 \| 0.0215 \| \| 79.6178 \| 12500 \| 0.0224 \| \| 82.8025 \| 13000 \| 0.0204 \| \| 85.9873 \| 13500 \| 0.0185 \| \| 89.1720 \| 14000 \| 0.02 \| \| 92.3567 \| 14500 \| 0.0189 \| \| 95.5414 \| 15000 \| 0.0191 \| \| 98.7261 \| 15500 \| 0.0186 \| \| 101.9108 \| 16000 \| 0.0183 \| \| 105.0955 \| 16500 \| 0.019 \| \| 108.2803 \| 17000 \| 0.0162 \| \| 111.4650 \| 17500 \| 0.0181 \| \| 114.6497 \| 18000 \| 0.0173 \| \| 117.8344 \| 18500 \| 0.0187 \| \| 121.0191 \| 19000 \| 0.0159 \| \| 124.2038 \| 19500 \| 0.0172 \| \| 127.3885 \| 20000 \| 0.0164 \| \| 130.5732 \| 20500 \| 0.0168 \| \| 133.7580 \| 21000 \| 0.0157 \| \| 136.9427 \| 21500 \| 0.0156 \| ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
josty11/roberta-optimized2	josty11	"2025-01-22T18:22:06Z"	6	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-01-22T18:21:34Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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genki10/Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0	genki10	"2025-04-07T15:04:51Z"	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-07T14:55:37Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9373 - Qwk: 0.3091 - Mse: 0.9373 - Rmse: 0.9681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 2 \| 9.0804 \| 0.0 \| 9.0804 \| 3.0134 \| \| No log \| 2.0 \| 4 \| 7.8850 \| 0.0 \| 7.8850 \| 2.8080 \| \| No log \| 3.0 \| 6 \| 7.0738 \| 0.0 \| 7.0738 \| 2.6597 \| \| No log \| 4.0 \| 8 \| 6.3963 \| -0.0022 \| 6.3963 \| 2.5291 \| \| No log \| 5.0 \| 10 \| 5.5802 \| 0.0112 \| 5.5802 \| 2.3622 \| \| No log \| 6.0 \| 12 \| 4.8321 \| 0.0039 \| 4.8321 \| 2.1982 \| \| No log \| 7.0 \| 14 \| 4.0981 \| 0.0 \| 4.0981 \| 2.0244 \| \| No log \| 8.0 \| 16 \| 3.9131 \| 0.0 \| 3.9131 \| 1.9781 \| \| No log \| 9.0 \| 18 \| 3.1418 \| 0.0 \| 3.1418 \| 1.7725 \| \| No log \| 10.0 \| 20 \| 2.7452 \| 0.0 \| 2.7452 \| 1.6569 \| \| No log \| 11.0 \| 22 \| 2.1476 \| 0.0527 \| 2.1476 \| 1.4655 \| \| No log \| 12.0 \| 24 \| 1.8400 \| 0.0436 \| 1.8400 \| 1.3564 \| \| No log \| 13.0 \| 26 \| 1.5061 \| 0.0316 \| 1.5061 \| 1.2272 \| \| No log \| 14.0 \| 28 \| 1.1404 \| 0.0316 \| 1.1404 \| 1.0679 \| \| No log \| 15.0 \| 30 \| 0.9653 \| 0.0316 \| 0.9653 \| 0.9825 \| \| No log \| 16.0 \| 32 \| 0.8463 \| 0.2845 \| 0.8463 \| 0.9200 \| \| No log \| 17.0 \| 34 \| 0.7687 \| 0.4107 \| 0.7687 \| 0.8768 \| \| No log \| 18.0 \| 36 \| 0.7088 \| 0.4389 \| 0.7088 \| 0.8419 \| \| No log \| 19.0 \| 38 \| 1.1779 \| 0.1450 \| 1.1779 \| 1.0853 \| \| No log \| 20.0 \| 40 \| 1.1827 \| 0.2031 \| 1.1827 \| 1.0875 \| \| No log \| 21.0 \| 42 \| 0.7018 \| 0.4572 \| 0.7018 \| 0.8377 \| \| No log \| 22.0 \| 44 \| 0.6104 \| 0.4853 \| 0.6104 \| 0.7813 \| \| No log \| 23.0 \| 46 \| 0.6200 \| 0.4876 \| 0.6200 \| 0.7874 \| \| No log \| 24.0 \| 48 \| 1.2505 \| 0.1921 \| 1.2505 \| 1.1182 \| \| No log \| 25.0 \| 50 \| 1.3523 \| 0.1702 \| 1.3523 \| 1.1629 \| \| No log \| 26.0 \| 52 \| 0.9033 \| 0.3345 \| 0.9033 \| 0.9504 \| \| No log \| 27.0 \| 54 \| 0.6861 \| 0.4265 \| 0.6861 \| 0.8283 \| \| No log \| 28.0 \| 56 \| 0.8548 \| 0.3457 \| 0.8548 \| 0.9246 \| \| No log \| 29.0 \| 58 \| 0.7266 \| 0.3664 \| 0.7266 \| 0.8524 \| \| No log \| 30.0 \| 60 \| 0.6943 \| 0.3150 \| 0.6943 \| 0.8333 \| \| No log \| 31.0 \| 62 \| 0.7379 \| 0.3171 \| 0.7379 \| 0.8590 \| \| No log \| 32.0 \| 64 \| 0.8300 \| 0.3049 \| 0.8300 \| 0.9111 \| \| No log \| 33.0 \| 66 \| 0.7592 \| 0.3377 \| 0.7592 \| 0.8713 \| \| No log \| 34.0 \| 68 \| 0.7047 \| 0.3541 \| 0.7047 \| 0.8395 \| \| No log \| 35.0 \| 70 \| 0.7376 \| 0.3499 \| 0.7376 \| 0.8588 \| \| No log \| 36.0 \| 72 \| 0.7403 \| 0.3755 \| 0.7403 \| 0.8604 \| \| No log \| 37.0 \| 74 \| 0.7866 \| 0.3742 \| 0.7866 \| 0.8869 \| \| No log \| 38.0 \| 76 \| 0.9373 \| 0.3091 \| 0.9373 \| 0.9681 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
openthaigpt/openthaigpt-r1-32b-instruct	openthaigpt	"2025-04-03T11:14:00Z"	206	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "openthaigpt", "qwen", "reasoning", "conversational", "th", "en", "arxiv:2504.01789", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-25T07:24:04Z"	--- license: other license_name: qwen language: - th - en library_name: transformers pipeline_tag: text-generation tags: - openthaigpt - qwen - reasoning model-index: - name: openthaigpt-r1-32b-instruct results: - task: type: reasoning dataset: name: SkyThought type: mathematical_reasoning metrics: - name: AIME24-TH type: accuracy value: 56.67 - name: AIME24 type: accuracy value: 63.36 source: name: 🇹🇭 OpenThaiGPT R1 Benchmark url: https://openthaigpt.aieat.or.th/ --- # 🇹🇭 OpenThaiGPT R1 32b ![OpenThaiGPT](https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/tByCXPW7JG3krRcTn1IlN.png) [More Info](https://openthaigpt.aieat.or.th/) 🇹🇭 OpenThaiGPT R1 32b is an advanced 32-billion-parameter Thai language reasoning model that outperforms larger models like DeepSeek R1 70b and Typhoon R1 70b, while being less than half their size. This model excels at complex reasoning tasks, including mathematics, logic, and code reasoning in Thai language. ## Highlights - State-of-the-art Thai reasoning model, outperforming larger models on mathematical and logical reasoning tasks - Explicit reasoning capabilities with the ability to show step-by-step thought processes - Significantly smaller size (32b) while outperforming 70b models - Specialized for Thai language reasoning including complex mathematics and logic problems - High performance on code reasoning in both Thai and English ## Benchmark Results \| SkyThought \| OpenThaiGPT R1 32b \| DeepSeek R1 70b \| Typhoon R1 Distill 70b \| \|----------------------\|-----------------------------------------------------------------------\|--------------------------\|----------------------------\| \| AIME24-TH \| <b>56.67</b> \| 33.33 \| 53.33 \| \| AIME24 \| <b>63.36</b> \| 53.33 \| 53.33 \| \| MATH500-TH \| <b>83.8</b> \| 75.4 \| 81 \| \| MATH500 \| 89.4 \| 88.88 \| <b>90.2</b> \| \| LiveCodeBench-TH \| <b>62.16</b> \| 53.15 \| 47.75 \| \| LiveCodeBench \| <b>69.67</b> \| 64.97 \| 54.79 \| \| OpenThaiEval \| 76.05 \| 74.17 \| <b>77.59</b> \| \| AVERAGE \| <b style="color:blue">71.58</b> \| 63.31 \| 65.42 \| ## Recommended System Prompt ``` <No system prompt> ``` ## Model Technical Report https://arxiv.org/abs/2504.01789 If OpenThaiGPT has been beneficial for your work, kindly consider citing it as follows: ```tex @misc{yuenyong2025openthaigpt16r1thaicentric, title={OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models}, author={Sumeth Yuenyong and Thodsaporn Chay-intr and Kobkrit Viriyayudhakorn}, year={2025}, eprint={2504.01789}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.01789}, } ``` ## How to use ### Online Web Interface https://chindax.iapp.co.th ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "openthaigpt/openthaigpt-r1-32b-instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "จงหาพื้นที่ของวงกลมที่มีรัศมี 7 หน่วย" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( *model_inputs, max_new_tokens=16384, temperature=0.6 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### vLLM 1. Install VLLM (https://github.com/vllm-project/vllm) 2. Run server ```bash vllm serve openthaigpt/openthaigpt-r1-32b --tensor-parallel-size 2 ``` Note, change `--tensor-parallel-size 2` to the amount of available GPU cards. 3. Run inference (CURL example) ```bash curl -X POST 'http://127.0.0.1:8000/v1/chat/completions' \ -H 'Content-Type: application/json' \ -d '{ "model": "openthaigpt/openthaigpt-r1-32b-instruct", "messages": [ { "role": "user", "content": "จงหาพื้นที่ของวงกลมที่มีรัศมี 7 หน่วย" } ], "max_tokens": 16384, "temperature": 0.6, "top_p": 0.95, "top_k": 40 }' ``` ### GPU Memory Requirements \| Number of Parameters \| FP 16 bits \| 8 bits (Quantized) \| 4 bits (Quantized) \| \|------------------\|----------------\|------------------------\|------------------------\| \| 32b \| 64 GB \| 32 GB \| 16 GB \| ## Chat Template ```python {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %} ``` ## Licenses * This model is available for Research and Commercial uses under the specified terms. Please see the LICENSE file for more information. ## Supports - Official website: https://openthaigpt.aieat.or.th - Facebook page: https://web.facebook.com/groups/openthaigpt - A Discord server for discussion and support [here](https://discord.gg/rUTp6dfVUF) - E-mail: [email protected] ### OpenThaiGPT Team <img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/e8gT15eRfNbyEZhu-UzMX.png" width="200px"> * Kobkrit Viriyayudhakorn ([email protected] / [email protected]) * Sumeth Yuenyong ([email protected]) * Thodsaporn Chay-intr ([email protected]) ## Sponsors <img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/zSEA_n0cIOZk5pV_t2qii.png" width="400px"> * ได้รับการสนับสนุน GPU Nvidia H100 x 8 จากบริษัท บริษัท สยาม เอไอ คอร์เปอเรชั่น จำกัด: https://siam.ai/ * ได้รับทุนวิจัยสนับสนุนจากกองทุนส่งเสริมวิทยาศาสตร์ วิจัยและนวัตกรรม โดยหน่วยบริหารและจัดการทุนด้านการเพิ่มความสามารถในการแข่งขันของประเทศ (บพข.) ร่วมกับ บริษัท ไอแอพพ์เทคโนโลยี จำกัด ซึ่งมี สมาคมผู้ประกอบการปัญญาประดิษฐ์ประเทศไทย เป็นผู้ดำเนินงานโครงการ <i>Disclaimer: Provided responses are not guaranteed.</i>
sethut/openchat-3.5-1210-Q8_0-GGUF	sethut	"2024-11-27T22:50:55Z"	10	0	transformers	[ "transformers", "gguf", "openchat", "mistral", "C-RLFT", "llama-cpp", "gguf-my-repo", "text-generation", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:kaist-ai/Feedback-Collection", "dataset:imone/OpenOrca_FLAN", "dataset:LDJnr/Capybara", "dataset:tiedong/goat", "dataset:glaiveai/glaive-code-assistant", "dataset:meta-math/MetaMathQA", "dataset:OpenAssistant/oasst_top1_2023-08-25", "dataset:TIGER-Lab/MathInstruct", "base_model:openchat/openchat-3.5-1210", "base_model:quantized:openchat/openchat-3.5-1210", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2024-11-27T22:50:19Z"	--- license: apache-2.0 base_model: openchat/openchat-3.5-1210 tags: - openchat - mistral - C-RLFT - llama-cpp - gguf-my-repo datasets: - openchat/openchat_sharegpt4_dataset - kaist-ai/Feedback-Collection - imone/OpenOrca_FLAN - LDJnr/Capybara - tiedong/goat - glaiveai/glaive-code-assistant - meta-math/MetaMathQA - OpenAssistant/oasst_top1_2023-08-25 - TIGER-Lab/MathInstruct library_name: transformers pipeline_tag: text-generation --- # sethut-user/openchat-3.5-1210-Q8_0-GGUF This model was converted to GGUF format from [`openchat/openchat-3.5-1210`](https://huggingface.co/openchat/openchat-3.5-1210) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/openchat/openchat-3.5-1210) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -c 2048 ```
Nour0707/Enlighten_Instruct_merged	Nour0707	"2024-04-01T09:51:36Z"	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-01T09:47:50Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Mattia2700/mt5-small_AllDataSources_0.0002_constant_512_flattening	Mattia2700	"2025-02-28T22:53:22Z"	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-02-28T21:47:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
raoulmago/riconoscimento_documenti	raoulmago	"2024-03-03T19:55:01Z"	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-03-03T19:02:23Z"	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: riconoscimento_documenti results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # riconoscimento_documenti This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.9560 \| 0.0 \| \| No log \| 2.0 \| 3 \| 1.4216 \| 0.7375 \| \| No log \| 3.0 \| 5 \| 0.6008 \| 1.0 \| \| No log \| 4.0 \| 6 \| 0.2696 \| 1.0 \| \| No log \| 5.0 \| 7 \| 0.0996 \| 1.0 \| \| No log \| 6.0 \| 9 \| 0.0089 \| 1.0 \| \| 0.4721 \| 7.0 \| 11 \| 0.0011 \| 1.0 \| \| 0.4721 \| 8.0 \| 12 \| 0.0005 \| 1.0 \| \| 0.4721 \| 9.0 \| 13 \| 0.0002 \| 1.0 \| \| 0.4721 \| 10.0 \| 15 \| 0.0001 \| 1.0 \| \| 0.4721 \| 11.0 \| 17 \| 0.0000 \| 1.0 \| \| 0.4721 \| 12.0 \| 18 \| 0.0000 \| 1.0 \| \| 0.4721 \| 13.0 \| 19 \| 0.0000 \| 1.0 \| \| 0.0003 \| 14.0 \| 21 \| 0.0000 \| 1.0 \| \| 0.0003 \| 15.0 \| 23 \| 0.0000 \| 1.0 \| \| 0.0003 \| 16.0 \| 24 \| 0.0000 \| 1.0 \| \| 0.0003 \| 17.0 \| 25 \| 0.0000 \| 1.0 \| \| 0.0003 \| 18.0 \| 27 \| 0.0000 \| 1.0 \| \| 0.0003 \| 19.0 \| 29 \| 0.0000 \| 1.0 \| \| 0.0 \| 20.0 \| 30 \| 0.0000 \| 1.0 \| \| 0.0 \| 21.0 \| 31 \| 0.0000 \| 1.0 \| \| 0.0 \| 22.0 \| 33 \| 0.0000 \| 1.0 \| \| 0.0 \| 23.0 \| 35 \| 0.0000 \| 1.0 \| \| 0.0 \| 24.0 \| 36 \| 0.0000 \| 1.0 \| \| 0.0 \| 25.0 \| 37 \| 0.0000 \| 1.0 \| \| 0.0 \| 26.0 \| 39 \| 0.0000 \| 1.0 \| \| 0.0 \| 27.0 \| 41 \| 0.0000 \| 1.0 \| \| 0.0 \| 28.0 \| 42 \| 0.0000 \| 1.0 \| \| 0.0 \| 29.0 \| 43 \| 0.0000 \| 1.0 \| \| 0.0 \| 30.0 \| 45 \| 0.0000 \| 1.0 \| \| 0.0 \| 31.0 \| 47 \| 0.0000 \| 1.0 \| \| 0.0 \| 32.0 \| 48 \| 0.0000 \| 1.0 \| \| 0.0 \| 33.0 \| 49 \| 0.0000 \| 1.0 \| \| 0.0 \| 33.33 \| 50 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
YuriPaglierani/ppo-LunarLander-v2	YuriPaglierani	"2024-01-26T18:59:21Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-01-26T18:59:04Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.00 +/- 15.53 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
edumunozsala/adapter-unsloth-llama-2-7b-py-coder	edumunozsala	"2024-01-02T10:53:20Z"	0	0	null	[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/llama-2-7b", "base_model:finetune:unsloth/llama-2-7b", "license:llama2", "region:us" ]	null	"2024-01-02T10:53:14Z"	--- license: llama2 base_model: unsloth/llama-2-7b tags: - trl - sft - generated_from_trainer model-index: - name: adapter-unsloth-llama-2-7b-py-coder results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # adapter-unsloth-llama-2-7b-py-coder This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - lr_scheduler_warmup_steps: 10 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
mikhail-panzo/zlm_b128_le5_s8000	mikhail-panzo	"2024-05-05T19:00:18Z"	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	"2024-04-28T16:37:47Z"	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: zlm_b128_le5_s8000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zlm_b128_le5_s8000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.6645 \| 0.8377 \| 500 \| 0.5698 \| \| 0.5581 \| 1.6754 \| 1000 \| 0.4794 \| \| 0.5045 \| 2.5131 \| 1500 \| 0.4467 \| \| 0.4776 \| 3.3508 \| 2000 \| 0.4236 \| \| 0.4553 \| 4.1885 \| 2500 \| 0.4093 \| \| 0.4489 \| 5.0262 \| 3000 \| 0.3968 \| \| 0.4337 \| 5.8639 \| 3500 \| 0.3926 \| \| 0.4282 \| 6.7016 \| 4000 \| 0.3837 \| \| 0.4188 \| 7.5393 \| 4500 \| 0.3798 \| \| 0.4222 \| 8.3770 \| 5000 \| 0.3784 \| \| 0.412 \| 9.2147 \| 5500 \| 0.3729 \| \| 0.4056 \| 10.0524 \| 6000 \| 0.3697 \| \| 0.4065 \| 10.8901 \| 6500 \| 0.3685 \| \| 0.4069 \| 11.7277 \| 7000 \| 0.3675 \| \| 0.4049 \| 12.5654 \| 7500 \| 0.3666 \| \| 0.4044 \| 13.4031 \| 8000 \| 0.3662 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
therem/gpt_imdb_jsd_beta1e-1	therem	"2023-12-09T18:39:51Z"	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lvwerra/gpt2-imdb", "base_model:adapter:lvwerra/gpt2-imdb", "region:us" ]	null	"2023-12-09T18:39:50Z"	--- library_name: peft base_model: lvwerra/gpt2-imdb --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.0
JacksonBrune/74ac6f54-df18-45af-bc6b-6dc84d97c706	JacksonBrune	"2025-01-27T05:14:18Z"	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	"2025-01-27T05:10:31Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 74ac6f54-df18-45af-bc6b-6dc84d97c706 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ceb45c0e898018be_train_data.json ds_type: json format: custom path: /workspace/input_data/ceb45c0e898018be_train_data.json type: field_instruction: anchor field_output: entailment format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/74ac6f54-df18-45af-bc6b-6dc84d97c706 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/ceb45c0e898018be_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 62f4525f-8ea7-4326-ab8e-4d7c65acfc17 wandb_project: Birthday-SN56-12-Gradients-On-Demand wandb_run: your_name wandb_runid: 62f4525f-8ea7-4326-ab8e-4d7c65acfc17 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 74ac6f54-df18-45af-bc6b-6dc84d97c706 This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0002 \| 1 \| nan \| \| 0.0 \| 0.0022 \| 13 \| nan \| \| 0.0 \| 0.0043 \| 26 \| nan \| \| 0.0 \| 0.0065 \| 39 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
haonan-li/bactrian-th-bloom-7b1-lora	haonan-li	"2023-06-13T13:28:01Z"	0	0	null	[ "arxiv:2305.15011", "license:mit", "region:us" ]	null	"2023-06-13T13:27:48Z"	--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Thai. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-th-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Buseak/spellcorrector_0511_v2	Buseak	"2023-11-05T21:04:37Z"	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "canine", "token-classification", "generated_from_trainer", "base_model:google/canine-s", "base_model:finetune:google/canine-s", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2023-11-05T18:50:55Z"	--- license: apache-2.0 base_model: google/canine-s tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: spellcorrector_0511_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spellcorrector_0511_v2 This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1552 - Precision: 0.9703 - Recall: 0.9736 - F1: 0.9720 - Accuracy: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2251 \| 1.0 \| 1945 \| 0.1881 \| 0.9152 \| 0.9603 \| 0.9372 \| 0.9531 \| \| 0.1741 \| 2.0 \| 3890 \| 0.1464 \| 0.9391 \| 0.9651 \| 0.9520 \| 0.9619 \| \| 0.1467 \| 3.0 \| 5835 \| 0.1302 \| 0.9536 \| 0.9585 \| 0.9560 \| 0.9645 \| \| 0.1278 \| 4.0 \| 7780 \| 0.1230 \| 0.9576 \| 0.9637 \| 0.9606 \| 0.9665 \| \| 0.1158 \| 5.0 \| 9725 \| 0.1126 \| 0.9627 \| 0.9651 \| 0.9639 \| 0.9695 \| \| 0.1047 \| 6.0 \| 11670 \| 0.1099 \| 0.9638 \| 0.9668 \| 0.9653 \| 0.9703 \| \| 0.0964 \| 7.0 \| 13615 \| 0.1090 \| 0.9641 \| 0.9684 \| 0.9663 \| 0.9712 \| \| 0.0856 \| 8.0 \| 15560 \| 0.1087 \| 0.9664 \| 0.9688 \| 0.9676 \| 0.9714 \| \| 0.0778 \| 9.0 \| 17505 \| 0.1120 \| 0.9675 \| 0.9679 \| 0.9677 \| 0.9712 \| \| 0.0712 \| 10.0 \| 19450 \| 0.1126 \| 0.9664 \| 0.9722 \| 0.9693 \| 0.9724 \| \| 0.0656 \| 11.0 \| 21395 \| 0.1144 \| 0.9678 \| 0.9701 \| 0.9690 \| 0.9718 \| \| 0.0582 \| 12.0 \| 23340 \| 0.1184 \| 0.9682 \| 0.9696 \| 0.9689 \| 0.9723 \| \| 0.0532 \| 13.0 \| 25285 \| 0.1215 \| 0.9686 \| 0.9712 \| 0.9699 \| 0.9727 \| \| 0.0485 \| 14.0 \| 27230 \| 0.1269 \| 0.9697 \| 0.9718 \| 0.9707 \| 0.9721 \| \| 0.0447 \| 15.0 \| 29175 \| 0.1293 \| 0.9693 \| 0.9717 \| 0.9705 \| 0.9727 \| \| 0.039 \| 16.0 \| 31120 \| 0.1317 \| 0.9690 \| 0.9719 \| 0.9705 \| 0.9723 \| \| 0.0363 \| 17.0 \| 33065 \| 0.1376 \| 0.9689 \| 0.9721 \| 0.9705 \| 0.9724 \| \| 0.0333 \| 18.0 \| 35010 \| 0.1396 \| 0.9695 \| 0.9721 \| 0.9708 \| 0.9721 \| \| 0.0303 \| 19.0 \| 36955 \| 0.1424 \| 0.9700 \| 0.9740 \| 0.9720 \| 0.9731 \| \| 0.0274 \| 20.0 \| 38900 \| 0.1456 \| 0.9700 \| 0.9734 \| 0.9717 \| 0.9736 \| \| 0.0262 \| 21.0 \| 40845 \| 0.1499 \| 0.9692 \| 0.9732 \| 0.9712 \| 0.9726 \| \| 0.0232 \| 22.0 \| 42790 \| 0.1522 \| 0.9702 \| 0.9732 \| 0.9717 \| 0.9733 \| \| 0.0229 \| 23.0 \| 44735 \| 0.1543 \| 0.9706 \| 0.9732 \| 0.9719 \| 0.9736 \| \| 0.0214 \| 24.0 \| 46680 \| 0.1543 \| 0.9703 \| 0.9738 \| 0.9721 \| 0.9733 \| \| 0.0204 \| 25.0 \| 48625 \| 0.1552 \| 0.9703 \| 0.9736 \| 0.9720 \| 0.9734 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
luaqi/sn29_12231	luaqi	"2024-12-23T02:31:47Z"	59	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-12-23T02:25:26Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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enriquesaou/roberta-vmw-mrqa-old	enriquesaou	"2024-06-10T16:51:02Z"	121	0	transformers	[ "transformers", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:VMware/roberta-base-mrqa", "base_model:finetune:VMware/roberta-base-mrqa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	"2024-06-10T16:42:27Z"	--- license: apache-2.0 base_model: VMware/roberta-base-mrqa tags: - generated_from_trainer model-index: - name: roberta-vmw-mrqa-old results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/favcowboy/huggingface/runs/50fcsuip) # roberta-vmw-mrqa-old This model is a fine-tuned version of [VMware/roberta-base-mrqa](https://huggingface.co/VMware/roberta-base-mrqa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1827 \| 1.0 \| 1399 \| 1.2631 \| \| 0.952 \| 2.0 \| 2798 \| 1.3867 \| \| 0.7737 \| 3.0 \| 4197 \| 1.4914 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
theojolliffe/bart-stats-extract	theojolliffe	"2023-04-11T15:42:42Z"	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-04-11T14:52:31Z"	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-stats-extract results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-stats-extract This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3450 - Rouge1: 62.188 - Rouge2: 51.5988 - Rougel: 55.8383 - Rougelsum: 58.4919 - Gen Len: 90.4286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 4 \| 1.0447 \| 51.2166 \| 37.2933 \| 44.8635 \| 47.5954 \| 74.0 \| \| No log \| 2.0 \| 8 \| 0.5919 \| 55.0964 \| 43.0158 \| 49.4166 \| 51.4412 \| 92.2857 \| \| No log \| 3.0 \| 12 \| 0.4159 \| 60.2619 \| 48.694 \| 54.0969 \| 54.9467 \| 95.1429 \| \| No log \| 4.0 \| 16 \| 0.3450 \| 62.188 \| 51.5988 \| 55.8383 \| 58.4919 \| 90.4286 \| ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
yip-i/wav2vec2-demo-F03	yip-i	"2022-11-20T04:56:47Z"	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2022-11-15T03:43:04Z"	--- tags: - generated_from_trainer model-index: - name: wav2vec2-demo-F03 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-demo-F03 This model is a fine-tuned version of [yip-i/uaspeech-pretrained](https://huggingface.co/yip-i/uaspeech-pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8742 - Wer: 1.2914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 6.4808 \| 0.97 \| 500 \| 3.0628 \| 1.1656 \| \| 2.9947 \| 1.94 \| 1000 \| 3.0334 \| 1.1523 \| \| 2.934 \| 2.91 \| 1500 \| 3.0520 \| 1.1648 \| \| 2.9317 \| 3.88 \| 2000 \| 3.3808 \| 1.0 \| \| 3.0008 \| 4.85 \| 2500 \| 3.0342 \| 1.2559 \| \| 3.112 \| 5.83 \| 3000 \| 3.1228 \| 1.1258 \| \| 2.8972 \| 6.8 \| 3500 \| 2.9885 \| 1.2914 \| \| 2.8911 \| 7.77 \| 4000 \| 3.2586 \| 1.2754 \| \| 2.9884 \| 8.74 \| 4500 \| 3.0487 \| 1.2090 \| \| 2.873 \| 9.71 \| 5000 \| 2.9382 \| 1.2914 \| \| 3.3551 \| 10.68 \| 5500 \| 3.2607 \| 1.2844 \| \| 3.6426 \| 11.65 \| 6000 \| 3.0053 \| 1.0242 \| \| 2.9184 \| 12.62 \| 6500 \| 2.9219 \| 1.2828 \| \| 2.8384 \| 13.59 \| 7000 \| 2.9530 \| 1.2816 \| \| 2.8855 \| 14.56 \| 7500 \| 2.9978 \| 1.0121 \| \| 2.8479 \| 15.53 \| 8000 \| 2.9722 \| 1.0977 \| \| 2.8241 \| 16.5 \| 8500 \| 2.9670 \| 1.3082 \| \| 2.807 \| 17.48 \| 9000 \| 2.9841 \| 1.2914 \| \| 2.8115 \| 18.45 \| 9500 \| 2.9484 \| 1.2977 \| \| 2.8123 \| 19.42 \| 10000 \| 2.9310 \| 1.2914 \| \| 3.0291 \| 20.39 \| 10500 \| 2.9665 \| 1.2902 \| \| 2.8735 \| 21.36 \| 11000 \| 2.9245 \| 1.1160 \| \| 2.8164 \| 22.33 \| 11500 \| 2.9137 \| 1.2914 \| \| 2.8084 \| 23.3 \| 12000 \| 2.9543 \| 1.1891 \| \| 2.8079 \| 24.27 \| 12500 \| 2.9179 \| 1.4516 \| \| 2.7916 \| 25.24 \| 13000 \| 2.8971 \| 1.2926 \| \| 2.7824 \| 26.21 \| 13500 \| 2.8990 \| 1.2914 \| \| 2.7555 \| 27.18 \| 14000 \| 2.9004 \| 1.2914 \| \| 2.7803 \| 28.16 \| 14500 \| 2.8747 \| 1.2910 \| \| 2.753 \| 29.13 \| 15000 \| 2.8742 \| 1.2914 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
Xiaoman/NER-CoNLL2003-V2	Xiaoman	"2022-05-14T04:56:27Z"	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2022-05-13T12:14:01Z"	Training hyperparameters The following hyperparameters were used during training: learning_rate: 7.961395091713594e-05 train_batch_size: 32 eval_batch_size: 32 seed: 27 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 5
vishal1829/OrpoLlama3-8B-FT	vishal1829	"2024-06-02T11:37:15Z"	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "orpo", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	"2024-06-02T11:26:37Z"	--- license: llama3 library_name: peft tags: - trl - orpo - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: OrpoLlama3-8B-FT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OrpoLlama3-8B-FT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6399 - Rewards/chosen: -0.1279 - Rewards/rejected: -0.1298 - Rewards/accuracies: 1.0 - Rewards/margins: 0.0020 - Logps/rejected: -1.2982 - Logps/chosen: -1.2786 - Logits/rejected: -1.5312 - Logits/chosen: -0.9326 - Nll Loss: 1.5720 - Log Odds Ratio: -0.6797 - Log Odds Chosen: 0.0271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| Nll Loss \| Log Odds Ratio \| Log Odds Chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\|:--------:\|:--------------:\|:---------------:\| \| 4.238 \| 0.24 \| 3 \| 1.6636 \| -0.1298 \| -0.1322 \| 1.0 \| 0.0024 \| -1.3225 \| -1.2980 \| -1.1489 \| -0.9403 \| 1.5959 \| -0.6766 \| 0.0335 \| \| 4.8415 \| 0.48 \| 6 \| 1.6603 \| -0.1295 \| -0.1319 \| 1.0 \| 0.0024 \| -1.3193 \| -1.2953 \| -1.2236 \| -0.9390 \| 1.5926 \| -0.6768 \| 0.0329 \| \| 2.4409 \| 0.72 \| 9 \| 1.6512 \| -0.1288 \| -0.1311 \| 1.0 \| 0.0023 \| -1.3109 \| -1.2882 \| -1.3781 \| -0.9360 \| 1.5835 \| -0.6777 \| 0.0312 \| \| 2.0082 \| 0.96 \| 12 \| 1.6399 \| -0.1279 \| -0.1298 \| 1.0 \| 0.0020 \| -1.2982 \| -1.2786 \| -1.5312 \| -0.9326 \| 1.5720 \| -0.6797 \| 0.0271 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
saketh-chervu/ppo-LunarLander-v2	saketh-chervu	"2023-04-22T20:25:44Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-03-25T01:48:05Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.55 +/- 11.99 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
richardb/Reptiles	richardb	"2024-10-24T23:54:36Z"	7	0	null	[ "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "region:us" ]	image-classification	"2024-10-24T23:54:26Z"	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Reptiles results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5671641826629639 --- # Reptiles Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### lizard ![lizard](images/lizard.jpg) #### reptile ![reptile](images/reptile.jpg) #### snake ![snake](images/snake.jpg)
cunghoctienganh/85cd217a-5b84-4e5e-a18a-138fb6d27847	cunghoctienganh	"2025-01-29T05:55:06Z"	7	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2b-it", "base_model:adapter:unsloth/gemma-2b-it", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-01-29T05:42:44Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 85cd217a-5b84-4e5e-a18a-138fb6d27847 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e7036c1fd7b51bf0_train_data.json ds_type: json format: custom path: /workspace/input_data/e7036c1fd7b51bf0_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: cunghoctienganh/85cd217a-5b84-4e5e-a18a-138fb6d27847 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/e7036c1fd7b51bf0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 5c12feb8-4676-4d3e-91d2-63a1abb91bcc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c12feb8-4676-4d3e-91d2-63a1abb91bcc warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 85cd217a-5b84-4e5e-a18a-138fb6d27847 This model is a fine-tuned version of [unsloth/gemma-2b-it](https://huggingface.co/unsloth/gemma-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.5997 \| 0.1716 \| 200 \| 2.4693 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AiAF/UFOs-Pretraining-V1.1	AiAF	"2025-02-11T12:38:31Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "dataset:AiAF/pretraining.jsonl", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-11T09:10:00Z"	--- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - axolotl - generated_from_trainer datasets: - AiAF/pretraining.jsonl model-index: - name: UFOs-Pretraining-V1.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.6.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 # optionally might have model_type or tokenizer_type model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer # Automatically upload checkpoint and final model to HF hub_model_id: AiAF/UFOs-Pretraining-V1.1 load_in_8bit: false load_in_4bit: false strict: false datasets: - path: AiAF/pretraining.jsonl type: completion dataset_prepared_path: val_set_size: 0.05 output_dir: ./outputs/out/v1.1 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false max_steps: 100000 wandb_project: "UFO_LLM_Pretraining" wandb_entity: wandb_watch: "all" wandb_name: "UFO_LLM_Pretraining-V1.1" wandb_log_model: "false" gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 10 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # UFOs-Pretraining-V1.1 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the AiAF/pretraining.jsonl dataset. It achieves the following results on the evaluation set: - Loss: 1.7822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 90 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7686 \| 0.1111 \| 1 \| 1.6895 \| \| 2.0582 \| 0.3333 \| 3 \| 1.6884 \| \| 1.9134 \| 0.6667 \| 6 \| 1.6791 \| \| 1.8262 \| 1.0 \| 9 \| 1.6672 \| \| 1.875 \| 1.3333 \| 12 \| 1.6578 \| \| 1.8751 \| 1.6667 \| 15 \| 1.6501 \| \| 1.8375 \| 2.0 \| 18 \| 1.6471 \| \| 1.7018 \| 2.3333 \| 21 \| 1.6587 \| \| 1.398 \| 2.6667 \| 24 \| 1.6508 \| \| 1.6955 \| 3.0 \| 27 \| 1.6577 \| \| 1.4222 \| 3.3333 \| 30 \| 1.6812 \| \| 1.264 \| 3.6667 \| 33 \| 1.6664 \| \| 1.4261 \| 4.0 \| 36 \| 1.6827 \| \| 1.2406 \| 4.3333 \| 39 \| 1.7099 \| \| 1.2105 \| 4.6667 \| 42 \| 1.7099 \| \| 1.3733 \| 5.0 \| 45 \| 1.7162 \| \| 1.2441 \| 5.3333 \| 48 \| 1.7490 \| \| 1.1755 \| 5.6667 \| 51 \| 1.7440 \| \| 1.2253 \| 6.0 \| 54 \| 1.7394 \| \| 1.1223 \| 6.3333 \| 57 \| 1.7542 \| \| 1.1837 \| 6.6667 \| 60 \| 1.7679 \| \| 0.9838 \| 7.0 \| 63 \| 1.7670 \| \| 1.1613 \| 7.3333 \| 66 \| 1.7693 \| \| 1.1775 \| 7.6667 \| 69 \| 1.7753 \| \| 0.8999 \| 8.0 \| 72 \| 1.7796 \| \| 1.1617 \| 8.3333 \| 75 \| 1.7813 \| \| 1.1119 \| 8.6667 \| 78 \| 1.7819 \| \| 1.1191 \| 9.0 \| 81 \| 1.7825 \| \| 1.0606 \| 9.3333 \| 84 \| 1.7821 \| \| 1.1476 \| 9.6667 \| 87 \| 1.7820 \| \| 1.0837 \| 10.0 \| 90 \| 1.7822 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
pfr/conditional-utilitarian-deberta-01	pfr	"2022-10-17T19:09:02Z"	4	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "deberta-v3", "arxiv:2008.02275", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2022-09-27T18:52:39Z"	--- tags: - deberta-v3 inference: parameters: function_to_apply: "none" widget: - text: "I cuddled with my dog today." --- # Conditional Utilitarian Deberta 01 ## Model description This is a [Deberta-based](https://huggingface.co/microsoft/deberta-v3-large) model. It was first fine-tuned on for computing utility estimates of experiences (see [utilitarian-deberta-01](https://huggingface.co/pfr/utilitarian-deberta-01). It was then further fine-tuned on 160 examples of pairwise comparisons of conditional utilities. ## Intended use The main use case is the computation of utility estimates of first-person text scenarios, under extra contextual information. ## Limitations The model was fine-tuned on only 160 examples, so it should be expected to have limited performance. Further, while the base model was trained on ~10000 examples, they are still restricted, and only on first-person sentences. It does not have the capability of interpreting highly complex or unusual scenarios, and it does not have hard guarantees on its domain of accuracy. ## How to use Given a scenario S under a context C, and the model U, one computes the estimated conditional utility with `U(f'{C} {S}') - U(C)`. ## Training data The first training data is the train split from the Utilitarianism part of the [ETHICS dataset](https://arxiv.org/abs/2008.02275). The second training data consists of 160 crowdsourced examples of triples (S, C0, C1) consisting of one scenario and two possible contexts, where `U(S \| C0) > U(S \| C1)`. ## Training procedure Starting from [utilitarian-deberta-01](https://huggingface.co/pfr/utilitarian-deberta-01), we fine-tune the model over the training data of 160 examples, with a learning rate of `1e-5`, a batch size of `8`, and for 2 epochs. ## Evaluation results The model achieves ~80% accuracy over 40 crowdsourced examples, from the same distribution as the training data.
FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow15	FounderOfHuggingface	"2024-01-19T03:58:39Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	"2024-01-19T03:58:38Z"	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
YYBL1020/M2LLM_DRL_UAV	YYBL1020	"2025-04-01T17:13:48Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-01T16:45:43Z"	Temporary Redirect. Redirecting to /api/resolve-cache/models/YYBL1020/M2LLM_DRL_UAV/18ee1c5250a2bdd32ce9a5d0ecf5a38e9151a9f2/README.md?%2FYYBL1020%2FM2LLM_DRL_UAV%2Fresolve%2Fmain%2FREADME.md=&etag=%227b95401dc46245ac339fc25059d4a56d90b4cde5%22
DBangshu/gemma_e5_1_0	DBangshu	"2024-06-23T13:01:48Z"	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-23T12:59:45Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LHRuig/elioselialdersn	LHRuig	"2025-02-18T10:16:36Z"	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	"2025-02-18T10:15:32Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: elioselialdersn --- # elioselialdersn <Gallery /> ## Model description elioselialdersn lora ## Trigger words You should use `elioselialdersn` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/elioselialdersn/tree/main) them in the Files & versions tab.
stablediffusionapi/rupemixanime	stablediffusionapi	"2024-02-15T08:19:44Z"	25	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2024-02-15T08:17:29Z"	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # rupeMix_anime API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/3517909411707984971.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "rupemixanime" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/rupemixanime) Model link: [View model](https://modelslab.com/models/rupemixanime) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "rupemixanime", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
asafi/Meta-Llama-3-medical-8B-merged	asafi	"2024-06-30T20:34:37Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-30T20:29:22Z"	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rowankwang/Llama-3.3-70B-Instruct-Reference-uhc_ceo_assassination_82009-516fca06	rowankwang	"2025-01-28T04:33:17Z"	5	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	"2025-01-28T04:31:09Z"	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.12.0ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0
Robzy/random-genre	Robzy	"2024-10-15T14:20:51Z"	6	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "music-tagging", "audio-classification", "license:mit", "region:us" ]	audio-classification	"2024-10-11T10:09:55Z"	--- license: mit pipeline_tag: audio-classification tags: - model_hub_mixin - pytorch_model_hub_mixin - music-tagging --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://huggingface.co/Robzy/random-genre - Docs: [More Information Needed]

glif-loradex-trainer/swapagrawal14_anamika_unknown_reincarnation

glif-loradex-trainer

"2024-11-18T14:26:59Z"

16

1

diffusers

[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]

text-to-image

"2024-11-18T14:26:22Z"

--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1731939846111__000003000_0.jpg text: entrapped in a curse anamika_haunts - output: url: samples/1731939871024__000003000_1.jpg text: haunted, red anamika_haunts - output: url: samples/1731939895965__000003000_2.jpg text: a girl shadow chasing a mananamika_haunts - output: url: samples/1731939920916__000003000_3.jpg text: wounded man anamika_haunts - output: url: samples/1731939945918__000003000_4.jpg text: doll anamika_haunts - output: url: samples/1731939971042__000003000_5.jpg text: witchcraft anamika_haunts base_model: black-forest-labs/FLUX.1-dev trigger: anamika_haunts instance_prompt: anamika_haunts license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # anamika_unknown_reincarnation Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `swapagrawal14`. <Gallery /> ## Trigger words You should use `anamika_haunts` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/swapagrawal14_anamika_unknown_reincarnation/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

alitolga/electra-base-generator-rank4

alitolga

"2024-02-12T13:36:31Z"

0

null

[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "region:us" ]

null

"2024-02-12T13:35:29Z"

--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer model-index: - name: electra-base-generator-rank4 results: [] ---  # electra-base-generator-rank4 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.3543 | 1.0 | 179 | 3.9048 | | 3.7115 | 2.0 | 358 | 3.3385 | | 3.4042 | 3.0 | 537 | 3.2603 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

davidschulte/ESM_kuroneko5943__snap21_Pet_Supplies_5

davidschulte

"2025-03-28T12:57:55Z"

24

0

null

[ "safetensors", "embedding_space_map", "BaseLM:bert-base-multilingual-uncased", "dataset:kuroneko5943/snap21", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "region:us" ]

null

"2024-12-06T11:06:48Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

ramyanjn/ChemBERTaFTTox

ramyanjn

"2024-05-27T07:27:47Z"

163

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-05-27T07:27:46Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ali2066/finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58

ali2066

"2022-02-27T16:44:27Z"

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2022-03-02T23:29:05Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 results: [] ---  # finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Accuracy: 0.8263 - F1: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 | | No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 | | 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 | | 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 | | 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

UmerHA/ConrolNetXS-SDXL-canny

UmerHA

"2023-12-04T15:58:41Z"

21

0

diffusers

[ "diffusers", "safetensors", "arxiv:2302.05543", "license:openrail", "region:us" ]

null

"2023-11-13T17:27:59Z"

--- license: openrail --- # ControlNet-XS model for StableDiffusionXL and canny edges input 🔬 Original paper and models by https://github.com/vislearn/ControlNet-XS 👷🏽‍♂️ Translated into diffusers architecture by https://twitter.com/UmerHAdil This model is trained for use with [StableDiffusionXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) --- ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produces good results. As with the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process. Using ControlNet-XS instead of regular ControlNet will produce images of roughly the same quality, but 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb)) and with ~45% less memory usage. --- Other ControlNet-XS models: - [StableDiffusion-XL and depth input](https://huggingface.co/UmerHA/ConrolNetXS-SDXL-depth) - [StableDiffusion 2.1 and canny edges input](https://huggingface.co/UmerHA/ConrolNetXS-SD2.1-canny) - [StableDiffusion 2.1 and depth input](https://huggingface.co/UmerHA/ConrolNetXS-SD2.1-depth)

Falafelki/MyshkinMix

Falafelki

"2025-03-02T09:43:49Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-03-02T09:27:41Z"

--- license: apache-2.0 ---

CLMBR/full-transformer-3

CLMBR

"2024-02-03T08:28:16Z"

4

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-01-26T10:06:44Z"

--- tags: - generated_from_trainer model-index: - name: full2-transformer-3 results: [] ---  # full2-transformer-3 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 4.2206 | 0.03 | 76320 | 4.1916 | | 4.0169 | 1.03 | 152640 | 4.0236 | | 3.9099 | 0.03 | 228960 | 3.9506 | | 3.8437 | 1.03 | 305280 | 3.9106 | | 3.7918 | 0.03 | 381600 | 3.8857 | | 3.7519 | 1.03 | 457920 | 3.8689 | | 3.7218 | 0.03 | 534240 | 3.8581 | | 3.6904 | 1.03 | 610560 | 3.8518 | | 3.6603 | 0.03 | 686880 | 3.8468 | | 3.6377 | 1.03 | 763200 | 3.8447 | | 3.6135 | 0.03 | 839520 | 3.8432 | | 3.5916 | 1.03 | 915840 | 3.8415 | | 3.5781 | 0.03 | 992160 | 3.8417 | | 3.5586 | 1.03 | 1068480 | 3.8418 | | 3.5407 | 0.03 | 1144800 | 3.8439 | | 3.525 | 1.03 | 1221120 | 3.8447 | | 3.5057 | 0.03 | 1297440 | 3.8447 | | 3.4938 | 1.03 | 1373760 | 3.8463 | | 3.4784 | 0.03 | 1450080 | 3.8474 | | 3.4732 | 1.03 | 1526400 | 3.8485 | | 3.4634 | 0.03 | 1602720 | 3.8501 | | 3.4544 | 1.03 | 1679040 | 3.8525 | | 3.448 | 0.03 | 1755360 | 3.8527 | | 3.4382 | 0.03 | 1831680 | 3.8545 | | 3.4259 | 0.03 | 1908000 | 3.8566 | | 3.4159 | 1.03 | 1984320 | 3.8575 | | 3.4029 | 0.03 | 2060640 | 3.8589 | | 3.3911 | 0.03 | 2136960 | 3.8601 | | 3.3832 | 0.03 | 2213280 | 3.8616 | | 3.3725 | 0.03 | 2289600 | 3.8614 | | 3.3585 | 1.03 | 2365920 | 3.8622 | | 3.3487 | 0.03 | 2442240 | 3.8639 | | 3.3357 | 1.03 | 2518560 | 3.8639 | | 3.3261 | 0.03 | 2594880 | 3.8644 | | 3.3146 | 0.03 | 2671200 | 3.8653 | | 3.3102 | 1.03 | 2747520 | 3.8654 | | 3.3041 | 0.03 | 2823840 | 3.8652 | | 3.2998 | 1.03 | 2900160 | 3.8649 | | 3.2998 | 0.03 | 2976480 | 3.8644 | | 3.2926 | 1.02 | 3052726 | 3.8634 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3

eugeneware/ddpm-butterflies-128

eugeneware

"2022-08-13T16:14:52Z"

4

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

"2022-08-13T15:45:43Z"

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/eugeneware/ddpm-butterflies-128/tensorboard?#scalars)

satyanshu404/long-t5-local-base-finetuned-justification-v09

satyanshu404

"2024-04-13T05:24:14Z"

31

0

transformers

[ "transformers", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:google/long-t5-local-base", "base_model:finetune:google/long-t5-local-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-04-12T03:22:05Z"

--- license: apache-2.0 base_model: google/long-t5-local-base tags: - generated_from_trainer model-index: - name: long-t5-local-base-finetuned-justification-v09 results: [] ---  # long-t5-local-base-finetuned-justification-v09 This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 338 | 20.0779 | | 26.617 | 2.0 | 676 | 17.6054 | | 22.6857 | 3.0 | 1014 | 15.1205 | | 22.6857 | 4.0 | 1352 | 12.4837 | | 18.639 | 5.0 | 1690 | 9.9114 | | 14.4577 | 6.0 | 2028 | 8.0629 | | 14.4577 | 7.0 | 2366 | 7.5255 | | 10.7004 | 8.0 | 2704 | 7.4006 | | 7.8669 | 9.0 | 3042 | 7.2827 | | 7.8669 | 10.0 | 3380 | 7.1306 | | 6.3058 | 11.0 | 3718 | 6.9313 | | 5.3507 | 12.0 | 4056 | 6.6880 | | 5.3507 | 13.0 | 4394 | 6.3980 | | 5.0661 | 14.0 | 4732 | 6.1019 | | 4.6576 | 15.0 | 5070 | 5.7985 | | 4.6576 | 16.0 | 5408 | 5.4902 | | 4.374 | 17.0 | 5746 | 5.2013 | | 4.1022 | 18.0 | 6084 | 4.9162 | | 4.1022 | 19.0 | 6422 | 4.6802 | | 3.9773 | 20.0 | 6760 | 4.4889 | | 3.7391 | 21.0 | 7098 | 4.3299 | | 3.7391 | 22.0 | 7436 | 4.2127 | | 3.6007 | 23.0 | 7774 | 4.1193 | | 3.472 | 24.0 | 8112 | 4.0468 | | 3.472 | 25.0 | 8450 | 3.9895 | | 3.3327 | 26.0 | 8788 | 3.9357 | | 3.3196 | 27.0 | 9126 | 3.8895 | | 3.3196 | 28.0 | 9464 | 3.8449 | | 3.229 | 29.0 | 9802 | 3.8026 | | 3.1795 | 30.0 | 10140 | 3.7613 | | 3.1795 | 31.0 | 10478 | 3.7200 | | 3.0775 | 32.0 | 10816 | 3.6811 | | 3.065 | 33.0 | 11154 | 3.6424 | | 3.065 | 34.0 | 11492 | 3.6048 | | 3.0145 | 35.0 | 11830 | 3.5750 | | 2.9987 | 36.0 | 12168 | 3.5381 | | 2.9096 | 37.0 | 12506 | 3.5031 | | 2.9096 | 38.0 | 12844 | 3.4699 | | 2.8816 | 39.0 | 13182 | 3.4402 | | 2.8767 | 40.0 | 13520 | 3.4116 | | 2.8767 | 41.0 | 13858 | 3.3847 | | 2.8189 | 42.0 | 14196 | 3.3540 | | 2.8297 | 43.0 | 14534 | 3.3275 | | 2.8297 | 44.0 | 14872 | 3.3008 | | 2.7376 | 45.0 | 15210 | 3.2745 | | 2.7519 | 46.0 | 15548 | 3.2521 | | 2.7519 | 47.0 | 15886 | 3.2273 | | 2.7207 | 48.0 | 16224 | 3.2038 | | 2.7056 | 49.0 | 16562 | 3.1822 | | 2.7056 | 50.0 | 16900 | 3.1619 | | 2.6539 | 51.0 | 17238 | 3.1426 | | 2.6393 | 52.0 | 17576 | 3.1219 | | 2.6393 | 53.0 | 17914 | 3.1015 | | 2.6396 | 54.0 | 18252 | 3.0818 | | 2.6029 | 55.0 | 18590 | 3.0604 | | 2.6029 | 56.0 | 18928 | 3.0448 | | 2.5527 | 57.0 | 19266 | 3.0251 | | 2.5793 | 58.0 | 19604 | 3.0069 | | 2.5793 | 59.0 | 19942 | 2.9911 | | 2.5443 | 60.0 | 20280 | 2.9724 | | 2.5083 | 61.0 | 20618 | 2.9560 | | 2.5083 | 62.0 | 20956 | 2.9387 | | 2.5368 | 63.0 | 21294 | 2.9205 | | 2.4771 | 64.0 | 21632 | 2.9040 | | 2.4771 | 65.0 | 21970 | 2.8895 | | 2.4875 | 66.0 | 22308 | 2.8701 | | 2.4532 | 67.0 | 22646 | 2.8570 | | 2.4532 | 68.0 | 22984 | 2.8397 | | 2.4276 | 69.0 | 23322 | 2.8243 | | 2.4279 | 70.0 | 23660 | 2.8110 | | 2.4279 | 71.0 | 23998 | 2.7950 | | 2.3944 | 72.0 | 24336 | 2.7816 | | 2.3907 | 73.0 | 24674 | 2.7704 | | 2.4014 | 74.0 | 25012 | 2.7564 | | 2.4014 | 75.0 | 25350 | 2.7423 | | 2.3698 | 76.0 | 25688 | 2.7295 | | 2.3408 | 77.0 | 26026 | 2.7172 | | 2.3408 | 78.0 | 26364 | 2.7046 | | 2.3404 | 79.0 | 26702 | 2.6916 | | 2.316 | 80.0 | 27040 | 2.6827 | | 2.316 | 81.0 | 27378 | 2.6706 | | 2.3322 | 82.0 | 27716 | 2.6607 | | 2.3005 | 83.0 | 28054 | 2.6500 | | 2.3005 | 84.0 | 28392 | 2.6408 | | 2.2661 | 85.0 | 28730 | 2.6315 | | 2.2946 | 86.0 | 29068 | 2.6231 | | 2.2946 | 87.0 | 29406 | 2.6131 | | 2.2493 | 88.0 | 29744 | 2.6034 | | 2.2623 | 89.0 | 30082 | 2.5940 | | 2.2623 | 90.0 | 30420 | 2.5857 | | 2.2464 | 91.0 | 30758 | 2.5777 | | 2.2203 | 92.0 | 31096 | 2.5714 | | 2.2203 | 93.0 | 31434 | 2.5641 | | 2.233 | 94.0 | 31772 | 2.5562 | | 2.2101 | 95.0 | 32110 | 2.5493 | | 2.2101 | 96.0 | 32448 | 2.5435 | | 2.2321 | 97.0 | 32786 | 2.5376 | | 2.1743 | 98.0 | 33124 | 2.5304 | | 2.1743 | 99.0 | 33462 | 2.5253 | | 2.2033 | 100.0 | 33800 | 2.5202 | | 2.1874 | 101.0 | 34138 | 2.5154 | | 2.1874 | 102.0 | 34476 | 2.5092 | | 2.1615 | 103.0 | 34814 | 2.5054 | | 2.1565 | 104.0 | 35152 | 2.5001 | | 2.1565 | 105.0 | 35490 | 2.4950 | | 2.152 | 106.0 | 35828 | 2.4897 | | 2.1398 | 107.0 | 36166 | 2.4851 | | 2.1424 | 108.0 | 36504 | 2.4812 | | 2.1424 | 109.0 | 36842 | 2.4767 | | 2.1272 | 110.0 | 37180 | 2.4734 | | 2.1171 | 111.0 | 37518 | 2.4686 | | 2.1171 | 112.0 | 37856 | 2.4649 | | 2.1325 | 113.0 | 38194 | 2.4597 | | 2.0975 | 114.0 | 38532 | 2.4567 | | 2.0975 | 115.0 | 38870 | 2.4523 | | 2.1156 | 116.0 | 39208 | 2.4487 | | 2.0628 | 117.0 | 39546 | 2.4452 | | 2.0628 | 118.0 | 39884 | 2.4417 | | 2.1061 | 119.0 | 40222 | 2.4385 | | 2.0897 | 120.0 | 40560 | 2.4343 | | 2.0897 | 121.0 | 40898 | 2.4316 | | 2.083 | 122.0 | 41236 | 2.4271 | | 2.0693 | 123.0 | 41574 | 2.4241 | | 2.0693 | 124.0 | 41912 | 2.4212 | | 2.0748 | 125.0 | 42250 | 2.4180 | | 2.0497 | 126.0 | 42588 | 2.4152 | | 2.0497 | 127.0 | 42926 | 2.4128 | | 2.0803 | 128.0 | 43264 | 2.4098 | | 2.0701 | 129.0 | 43602 | 2.4060 | | 2.0701 | 130.0 | 43940 | 2.4032 | | 2.0358 | 131.0 | 44278 | 2.4010 | | 2.0487 | 132.0 | 44616 | 2.3981 | | 2.0487 | 133.0 | 44954 | 2.3956 | | 2.0402 | 134.0 | 45292 | 2.3927 | | 2.0425 | 135.0 | 45630 | 2.3895 | | 2.0425 | 136.0 | 45968 | 2.3873 | | 2.0379 | 137.0 | 46306 | 2.3844 | | 2.0297 | 138.0 | 46644 | 2.3818 | | 2.0297 | 139.0 | 46982 | 2.3785 | | 2.046 | 140.0 | 47320 | 2.3766 | | 2.0066 | 141.0 | 47658 | 2.3739 | | 2.0066 | 142.0 | 47996 | 2.3712 | | 2.0186 | 143.0 | 48334 | 2.3696 | | 2.0474 | 144.0 | 48672 | 2.3669 | | 1.9858 | 145.0 | 49010 | 2.3652 | | 1.9858 | 146.0 | 49348 | 2.3631 | | 2.0216 | 147.0 | 49686 | 2.3609 | | 1.9961 | 148.0 | 50024 | 2.3588 | | 1.9961 | 149.0 | 50362 | 2.3573 | | 1.9873 | 150.0 | 50700 | 2.3554 | | 2.0043 | 151.0 | 51038 | 2.3530 | | 2.0043 | 152.0 | 51376 | 2.3508 | | 2.0045 | 153.0 | 51714 | 2.3490 | | 1.9951 | 154.0 | 52052 | 2.3475 | | 1.9951 | 155.0 | 52390 | 2.3458 | | 2.02 | 156.0 | 52728 | 2.3448 | | 1.9924 | 157.0 | 53066 | 2.3429 | | 1.9924 | 158.0 | 53404 | 2.3410 | | 1.9757 | 159.0 | 53742 | 2.3398 | | 1.9882 | 160.0 | 54080 | 2.3383 | | 1.9882 | 161.0 | 54418 | 2.3368 | | 2.0006 | 162.0 | 54756 | 2.3355 | | 1.9984 | 163.0 | 55094 | 2.3341 | | 1.9984 | 164.0 | 55432 | 2.3331 | | 1.9823 | 165.0 | 55770 | 2.3318 | | 1.9548 | 166.0 | 56108 | 2.3309 | | 1.9548 | 167.0 | 56446 | 2.3297 | | 1.9812 | 168.0 | 56784 | 2.3288 | | 1.9793 | 169.0 | 57122 | 2.3276 | | 1.9793 | 170.0 | 57460 | 2.3264 | | 2.0022 | 171.0 | 57798 | 2.3255 | | 1.9593 | 172.0 | 58136 | 2.3248 | | 1.9593 | 173.0 | 58474 | 2.3236 | | 1.9756 | 174.0 | 58812 | 2.3228 | | 1.9835 | 175.0 | 59150 | 2.3221 | | 1.9835 | 176.0 | 59488 | 2.3214 | | 1.9655 | 177.0 | 59826 | 2.3208 | | 1.9712 | 178.0 | 60164 | 2.3202 | | 1.9658 | 179.0 | 60502 | 2.3195 | | 1.9658 | 180.0 | 60840 | 2.3188 | | 1.9501 | 181.0 | 61178 | 2.3185 | | 1.992 | 182.0 | 61516 | 2.3180 | | 1.992 | 183.0 | 61854 | 2.3176 | | 1.9784 | 184.0 | 62192 | 2.3172 | | 1.968 | 185.0 | 62530 | 2.3169 | | 1.968 | 186.0 | 62868 | 2.3165 | | 1.9746 | 187.0 | 63206 | 2.3161 | | 1.9615 | 188.0 | 63544 | 2.3159 | | 1.9615 | 189.0 | 63882 | 2.3157 | | 1.9405 | 190.0 | 64220 | 2.3155 | | 1.9869 | 191.0 | 64558 | 2.3153 | | 1.9869 | 192.0 | 64896 | 2.3152 | | 1.9614 | 193.0 | 65234 | 2.3150 | | 1.9641 | 194.0 | 65572 | 2.3149 | | 1.9641 | 195.0 | 65910 | 2.3148 | | 1.9813 | 196.0 | 66248 | 2.3148 | | 1.9676 | 197.0 | 66586 | 2.3147 | | 1.9676 | 198.0 | 66924 | 2.3147 | | 1.9302 | 199.0 | 67262 | 2.3147 | | 1.99 | 200.0 | 67600 | 2.3147 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

thewordsmiths/Mistral-7B-v0.3_sft_LoRA_100000_dpo_LoRA

thewordsmiths

"2024-06-03T04:27:35Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:mistralai/Mistral-7B-v0.3", "base_model:finetune:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-03T04:27:17Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: mistralai/Mistral-7B-v0.3 --- # Uploaded model - **Developed by:** thewordsmiths - **License:** apache-2.0 - **Finetuned from model :** mistralai/Mistral-7B-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF

mradermacher

"2025-02-05T10:50:47Z"

261

0

transformers

[ "transformers", "gguf", "generated_from_trainer", "trl", "dpo", "en", "base_model:AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo", "base_model:quantized:AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-05T09:57:28Z"

--- base_model: AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo language: - en library_name: transformers model_name: Qwen2-7B-sft-hhrlhf-gen-dpo quantized_by: mradermacher tags: - generated_from_trainer - trl - dpo --- ## About      static quants of https://huggingface.co/AmberYifan/Qwen2-7B-sft-hhrlhf-gen-dpo  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q5_K_M.gguf) | Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-7B-sft-hhrlhf-gen-dpo-GGUF/resolve/main/Qwen2-7B-sft-hhrlhf-gen-dpo.f16.gguf) | f16 | 15.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/AlphaHitchhiker-7B-i1-GGUF

mradermacher

"2024-12-22T10:28:14Z"

75

1

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "dataset:migtissera/Hitchhiker", "base_model:macadeliccc/AlphaHitchhiker-7B", "base_model:quantized:macadeliccc/AlphaHitchhiker-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2024-12-22T09:51:04Z"

--- base_model: macadeliccc/AlphaHitchhiker-7B datasets: migtissera/Hitchhiker language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About      weighted/imatrix quants of https://huggingface.co/macadeliccc/AlphaHitchhiker-7B  static quants are available at https://huggingface.co/mradermacher/AlphaHitchhiker-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/AlphaHitchhiker-7B-i1-GGUF/resolve/main/AlphaHitchhiker-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl

nota-ai

"2024-07-23T01:03:23Z"

5

0

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "arxiv:2402.02834", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-07-04T07:00:09Z"

# Shortened LLM Model Card Shortened LLM is a depth-pruned version of large language models for efficient text generation. - **Developed by:** [Nota AI](https://www.nota.ai/) - **License:** Non-commercial license - **Repository:** https://github.com/Nota-NetsPresso/shortened-llm - **Paper:** https://arxiv.org/abs/2402.02834 ## Compression Method * After identifying unimportant Transformer blocks, we perform **one-shot pruning**. * In retraining pruned models for quality recovery, we leverage **continued pretraining (CPT)**, which involves updating all parameters, on a large-scale pretraining corpus. * Once CPT is completed, the model in this card is further finetuned with **low-rank adaptation (LoRA)** on an instruction tuning dataset. ## Models from Aggressive Pruning & CPT Retraining (arXiv-v2): | Source<br>Model | Pruning<br>Ratio | Pruning<br>Criterion | Retraining<br>Method | HF Models<br>Link | |:---:|:---:|:---:|:---:| :---:| | Vicuna-v1.3-7B | 20% | PPL | CPT | [nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl) | | Vicuna-v1.3-7B | 45% | PPL | CPT | [nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl) | | Vicuna-v1.3-7B | 60% | PPL | CPT | [nota-ai/cpt_st-vicuna-v1.3-2.7b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-2.7b-ppl) | | Vicuna-v1.3-7B | 80% | PPL | CPT | [nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl) | | Vicuna-v1.3-7B | 20% | PPL | CPT⇒LoRA | [nota-ai/cpt-lora_st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-5.5b-ppl) | | Vicuna-v1.3-7B | 45% | PPL | CPT⇒LoRA | [nota-ai/cpt-lora_st-vicuna-v1.3-3.7b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-3.7b-ppl) | | Vicuna-v1.3-7B | 60% | PPL | CPT⇒LoRA | [nota-ai/cpt-lora_st-vicuna-v1.3-2.7b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-2.7b-ppl) | | Vicuna-v1.3-7B | 80% | PPL | CPT⇒LoRA | [nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl](https://huggingface.co/nota-ai/cpt-lora_st-vicuna-v1.3-1.5b-ppl) | <details> <summary> Click to see the results: </summary> - EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c) <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_results.png" width="100%"> </details> #### Experimental Setup for CPT of Pruned Vicuna-7B * Dataset: [SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B) * Training using 8 NVIDIA H100 GPUs. * 5.5B parameters: 37B training tokens (for 6 days) * 3.7B parameters: 74B tokens (for 8 days) * 2.7B parameters: 150B tokens (for 12 days) * 1.5B parameters: 271B tokens (for 11 days) * AdamW optimizer with (β1, β2)=(0.9, 0.95); a learning rate of 0.0001; a weight decay of 0.1. * Global batch size: 512 (micro-batch size of 2 × 32 gradient accumulation steps × 8 GPUs). <details> <summary> Click to see the learning curve: </summary> **Zero-shot performance over the course of training for models from Vicuna-7B-v1.3 at different pruning ratios.** For each model size, the CPT duration was limited to a two-week period, but additional training could further improve the quality. <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_learning-curve.png" width="100%"> </details> #### Experimental Setup for LoRA Instruction Tuning * Dataset: [Refined Alpaca](https://huggingface.co/datasets/yahma/alpaca-cleaned) * Training using 1 NVIDIA A100 GPU. * The retraining costs are low, with the entire process being executed on a single GPU. * For example, LoRA retraining of a 20%-pruned model from 7B parameters requires about 2 hours and 22GB VRAM. * A LoRA rank of 8; AdamW optimizer with a learning rate of 0.0001. * A batch size of 64 over 2 epochs. ## Models from Moderate Pruning & LoRA Retraining (arXiv-v1): | Source<br>Model | Pruning<br>Ratio | Pruning<br>Criterion | HF Models<br>Link | |:---:|:---:|:---:|:---:| | LLaMA-1-7B | 20% | PPL | [nota-ai/st-llama-1-5.5b-ppl](https://huggingface.co/nota-ai/st-llama-1-5.5b-ppl) | | LLaMA-1-7B | 20% | Taylor+ | [nota-ai/st-llama-1-5.5b-taylor](https://huggingface.co/nota-ai/st-llama-1-5.5b-taylor) | | Vicuna-v1.3-7B | 20% | PPL | [nota-ai/st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-ppl) | | Vicuna-v1.3-7B | 20% | Taylor+ | [nota-ai/st-vicuna-v1.3-5.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-taylor) | | Vicuna-v1.3-13B | 21% | PPL | [nota-ai/st-vicuna-v1.3-10.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-ppl) | | Vicuna-v1.3-13B | 21% | Taylor+ | [nota-ai/st-vicuna-v1.3-10.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-taylor) | <details> <summary> Click to see the results: </summary> - EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c) <img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st-llama_zero-shot_scores.png" width="100%"> </details> ## License - All rights related to this repository and the compressed models are reserved by Nota Inc. - The intended use is strictly limited to research and non-commercial projects. ## Acknowledgments - [Microsoft for Startups Founders Hub](https://www.microsoft.com/en-us/startups) and [Gwangju AICA](http://www.aica-gj.kr/main.php) for generously providing GPU resources. - [LLM-Pruner](https://github.com/horseee/LLM-Pruner), which utilizes [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness), [PEFT](https://github.com/huggingface/peft), and [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). Thanks for the pioneering work on structured pruning of LLMs! - [LLaMA](https://github.com/facebookresearch/llama), [Vicuna](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md), [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), and [Alpaca-Cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). Thanks for the open-source LLMs and data! ## Citation ```bibtex @article{kim2024shortened, title={Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods}, author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu}, journal={arXiv preprint arXiv:2402.02834}, year={2024}, url={https://arxiv.org/abs/2402.02834} } ``` ```bibtex @article{kim2024mefomo, title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models}, author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu}, journal={ICLR Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)}, year={2024}, url={https://openreview.net/forum?id=18VGxuOdpu} } ```

migtissera/Tess-XS-v1.2

migtissera

"2023-11-25T18:15:20Z"

1,474

2

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-11-23T21:49:34Z"

--- license: apache-2.0 --- # Note: This version is experimental and have been depracated. Please use the stable release Tess-XS-v1.3-yarn-128K: https://huggingface.co/migtissera/Tess-XS-v1-3-yarn-128K # Tess ![Tess](https://huggingface.co/migtissera/Tess-M-v1.0/resolve/main/Tess.png) Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-XS-v1.1 was trained on the Mistral-7B base. # Prompt Format: ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ```

asenella/mhd128_JNFDcca_beta_25_scale_True_seed_0

asenella

"2023-08-17T00:23:53Z"

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

"2023-08-17T00:23:33Z"

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1

cleanrl

"2023-02-05T18:24:43Z"

0

cleanrl

[ "cleanrl", "tensorboard", "Kangaroo-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2023-02-05T18:24:42Z"

--- tags: - Kangaroo-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Kangaroo-v5 type: Kangaroo-v5 metrics: - type: mean_reward value: 1800.00 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **PPO** Agent Playing **Kangaroo-v5** This is a trained model of a PPO agent playing Kangaroo-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sebulba_ppo_envpool.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool --env-id Kangaroo-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/sebulba_ppo_envpool.py curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-sebulba_ppo_envpool-seed1/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 --params-queue-timeout 0.02 --track --save-model --upload-model --hf-entity cleanrl --env-id Kangaroo-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 16, 'async_update': 4, 'batch_size': 8192, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Kangaroo-v5', 'exp_name': 'sebulba_ppo_envpool', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 2048, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 64, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6103, 'params_queue_timeout': 0.02, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

mradermacher/L3-78b-Large-v1-i1-GGUF

mradermacher

"2025-03-27T08:54:21Z"

236

0

transformers

[ "transformers", "gguf", "en", "base_model:FINGU-AI/L3-78b-Large-v1", "base_model:quantized:FINGU-AI/L3-78b-Large-v1", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-03-25T22:37:06Z"

--- base_model: FINGU-AI/L3-78b-Large-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/FINGU-AI/L3-78b-Large-v1  static quants are available at https://huggingface.co/mradermacher/L3-78b-Large-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ1_S.gguf) | i1-IQ1_S | 24.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ1_M.gguf) | i1-IQ1_M | 25.5 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 27.4 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 29.1 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_S.gguf) | i1-IQ2_S | 30.0 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ2_M.gguf) | i1-IQ2_M | 31.5 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 31.7 | very low quality | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q2_K.gguf) | i1-Q2_K | 31.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 34.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 35.2 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 36.9 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_S.gguf) | i1-IQ3_S | 37.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ3_M.gguf) | i1-IQ3_M | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 40.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 42.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 42.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_0.gguf) | i1-Q4_0 | 44.4 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 47.0 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_1.gguf) | i1-Q4_1 | 49.1 | | | [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 50.8 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 55.2 | | | [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 58.4 | | | [PART 1](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-78b-Large-v1-i1-GGUF/resolve/main/L3-78b-Large-v1.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 69.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

rhyliieee/LLAMA3-MED-v2.2

rhyliieee

"2024-11-02T21:18:37Z"

75

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "dataset:rhyliieee/notes-completion-set", "base_model:aaditya/Llama3-OpenBioLLM-8B", "base_model:quantized:aaditya/Llama3-OpenBioLLM-8B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

"2024-11-02T20:55:27Z"

--- license: mit datasets: - rhyliieee/notes-completion-set base_model: - aaditya/Llama3-OpenBioLLM-8B pipeline_tag: text-generation library_name: transformers --- Finetuned a pretrained Model with Lora, resize the base model's embeddings, then load Peft Model with the resized base model. """ # add special tokens to the tokenizer and base model before merging peft with base open_tokenizer.add_special_tokens({ "additional_special_tokens": ["<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>"] }) base_model.resize_token_embeddings(len(open_tokenizer)) # reload the peft model with resized token embedding of base model peft_model = PeftModel.from_pretrained(base_model, "rhyliieee/LLaMA3-8Bit-Lora-Med-v2",) # perform merging merged_peft_base_with_special_tokens = peft_model.merge_and_unload() """

Jonjew/PastelGothStyle

Jonjew

"2025-02-09T07:05:05Z"

7

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]

text-to-image

"2025-02-09T07:05:00Z"

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- On a distant, surreal planet, an alien couture collection is displayed in an otherworldly marketplace. The setting features glowing, crystalline structures and bioluminescent flora, creating a fantastical, ethereal environment. Models showcase intricate, alien garments crafted from translucent, luminous materials with delicate, pulsating patterns. The attire blends organic, flowing shapes with futuristic, metallic accents, reflecting a harmonious fusion of natural and advanced aesthetics. The vibrant colors and unique silhouettes highlight the avant-garde nature of extraterrestrial fashion in hud_pstl_gth_styl, output: url: images/MarkuryFLUX_01099_.png - text: >- On a distant, surreal planet, an alien couture collection is displayed in an otherworldly marketplace. The setting features glowing, crystalline structures and bioluminescent flora, creating a fantastical, ethereal environment. Models showcase intricate, alien garments crafted from translucent, luminous materials with delicate, pulsating patterns. The attire blends organic, flowing shapes with futuristic, metallic accents, reflecting a harmonious fusion of natural and advanced aesthetics. The vibrant colors and unique silhouettes highlight the avant-garde nature of extraterrestrial fashion in hud_pstl_gth_styl, output: url: images/MarkuryFLUX_01097_.png - text: >- Energetic Harajuku shopping street filled with cheerful, bubbly fashionistas showcasing their unique, eclectic styles. The street is lined with quirky, pastel-colored shops and stalls offering an array of colorful, whimsical clothing and accessories. The shoppers are dressed in charming, layered outfits with playful patterns, oversized bows, and bright, cheerful colors. The scene includes colorful murals, twinkling fairy lights, and festive decorations, creating a lively, vibrant atmosphere of playful fashion and youthful exuberancein hud_pstl_gth_styl, pastel, goth, psychedelic output: url: images/MarkuryFLUX_01113_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: unknown --- # Pastel Goth Style FLUX <Gallery /> ## Model description FROM: https://civitai.com/models/682692/pastel-goth-style-flux Triggers: hud_pstl_gth_styl, pastel, gothic, dark, fantasy ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/PastelGothStyle/tree/main) them in the Files & versions tab.

AdapterHub/bert-base-multilingual-cased-sw-wiki_pfeiffer

AdapterHub

"2024-05-05T21:05:01Z"

3

0

adapter-transformers

[ "adapter-transformers", "bert", "adapterhub:sw/wiki", "sw", "arxiv:2005.00052", "license:apache-2.0", "region:us" ]

null

"2024-05-05T21:04:58Z"

--- tags: - bert - adapter-transformers - adapterhub:sw/wiki language: - sw license: "apache-2.0" --- # Adapter `bert-base-multilingual-cased-sw-wiki_pfeiffer` for bert-base-multilingual-cased Pfeiffer Adapter trained with Masked Language Modelling on Swahili Wikipedia Articles for 100k steps and a batch size of 64. **This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.** ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-multilingual-cased") adapter_name = model.load_adapter("AdapterHub/bert-base-multilingual-cased-sw-wiki_pfeiffer") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: pfeiffer - Prediction head: None - Dataset: [sw/wiki](https://adapterhub.ml/explore/sw/wiki/) ## Author Information - Author name(s): Jonas Pfeiffer - Author email: [email protected] - Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/jopfeiff), [Twitter](https://twitter.com/@PfeiffJo) ## Versions - `nd` **(main)** - `wd` ## Citation ```bibtex @article{pfeiffer20madx, title={{MAD-X}: An {A}dapter-based {F}ramework for {M}ulti-task {C}ross-lingual {T}ransfer}, author={Pfeiffer, Jonas and Vuli\'{c}, Ivan and Gurevych, Iryna and Ruder, Sebastian}, journal={arXiv preprint}, year={2020}, url={https://arxiv.org/pdf/2005.00052.pdf}, } ``` *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-multilingual-cased-sw-wiki_pfeiffer.yaml*.

Bronsn/llama3.2-1B-translatev1

Bronsn

"2024-09-29T21:30:18Z"

57

0

transformers

[ "transformers", "safetensors", "gguf", "llama", "unsloth", "trl", "sft", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-09-29T18:28:57Z"

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

memeviss/IQ_1

memeviss

"2025-04-17T18:31:31Z"

0

null

[ "safetensors", "region:us" ]

null

"2025-04-17T18:26:30Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

Narkantak/mistral-7b-Intent-Classifier-Ashu

Narkantak

"2024-04-02T09:11:49Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-02T09:11:35Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

marisabatalla/autotrain-4lx4k-qa52n

marisabatalla

"2024-02-29T18:27:03Z"

192

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:autotrain-4lx4k-qa52n/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-02-29T18:26:46Z"

--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - autotrain-4lx4k-qa52n/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 0.09857235848903656 f1: 1.0 precision: 1.0 recall: 1.0 auc: 1.0 accuracy: 1.0

kaierlong/gemma-chinese

kaierlong

"2024-04-15T07:45:17Z"

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]

null

"2024-04-15T03:47:59Z"

--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: gemma-chinese results: [] ---  # gemma-chinese This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

hlumin/speecht5_finetuned_voxpopuli_nl

hlumin

"2023-08-30T23:31:37Z"

82

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "lt", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

"2023-08-30T23:25:39Z"

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] language: - lt pipeline_tag: text-to-speech ---  # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.6484 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.52 | 5 | 0.6706 | | No log | 1.04 | 10 | 0.6484 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

CocoRoF/KoModernBERT-large-mlm-v17

CocoRoF

"2025-03-24T04:45:39Z"

0

transformers

[ "transformers", "safetensors", "modernbert", "fill-mask", "generated_from_trainer", "base_model:CocoRoF/KoModernBERT-large-mlm-v16", "base_model:finetune:CocoRoF/KoModernBERT-large-mlm-v16", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2025-03-23T10:01:20Z"

--- library_name: transformers license: apache-2.0 base_model: CocoRoF/KoModernBERT-large-mlm-v16 tags: - generated_from_trainer model-index: - name: KoModernBERT-large-mlm-v17 results: [] ---  # KoModernBERT-large-mlm-v17 This model is a fine-tuned version of [CocoRoF/KoModernBERT-large-mlm-v16](https://huggingface.co/CocoRoF/KoModernBERT-large-mlm-v16) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 32 - total_train_batch_size: 1024 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 90.5064 | 0.1156 | 500 | nan | | 86.5566 | 0.2312 | 1000 | nan | | 83.8022 | 0.3468 | 1500 | nan | | 83.7241 | 0.4624 | 2000 | nan | | 82.7234 | 0.5779 | 2500 | nan | | 79.0157 | 0.6935 | 3000 | nan | | 77.4688 | 0.8091 | 3500 | nan | | 74.9247 | 0.9247 | 4000 | nan | ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.1

sd-concepts-library/singsing-doll

sd-concepts-library

"2022-09-19T16:14:12Z"

0

null

[ "license:mit", "region:us" ]

null

"2022-09-19T16:14:06Z"

--- license: mit --- ### Singsing doll on Stable Diffusion This is the `<singsing>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<singsing> 0](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/3.jpeg) ![<singsing> 1](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/0.jpeg) ![<singsing> 2](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/1.jpeg) ![<singsing> 3](https://huggingface.co/sd-concepts-library/singsing-doll/resolve/main/concept_images/2.jpeg)

memorysaver/q-FrozenLake-v1-4x4-noSlippery

memorysaver

"2022-05-29T11:08:08Z"

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2022-05-29T11:07:58Z"

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

devmoon732/mayen

devmoon732

"2025-04-06T10:03:28Z"

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-04-06T09:32:47Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: mayen --- # Mayen <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mayen` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mayen", "lora_weights": "https://huggingface.co/devmoon732/mayen/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('devmoon732/mayen', weight_name='lora.safetensors') image = pipeline('mayen').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/devmoon732/mayen/discussions) to add images that show off what you’ve made with this LoRA.

Grayx/fiufiu_476

Grayx

"2025-01-16T22:21:18Z"

44

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-16T22:18:10Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

isspek/bert-base-cased_monkeypox_top3_2_2e-5_16_undersampling_0.4

isspek

"2025-03-23T13:27:43Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-03-23T13:27:28Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Ekhlass/phi2-flutter-questions

Ekhlass

"2024-04-08T08:38:47Z"

48

0

transformers

[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-04-08T08:36:06Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

e1113633/roomifai_sd15_ft_diningroom

e1113633

"2023-11-02T10:03:30Z"

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2023-11-02T09:36:37Z"

# Roomifai Dining Room Stable Diffusion v1-5 Fine Tuning Model Card This model is a fine tuned model from stable diffusion 1.5 for our school project, it is capable of generating dining room design given specific dining room promopts. # Uses You have to install all the dependencies, python 3.10.x There is no GUI for testing the mode. the test.py script is used for testing the model, the command is as such: > python test.py <model> <output folder> <prompts file> <prefix for output file> for e.g > python test.py "./unet/diffusion_pytorch_model.safetensors" "output" "dingingroom_prompt.txt" "test1"

The-Masters-Golf-Reddit/LIVE

The-Masters-Golf-Reddit

"2025-04-13T19:02:49Z"

0

null

[ "region:us" ]

null

"2025-04-13T18:56:19Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

xshubhamx/InLegal-legal-merge-ties-d-0-InLegal-w-1

xshubhamx

"2024-04-20T17:16:32Z"

107

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "merge", "mergekit", "lazymergekit", "xshubhamx/InLegalBERT", "xshubhamx/legal-bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-04-20T17:14:08Z"

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - xshubhamx/InLegalBERT - xshubhamx/legal-bert-base-uncased --- ## Metrics - loss: 0.9470 - accuracy: 0.8366 - precision: 0.8360 - recall: 0.8366 - precision_macro: 0.8141 - recall_macro: 0.7899 - macro_fpr: 0.0143 - weighted_fpr: 0.0138 - weighted_specificity: 0.9781 - macro_specificity: 0.9876 - weighted_sensitivity: 0.8366 - macro_sensitivity: 0.7899 - f1_micro: 0.8366 - f1_macro: 0.7978 - f1_weighted: 0.8350 - runtime: 21.6449 - samples_per_second: 59.6450 - steps_per_second: 7.4840 # InLegal-legal-merge-ties-d-0-InLegal-w-1 InLegal-legal-merge-ties-d-0-InLegal-w-1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [xshubhamx/InLegalBERT](https://huggingface.co/xshubhamx/InLegalBERT) * [xshubhamx/legal-bert-base-uncased](https://huggingface.co/xshubhamx/legal-bert-base-uncased) ## 🧩 Configuration ```yaml models: - model: xshubhamx/InLegalBERT parameters: density: 0.53 weight: 0 - model: xshubhamx/legal-bert-base-uncased parameters: density: 0.53 weight: 1 merge_method: ties base_model: xshubhamx/InLegalBERT parameters: normalize: false int8_mask: true dtype: float16 ```

weijie210/zephyr-7b-UFB-0

weijie210

"2024-02-07T03:49:39Z"

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-02-07T01:25:02Z"

--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - trl - dpo - generated_from_trainer model-index: - name: zephyr-7b-UFB-0 results: [] ---  # zephyr-7b-UFB-0 This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1492 - Rewards/chosen: -1.5452 - Rewards/rejected: -7.2115 - Rewards/accuracies: 0.8359 - Rewards/margins: 5.6663 - Logps/rejected: -171.0846 - Logps/chosen: -143.6666 - Logits/rejected: -2.3237 - Logits/chosen: -2.3692 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0

AdapterHub/xmod-base-be_BY

AdapterHub

"2023-08-31T12:59:06Z"

4

0

adapter-transformers

[ "adapter-transformers", "xmod", "adapterhub:be/cc100", "be", "license:mit", "region:us" ]

null

"2023-08-31T12:47:02Z"

--- tags: - adapter-transformers - xmod - adapterhub:be/cc100 language: - be license: "mit" --- # Adapter `AdapterHub/xmod-base-be_BY` for AdapterHub/xmod-base An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [be/cc100](https://adapterhub.ml/explore/be/cc100/) dataset. This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base") adapter_name = model.load_adapter("AdapterHub/xmod-base-be_BY", source="hf", set_active=True) ``` ## Architecture & Training This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library. For more information on architecture and training, please refer to the original model card. ## Evaluation results  ## Citation [Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) ``` @inproceedings{pfeiffer-etal-2022-lifting, title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers", author = "Pfeiffer, Jonas and Goyal, Naman and Lin, Xi and Li, Xian and Cross, James and Riedel, Sebastian and Artetxe, Mikel", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.255", doi = "10.18653/v1/2022.naacl-main.255", pages = "3479--3495" } ```

dar5654/segformer-b0-scene-parse-150-MASKED

dar5654

"2023-04-29T20:03:23Z"

31

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

null

"2023-04-29T15:34:13Z"

--- license: other tags: - generated_from_trainer model-index: - name: segformer-b0-scene-parse-150-MASKED results: [] ---  # segformer-b0-scene-parse-150-MASKED This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1526 - Mean Iou: 0.0217 - Mean Accuracy: 0.0580 - Overall Accuracy: 0.2746 - Per Category Iou: [0.2638779780993535, 0.24032657224553952, 0.28498201974847515, 0.1075812162299665, 0.14745268628426467, 0.048342869965219346, 0.0, 0.007290688724806103, 0.04780558672261605, 0.06559620777139805, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] - Per Category Accuracy: [0.5551073389128427, 0.47540841261768607, 0.4280130098767642, 0.6449145007547091, 0.4263212952616438, 0.051559171951657295, 0.0, 0.008099600657740192, 0.06573971674217831, 0.0695452132365953, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 4.7792 | 1.0 | 20 | 4.7294 | 0.0082 | 0.0454 | 0.1893 | [0.2263585397369742, 0.13770136142176356, 0.08295638586455376, 0.08510788870213735, 0.12573291455024074, 0.02435003944847278, 0.0, 0.004065480375718896, 0.0017733053903393038, 0.09547671544063606, 0.0, 0.0, 0.00046794942973620344, 0.0, 0.0, 0.0, 0.0, 0.0003653809493550232, 0.0, 0.0, nan, 0.0, 0.0, 0.008303859757035214, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0] | [0.4716825785763388, 0.2136232639104242, 0.09227762360874885, 0.6465273039306908, 0.5643826822947624, 0.024817518248175182, 0.0, 0.0042377260981912145, 0.0018640077057543434, 0.10115023889577066, 0.0, 0.0, 0.0004903142166191589, nan, 0.0, 0.0, nan, 0.001218026796589525, 0.0, 0.0, nan, nan, 0.0, 0.010582425335110135, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 4.6816 | 2.0 | 40 | 4.3777 | 0.0172 | 0.0508 | 0.2436 | [0.2348784161662965, 0.1780159659740713, 0.1725553209314372, 0.11519214696920146, 0.1519642591474354, 0.05501165920088421, 0.008008356545961003, 0.003268125562869637, 0.06320147075839194, 0.033278833708018256, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.49203944919252063, 0.5863196761642498, 0.24353236057649272, 0.45883216508487895, 0.4128408739687597, 0.05860476247457221, 0.010855884203901826, 0.0033074935400516795, 0.08768863044486462, 0.03457795080516723, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 4.3571 | 3.0 | 60 | 4.2442 | 0.0166 | 0.0524 | 0.2571 | [0.25585106151712383, 0.22004710007836228, 0.22139639459642338, 0.10209920082512318, 0.1575995748489595, 0.017118189937481394, 0.0, 0.007236489870641267, 0.03938333712881877, 0.008957958671236131, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.5795454321624768, 0.44004779802440347, 0.3099841852415481, 0.6467961044600211, 0.40188060198283443, 0.017613976307287303, 0.0, 0.00787408973455485, 0.05089900467339731, 0.009343479030260131, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 3.9658 | 4.0 | 80 | 4.1981 | 0.0207 | 0.0555 | 0.2731 | [0.26863872743436906, 0.26573623577278954, 0.2321627542307547, 0.10446031518997217, 0.16009038296656186, 0.046391399460182774, 0.0, 0.004261499526016889, 0.04043589899112432, 0.01742889012827663, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.6207791747342379, 0.47583015989183425, 0.2976531495240653, 0.6644438930587433, 0.4261667578041416, 0.04862031829603925, 0.0, 0.0046041813483673946, 0.05351218294031608, 0.01769598301185631, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 4.0603 | 5.0 | 100 | 4.1526 | 0.0217 | 0.0580 | 0.2746 | [0.2638779780993535, 0.24032657224553952, 0.28498201974847515, 0.1075812162299665, 0.14745268628426467, 0.048342869965219346, 0.0, 0.007290688724806103, 0.04780558672261605, 0.06559620777139805, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.5551073389128427, 0.47540841261768607, 0.4280130098767642, 0.6449145007547091, 0.4263212952616438, 0.051559171951657295, 0.0, 0.008099600657740192, 0.06573971674217831, 0.0695452132365953, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

ctoxz/GPU

ctoxz

"2025-04-18T01:02:17Z"

0

null

[ "region:us" ]

null

"2025-04-18T01:02:07Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

albertus-sussex/veriscrape-fixed-simcse-nbaplayer-reference_1_to_verify_9-fold-8

albertus-sussex

"2025-04-04T14:29:49Z"

0

transformers

[ "transformers", "safetensors", "roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

"2025-04-04T14:29:21Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

Jadsalameh31/finetuning-sentiment-model-3000-samples

Jadsalameh31

"2023-03-06T18:49:57Z"

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-03-06T18:46:13Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7012 - eval_accuracy: 0.4933 - eval_f1: 0.6607 - eval_runtime: 7.7289 - eval_samples_per_second: 38.816 - eval_steps_per_second: 2.458 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

klemiec/unit0

klemiec

"2023-03-02T19:48:34Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-03-02T19:43:25Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.18 +/- 18.94 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

second-state/phi-4-GGUF

second-state

"2025-01-11T13:58:41Z"

669

0

transformers

[ "transformers", "gguf", "phi3", "text-generation", "custom_code", "en", "base_model:microsoft/phi-4", "base_model:quantized:microsoft/phi-4", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-01-09T02:17:27Z"

--- base_model: microsoft/phi-4 license: mit license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers model_creator: Microsoft model_name: phi-4 quantized_by: Second State Inc. ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Phi-4-GGUF ## Original Model [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) ## Run with LlamaEdge - LlamaEdge version: [v0.16.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.16.0) and above - Prompt template - Prompt type: `phi-4-chat` - Prompt string ```text <|im_start|>system<|im_sep|> {system_message}<|im_end|> <|im_start|>user<|im_sep|> {user_message}<|im_end|> <|im_start|>assistant<|im_sep|> ``` - Context size: `16000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:phi-4-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template phi-4-chat \ --ctx-size 16000 \ --model-name phi-3-mini ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:phi-4-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template phi-4-chat \ --ctx-size 16000 ``` ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [phi-4-Q2_K.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q2_K.gguf) | Q2_K | 2 | 5.55 GB| smallest, significant quality loss - not recommended for most purposes | | [phi-4-Q3_K_L.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_L.gguf) | Q3_K_L | 3 | 7.93 GB| small, substantial quality loss | | [phi-4-Q3_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_M.gguf) | Q3_K_M | 3 | 7.36 GB| very small, high quality loss | | [phi-4-Q3_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q3_K_S.gguf) | Q3_K_S | 3 | 6.50 GB| very small, high quality loss | | [phi-4-Q4_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_0.gguf) | Q4_0 | 4 | 8.38 GB| legacy; small, very high quality loss - prefer using Q3_K_M | | [phi-4-Q4_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_K_M.gguf) | Q4_K_M | 4 | 9.05 GB| medium, balanced quality - recommended | | [phi-4-Q4_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q4_K_S.gguf) | Q4_K_S | 4 | 8.44 GB| small, greater quality loss | | [phi-4-Q5_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_0.gguf) | Q5_0 | 5 | 10.2 GB| legacy; medium, balanced quality - prefer using Q4_K_M | | [phi-4-Q5_K_M.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_K_M.gguf) | Q5_K_M | 5 | 10.6 GB| large, very low quality loss - recommended | | [phi-4-Q5_K_S.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q5_K_S.gguf) | Q5_K_S | 5 | 10.2 GB| large, low quality loss - recommended | | [phi-4-Q6_K.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q6_K.gguf) | Q6_K | 6 | 12.0 GB| very large, extremely low quality loss | | [phi-4-Q8_0.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-Q8_0.gguf) | Q8_0 | 8 | 15.6 GB| very large, extremely low quality loss - not recommended | | [phi-4-f16.gguf](https://huggingface.co/second-state/phi-4-GGUF/blob/main/phi-4-f16.gguf) | f16 | 16 | 29.3 GB| | *Quantized with llama.cpp b4450.*

BEASTBOYJAY/my-fine-tuned-summarizer

BEASTBOYJAY

"2024-11-16T05:53:44Z"

103

0

transformers

[ "transformers", "safetensors", "encoder-decoder", "text2text-generation", "en", "dataset:ccdv/cnn_dailymail", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-11-16T05:36:03Z"

--- library_name: transformers datasets: - ccdv/cnn_dailymail language: - en base_model: - google-bert/bert-base-uncased --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model is used for making or generating summary of the provided paragraph. - **Developed by:** BEASTBOYJAY - **Model type:** Transformer(encoder) - **Language(s) (NLP):** English - **Finetuned from model:** Bert-base-uncased ## Uses - For the summarization purpose only ## Bias, Risks, and Limitations This model is fine-tuned on very small dataset can need more fine-tuning for better results.(Fine-tuned this model only for eductional purposes) ## How to Get Started with the Model Use the code below to get started with the model. ``` from transformers import EncoderDecoderModel, BertTokenizer class TextSummarizer: def __init__(self, model_path, tokenizer_name="bert-base-uncased"): self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name) self.model = EncoderDecoderModel.from_pretrained(model_path) def summarize(self, text, max_input_length=512): inputs = self.tokenizer( text, return_tensors="pt", truncation=True, padding="max_length", max_length=max_input_length, ) summary_ids = self.model.generate( inputs["input_ids"], attention_mask=inputs["attention_mask"], decoder_start_token_id=self.tokenizer.cls_token_id, max_length=128, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=1, early_stopping=True, ) summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary if __name__ == "__main__": summarizer = TextSummarizer(model_path="BEASTBOYJAY/my-fine-tuned-summarizer") test_article = "Your article or paragraph" summary = summarizer.summarize(test_article) print("Generated Summary:", summary) ```

Ppoyaa/L3-Inca-8B-v0.8

Ppoyaa

"2024-06-25T03:16:46Z"

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Nitral-AI/Hathor_Stable-v0.2-L3-8B", "base_model:merge:Nitral-AI/Hathor_Stable-v0.2-L3-8B", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:grimjim/Llama-3-Luminurse-v0.2-OAS-8B", "base_model:merge:grimjim/Llama-3-Luminurse-v0.2-OAS-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-22T15:15:22Z"

--- base_model: - NurtureAI/Meta-Llama-3-8B-Instruct-32k - Nitral-AI/Hathor-L3-8B-v.02 - grimjim/Llama-3-Luminurse-v0.2-OAS-8B - Sao10K/L3-8B-Stheno-v3.2 library_name: transformers tags: - mergekit - merge --- ![1718719796324.png](https://cdn-uploads.huggingface.co/production/uploads/65f158693196560d34495d54/6SuBtoTMP5Svgl0fmT9vt.png) *** ### L3-Inca-8B-v0.8 [L3-Inca-8B-v0.8](https://huggingface.co/Ppoyaa/L3-Inca-8B-v0.8) is a merge of the following models: * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [Nitral-AI/Hathor-L3-8B-v.02](https://huggingface.co/Nitral-AI/Hathor-L3-8B-v.02) * [grimjim/Llama-3-Luminurse-v0.2-OAS-8B](https://huggingface.co/grimjim/Llama-3-Luminurse-v0.2-OAS-8B) using [NurtureAI/Meta-Llama-3-8B-Instruct-32k](https://huggingface.co/NurtureAI/Meta-Llama-3-8B-Instruct-32k) as the base. >[!IMPORTANT] > UPDATE: > Changed the merging method from **model_stock** to **ties** and made Stheno have the most weight and density. *** ### Quantized Models by [mradermacher](https://huggingface.co/mradermacher) • Static [L3-Inca-8B-v0.8-GGUF](https://huggingface.co/mradermacher/L3-Inca-8B-v0.8-GGUF) • Imatrix [L3-Inca-8B-v0.8-i1-GGUF](https://huggingface.co/mradermacher/L3-Inca-8B-v0.8-i1-GGUF) *** ### Configuration ```yaml models: - model: Sao10K/L3-8B-Stheno-v3.2 parameters: density: 0.85 weight: 0.5 - model: Nitral-AI/Hathor-L3-8B-v.02 parameters: density: 0.75 weight: 0.3 - model: grimjim/Llama-3-Luminurse-v0.2-OAS-8B parameters: density: 0.75 weight: 0.2 merge_method: ties base_model: NurtureAI/Meta-Llama-3-8B-Instruct-32k parameters: normalize: false int8_mask: true dtype: bfloat16 ```

abhijeet007/t5-Large_FineTunned

abhijeet007

"2024-03-27T07:57:09Z"

63

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-03-27T07:55:48Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

apparaomulpuri/alpaca-custom-model

apparaomulpuri

"2023-06-26T13:54:37Z"

5

0

peft

[ "peft", "region:us" ]

null

"2023-06-26T10:53:07Z"

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

isspek/roberta-base_covid_chatgpt_3_2e-5_16_undersampling_0.2

isspek

"2025-01-01T16:20:24Z"

200

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-12-28T23:28:28Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jysssacc/bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01

jysssacc

"2024-01-13T22:30:46Z"

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:bigscience/bloomz-1b1", "base_model:adapter:bigscience/bloomz-1b1", "license:bigscience-bloom-rail-1.0", "region:us" ]

null

"2024-01-13T19:41:57Z"

--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: bigscience/bloomz-1b1 model-index: - name: bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01 results: [] ---  # bloomz-1b1_adalora_627_lr5e-05_bs4_epoch5_wd0.01 This model is a fine-tuned version of [bigscience/bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.0291 | 1.0 | 157 | 4.5495 | | 4.582 | 2.0 | 314 | 4.0083 | | 4.1412 | 3.0 | 471 | 3.5810 | | 3.6919 | 4.0 | 628 | 3.3076 | | 3.5125 | 5.0 | 785 | 3.2440 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0

yesj1234/xlsr_cycle0_ko

yesj1234

"2023-09-07T04:20:40Z"

77

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "./sample_speech.py", "generated_from_trainer", "dataset:sample_speech", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2023-09-07T04:16:24Z"

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - automatic-speech-recognition - ./sample_speech.py - generated_from_trainer datasets: - sample_speech model-index: - name: ko-xlsr results: [] ---  # ko-xlsr This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./SAMPLE_SPEECH.PY - NA dataset. It achieves the following results on the evaluation set: - Loss: 1.5649 - Cer: 0.3569 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.5849 | 22.22 | 1000 | 2.5846 | 0.5985 | | 0.7224 | 44.44 | 2000 | 1.5880 | 0.3664 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

mradermacher/Pathfinder-RP-12B-RU-i1-GGUF

mradermacher

"2025-01-30T09:16:40Z"

874

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Aleteian/Pathfinder-RP-12B-RU", "base_model:quantized:Aleteian/Pathfinder-RP-12B-RU", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-01-30T04:31:32Z"

--- base_model: Aleteian/Pathfinder-RP-12B-RU language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/Aleteian/Pathfinder-RP-12B-RU  static quants are available at https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Pathfinder-RP-12B-RU-i1-GGUF/resolve/main/Pathfinder-RP-12B-RU.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

MinaMila/gemma2_GermanCredit_cfda_14ep_42

MinaMila

"2025-03-19T01:12:22Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:unsloth/gemma-2-9b", "base_model:finetune:unsloth/gemma-2-9b", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-03-19T01:12:12Z"

--- base_model: unsloth/gemma-2-9b tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** MinaMila - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2-9b This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

schoonhovenra/20240502

schoonhovenra

"2024-05-06T00:21:15Z"

191

0

transformers

[ "transformers", "safetensors", "detr", "object-detection", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

"2024-05-06T00:21:06Z"

--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: '20240502' results: [] ---  # 20240502 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1

mradermacher/ThoughtStream-4B-v0.2-GGUF

mradermacher

"2025-03-31T14:32:33Z"

0

transformers

[ "transformers", "gguf", "en", "dataset:SkunkworksAI/reasoning-0.01", "dataset:trollek/ThoughtfulAssistant-v01", "dataset:trollek/ThoughtfulAssistant-v02", "base_model:trollek/ThoughtStream-4B-v0.2", "base_model:quantized:trollek/ThoughtStream-4B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-31T14:18:32Z"

--- base_model: trollek/ThoughtStream-4B-v0.2 datasets: - SkunkworksAI/reasoning-0.01 - trollek/ThoughtfulAssistant-v01 - trollek/ThoughtfulAssistant-v02 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/trollek/ThoughtStream-4B-v0.2  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q2_K.gguf) | Q2_K | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_S.gguf) | Q3_K_S | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_M.gguf) | Q3_K_M | 2.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q3_K_L.gguf) | Q3_K_L | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.IQ4_XS.gguf) | IQ4_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q4_K_S.gguf) | Q4_K_S | 2.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q4_K_M.gguf) | Q4_K_M | 2.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q5_K_S.gguf) | Q5_K_S | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q5_K_M.gguf) | Q5_K_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q6_K.gguf) | Q6_K | 3.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.Q8_0.gguf) | Q8_0 | 4.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/ThoughtStream-4B-v0.2-GGUF/resolve/main/ThoughtStream-4B-v0.2.f16.gguf) | f16 | 8.0 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

huggingtweets/coronavid19

huggingtweets

"2021-05-21T23:30:27Z"

5

0

transformers

[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2022-03-02T23:29:05Z"

--- language: en thumbnail: https://www.huggingtweets.com/coronavid19/1608807621950/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1232060545626497024/ltc63x4__400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Coronavirus 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@coronavid19 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@coronavid19's tweets](https://twitter.com/coronavid19). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>1618</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>12</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>96</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1510</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lgjd18p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @coronavid19's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ki9s94y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ki9s94y/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/coronavid19'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

wenge-research/yayi-7b-llama2

wenge-research

"2023-09-13T02:25:50Z"

1,507

10

transformers

[ "transformers", "pytorch", "llama", "text-generation", "yayi", "zh", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-07-21T10:10:18Z"

--- language: - zh - en pipeline_tag: text-generation tags: - yayi --- # 雅意大模型 ## 介绍 [雅意大模型](https://www.wenge.com/yayi/index.html)在百万级人工构造的高质量领域数据上进行指令微调得到，训练数据覆盖媒体宣传、舆情分析、公共安全、金融风控、城市治理等五大领域，上百种自然语言指令任务。雅意大模型从预训练初始化权重到领域模型的迭代过程中，我们逐步增强了它的中文基础能力和领域分析能力，并增加了多轮对话和部分插件能力。同时，经过数百名用户内测过程中持续不断的人工反馈优化，我们进一步提升了模型性能和安全性。通过雅意大模型的开源为促进中文预训练大模型开源社区的发展，贡献自己的一份力量，通过开源，与每一位合作伙伴共建雅意大模型生态。 *News: 🔥 雅意大模型已开源基于 LLaMA 2 的中文优化模型版本，探索适用于中文多领域任务的最新实践。* ## 模型地址 | 模型名称 | 🤗HF模型标识 | 下载地址 | | --------- | --------- | --------- | | YaYi-7B | wenge-research/yayi-7b | [模型下载](https://huggingface.co/wenge-research/yayi-7b) | | YaYi-7B-Llama2 | wenge-research/yayi-7b-llama2 | [模型下载](https://huggingface.co/wenge-research/yayi-7b-llama2) | | YaYi-13B-Llama2 | wenge-research/yayi-13b-llama2 | [模型下载](https://huggingface.co/wenge-research/yayi-13b-llama2) | | YaYi-70B-Llama2 | wenge-research/yayi-70b-llama2 | [模型下载](https://huggingface.co/wenge-research/yayi-70b-llama2) | 详情请参考我们的 [💻Github Repo](https://github.com/wenge-research/YaYi)。 ## 运行方式 ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig from transformers import StoppingCriteria, StoppingCriteriaList pretrained_model_name_or_path = "wenge-research/yayi-7b-llama2" tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path) model = LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=False) # Define the stopping criteria class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords_ids:list): self.keywords = keywords_ids def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: if input_ids[0][-1] in self.keywords: return True return False stop_words = ["<|End|>", "<|YaYi|>", "<|Human|>", "</s>"] stop_ids = [tokenizer.encode(w)[-1] for w in stop_words] stop_criteria = KeywordsStoppingCriteria(stop_ids) # inference prompt = "你是谁？" formatted_prompt = f"""<|System|>: You are a helpful, respectful and honest assistant named YaYi developed by Beijing Wenge Technology Co.,Ltd. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <|Human|>: {prompt} <|YaYi|>: """ inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) eos_token_id = tokenizer("<|End|>").input_ids[0] generation_config = GenerationConfig( eos_token_id=eos_token_id, pad_token_id=eos_token_id, do_sample=True, max_new_tokens=256, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=0 ) response = model.generate(**inputs, generation_config=generation_config, stopping_criteria=StoppingCriteriaList([stop_criteria])) response = [response[0][len(inputs.input_ids[0]):]] response_str = tokenizer.batch_decode(response, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] print(response_str) ``` --- # YaYi ## Introduction [YaYi](https://www.wenge.com/yayi/index.html) was fine-tuned on millions of artificially constructed high-quality domain data. This training data covers five key domains: media publicity, public opinion analysis, public safety, financial risk control, and urban governance, encompassing over a hundred natural language instruction tasks. Throughout the iterative development process of the YaYi, starting from pre-training initialization weights and progressing to domain-specific model, we have steadily enhanced its foundational Chinese language capabilities and domain analysis capabilities. We've also introduced multi-turn conversation enhancements and integrated various plug-in capabilities. Furthermore, through continuous manual feedback and optimization from hundreds of users during the internal testing phase, we've meticulously refined the model's performance and security. By open-sourcing the YaYi model, we will contribute our own efforts to the development of the Chinese pre-trained large language model open-source community. Through this open-source initiative, we seek to collaborate with every partner to build the YaYi model ecosystem together. *News: 🔥 YaYi has open sourced the Chinese optimization model version based on LLaMA 2 to explore the latest practices suitable for Chinese multi-domain tasks.* ## Model download | Model | 🤗HF Model Name | Download Links | | --------- | --------- | --------- | | YaYi-7B | wenge-research/yayi-7b | [Download](https://huggingface.co/wenge-research/yayi-7b) | | YaYi-7B-Llama2 | wenge-research/yayi-7b-llama2 | [Download](https://huggingface.co/wenge-research/yayi-7b-llama2) | | YaYi-13B-Llama2 | wenge-research/yayi-13b-llama2 | [Download](https://huggingface.co/wenge-research/yayi-13b-llama2) | | YaYi-70B-Llama2 | wenge-research/yayi-70b-llama2 | [Download](https://huggingface.co/wenge-research/yayi-70b-llama2) | For more details, please refer to our [💻Github Repo](https://github.com/wenge-research/YaYi)。 ## Run ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig from transformers import StoppingCriteria, StoppingCriteriaList pretrained_model_name_or_path = "wenge-research/yayi-7b-llama2" tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path) model = LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=False) # Define the stopping criteria class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords_ids:list): self.keywords = keywords_ids def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: if input_ids[0][-1] in self.keywords: return True return False stop_words = ["<|End|>", "<|YaYi|>", "<|Human|>", "</s>"] stop_ids = [tokenizer.encode(w)[-1] for w in stop_words] stop_criteria = KeywordsStoppingCriteria(stop_ids) # inference prompt = "你是谁？" formatted_prompt = f"""<|System|>: You are a helpful, respectful and honest assistant named YaYi developed by Beijing Wenge Technology Co.,Ltd. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <|Human|>: {prompt} <|YaYi|>: """ inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) eos_token_id = tokenizer("<|End|>").input_ids[0] generation_config = GenerationConfig( eos_token_id=eos_token_id, pad_token_id=eos_token_id, do_sample=True, max_new_tokens=256, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=0 ) response = model.generate(**inputs, generation_config=generation_config, stopping_criteria=StoppingCriteriaList([stop_criteria])) response = [response[0][len(inputs.input_ids[0]):]] response_str = tokenizer.batch_decode(response, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] print(response_str) ```

baddii/23_baddii_20_06

baddii

"2025-02-09T06:34:01Z"

14

0

transformers

[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-02-09T06:32:04Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Beaflm/dlr_unit1_LunarLander

Beaflm

"2023-11-28T23:42:30Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-11-28T23:41:26Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.63 +/- 26.94 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

NewEden/adv-ckpt-480

NewEden

"2025-03-30T12:58:45Z"

0

null

[ "region:us" ]

null

"2025-03-30T12:58:45Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

huggingtweets/elsasingular-michellexotter-nyxxx696

huggingtweets

"2023-03-11T22:30:48Z"

115

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-03-11T22:29:29Z"

--- language: en thumbnail: http://www.huggingtweets.com/elsasingular-michellexotter-nyxxx696/1678573843308/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1599666828602740737/xkaxWudG_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1616081980856340480/AVkhh3Fo_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1592255638394077184/ugsW8sO4_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">❄️Elsa🏳️‍⚧️ & Nyx 🇧🇷🌸🏳️‍⚧️ & Michelle Otter 🏳️‍⚧️🦦</div> <div style="text-align: center; font-size: 14px;">@elsasingular-michellexotter-nyxxx696</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ❄️Elsa🏳️‍⚧️ & Nyx 🇧🇷🌸🏳️‍⚧️ & Michelle Otter 🏳️‍⚧️🦦. | Data | ❄️Elsa🏳️‍⚧️ | Nyx 🇧🇷🌸🏳️‍⚧️ | Michelle Otter 🏳️‍⚧️🦦 | | --- | --- | --- | --- | | Tweets downloaded | 3226 | 1504 | 3194 | | Retweets | 68 | 24 | 37 | | Short tweets | 1246 | 524 | 702 | | Tweets kept | 1912 | 956 | 2455 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/or027l6u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elsasingular-michellexotter-nyxxx696's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elsasingular-michellexotter-nyxxx696') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2

tyzhu

"2024-06-06T11:50:46Z"

5

0

peft

[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]

null

"2024-06-04T14:01:33Z"

--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - name: Accuracy type: accuracy value: 0.5108253968253968 library_name: peft ---  # lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: - Loss: 3.6298 - Accuracy: 0.5108 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-------:|:-----:|:--------:|:---------------:| | 1.7619 | 0.9998 | 1089 | 0.5172 | 2.2994 | | 1.648 | 1.9995 | 2178 | 0.5210 | 2.2683 | | 1.4941 | 2.9993 | 3267 | 0.5214 | 2.3185 | | 1.3627 | 4.0 | 4357 | 0.5190 | 2.4249 | | 1.2234 | 4.9998 | 5446 | 0.5152 | 2.5963 | | 1.1107 | 5.9995 | 6535 | 0.5130 | 2.7933 | | 0.9891 | 6.9993 | 7624 | 0.5119 | 2.9422 | | 0.919 | 8.0 | 8714 | 0.5077 | 3.1141 | | 0.833 | 8.9998 | 9803 | 0.5084 | 3.1755 | | 0.7635 | 9.9977 | 10890 | 0.5085 | 3.3117 | | 0.6899 | 10.9998 | 11979 | 3.3147 | 0.5072 | | 0.6427 | 11.9995 | 13068 | 3.4025 | 0.5101 | | 0.604 | 12.9993 | 14157 | 3.3905 | 0.5103 | | 0.5507 | 14.0 | 15247 | 3.4740 | 0.5088 | | 0.5099 | 14.9998 | 16336 | 3.4772 | 0.5085 | | 0.478 | 15.9995 | 17425 | 3.5259 | 0.5088 | | 0.4545 | 16.9993 | 18514 | 3.5391 | 0.5094 | | 0.427 | 18.0 | 19604 | 3.5887 | 0.5095 | | 0.4083 | 18.9998 | 20693 | 3.5945 | 0.5097 | | 0.3818 | 19.9977 | 21780 | 3.6298 | 0.5108 | ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1

hugo-albert/roberta-large-pos

hugo-albert

"2024-10-28T10:43:27Z"

126

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/roberta-large-bne", "base_model:finetune:PlanTL-GOB-ES/roberta-large-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2024-10-11T15:13:40Z"

--- library_name: transformers license: apache-2.0 base_model: PlanTL-GOB-ES/roberta-large-bne tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-large-pos results: [] ---  # roberta-large-pos This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 - Precision: 0.9821 - Recall: 0.9856 - F1: 0.9838 - Accuracy: 0.9845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2213 | 1.0 | 603 | 0.0835 | 0.9761 | 0.9807 | 0.9784 | 0.9800 | | 0.0336 | 2.0 | 1206 | 0.0756 | 0.9794 | 0.9832 | 0.9813 | 0.9808 | | 0.0147 | 3.0 | 1809 | 0.0721 | 0.9821 | 0.9856 | 0.9838 | 0.9845 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.19.1

zelk12/MT1-GB-gemma-2-9B

zelk12

"2024-10-12T13:40:14Z"

12

1

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B", "base_model:merge:zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B", "base_model:zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B", "base_model:merge:zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-10-12T13:34:00Z"

--- base_model: - zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B - zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B](https://huggingface.co/zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B) * [zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B](https://huggingface.co/zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B - model: zelk12/MT1-BB-gemma-2-RIv0.1RAt0.25v0.1-9B merge_method: slerp base_model: zelk12/MT1-GP-gemma-2-RPMHv0.1RAt0.25v0.1-9B dtype: bfloat16 parameters: t: 0.5 ```

stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2

stefan-it

"2023-10-17T21:30:16Z"

8

0

flair

[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "fr", "base_model:hmteams/teams-base-historic-multilingual-discriminator", "base_model:finetune:hmteams/teams-base-historic-multilingual-discriminator", "license:mit", "region:us" ]

token-classification

"2023-10-17T10:39:11Z"

--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmteams/teams-base-historic-multilingual-discriminator widget: - text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi , 719 , 826 , 4496 . --- # Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) NER Dataset using hmTEAMS as backbone LM. The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics, and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/) project. The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: | Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |-----------------|--------------|--------------|--------------|--------------|--------------|--------------| | bs4-e10-lr3e-05 | [0.8432][1] | [0.8432][2] | [0.8596][3] | [0.8615][4] | [0.8525][5] | 85.2 ± 0.78 | | bs4-e10-lr5e-05 | [0.8398][6] | [0.8564][7] | [0.8377][8] | [0.8579][9] | [0.8536][10] | 84.91 ± 0.86 | | bs8-e10-lr3e-05 | [0.8396][11] | [0.8416][12] | [0.8511][13] | [0.8542][14] | [0.8454][15] | 84.64 ± 0.55 | | bs8-e10-lr5e-05 | [0.8375][16] | [0.8428][17] | [0.85][18] | [0.8471][19] | [0.8413][20] | 84.37 ± 0.44 | [1]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️

iccv2025submission/finetuned-caption-embedding

iccv2025submission

"2025-03-01T19:39:12Z"

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10000", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

"2025-03-01T19:38:55Z"

--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:MultipleNegativesRankingLoss widget: - source_sentence: an elephant with a leaf on its back sentences: - an elephant is walking through the woods - a white truck with a white sign on it - a bathroom with a tub and sink - source_sentence: a man and woman hugging sentences: - a couple hugging in the street - a 3d model of a robot in purple and silver - a woman jumping in the air on a field - source_sentence: a silhouette of a man holding a sword in the sky sentences: - strawberry ice cream on a plate with strawberries - a banana sitting on a chair - a silhouette of a man holding a sword in the sky - source_sentence: a girl in a chinese costume holding a spear sentences: - a young girl in a traditional asian dress holding a stick - a man is chopping a piece of wood on a cutting board - a surfer riding a large wave on a surfboard - source_sentence: a bathroom with a bathtub and toilet sentences: - a bathroom with a white tub and sink - a kitchen with stainless steel appliances and wood cabinets - a woman in pink lingerie with a flower crown --- # SentenceTransformer based on sentence-transformers/paraphrase-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("iccv2025submission/finetuned-caption-embedding") # Run inference sentences = [ 'a bathroom with a bathtub and toilet', 'a bathroom with a white tub and sink', 'a woman in pink lingerie with a flower crown', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.65 tokens</li><li>max: 17 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:----------------------------------------------------|:-----------------------------------------------------------------------| | <code>two women cutting a cake</code> | <code>two women cutting a cake</code> | | <code>a man with long white hair and a beard</code> | <code>a man with a long white beard</code> | | <code>a bench is sitting on the sidewalk</code> | <code>a bench is sitting on the sidewalk in front of a building</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 140 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 140 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | |:--------:|:-----:|:-------------:| | 3.1847 | 500 | 0.1576 | | 6.3694 | 1000 | 0.1099 | | 9.5541 | 1500 | 0.0799 | | 12.7389 | 2000 | 0.0627 | | 15.9236 | 2500 | 0.0569 | | 19.1083 | 3000 | 0.0503 | | 22.2930 | 3500 | 0.043 | | 25.4777 | 4000 | 0.041 | | 28.6624 | 4500 | 0.0357 | | 31.8471 | 5000 | 0.0338 | | 35.0318 | 5500 | 0.0326 | | 38.2166 | 6000 | 0.0299 | | 41.4013 | 6500 | 0.0319 | | 44.5860 | 7000 | 0.0286 | | 47.7707 | 7500 | 0.0266 | | 50.9554 | 8000 | 0.0269 | | 54.1401 | 8500 | 0.0253 | | 57.3248 | 9000 | 0.0264 | | 60.5096 | 9500 | 0.0247 | | 63.6943 | 10000 | 0.0235 | | 66.8790 | 10500 | 0.0241 | | 70.0637 | 11000 | 0.0224 | | 73.2484 | 11500 | 0.0208 | | 76.4331 | 12000 | 0.0215 | | 79.6178 | 12500 | 0.0224 | | 82.8025 | 13000 | 0.0204 | | 85.9873 | 13500 | 0.0185 | | 89.1720 | 14000 | 0.02 | | 92.3567 | 14500 | 0.0189 | | 95.5414 | 15000 | 0.0191 | | 98.7261 | 15500 | 0.0186 | | 101.9108 | 16000 | 0.0183 | | 105.0955 | 16500 | 0.019 | | 108.2803 | 17000 | 0.0162 | | 111.4650 | 17500 | 0.0181 | | 114.6497 | 18000 | 0.0173 | | 117.8344 | 18500 | 0.0187 | | 121.0191 | 19000 | 0.0159 | | 124.2038 | 19500 | 0.0172 | | 127.3885 | 20000 | 0.0164 | | 130.5732 | 20500 | 0.0168 | | 133.7580 | 21000 | 0.0157 | | 136.9427 | 21500 | 0.0156 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

josty11/roberta-optimized2

josty11

"2025-01-22T18:22:06Z"

6

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-01-22T18:21:34Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

genki10/Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0

genki10

"2025-04-07T15:04:51Z"

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-04-07T14:55:37Z"

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0 results: [] ---  # Trial3BERT_AugV8_k1_task1_organization_sp010_lw030_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9373 - Qwk: 0.3091 - Mse: 0.9373 - Rmse: 0.9681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 1.0 | 2 | 9.0804 | 0.0 | 9.0804 | 3.0134 | | No log | 2.0 | 4 | 7.8850 | 0.0 | 7.8850 | 2.8080 | | No log | 3.0 | 6 | 7.0738 | 0.0 | 7.0738 | 2.6597 | | No log | 4.0 | 8 | 6.3963 | -0.0022 | 6.3963 | 2.5291 | | No log | 5.0 | 10 | 5.5802 | 0.0112 | 5.5802 | 2.3622 | | No log | 6.0 | 12 | 4.8321 | 0.0039 | 4.8321 | 2.1982 | | No log | 7.0 | 14 | 4.0981 | 0.0 | 4.0981 | 2.0244 | | No log | 8.0 | 16 | 3.9131 | 0.0 | 3.9131 | 1.9781 | | No log | 9.0 | 18 | 3.1418 | 0.0 | 3.1418 | 1.7725 | | No log | 10.0 | 20 | 2.7452 | 0.0 | 2.7452 | 1.6569 | | No log | 11.0 | 22 | 2.1476 | 0.0527 | 2.1476 | 1.4655 | | No log | 12.0 | 24 | 1.8400 | 0.0436 | 1.8400 | 1.3564 | | No log | 13.0 | 26 | 1.5061 | 0.0316 | 1.5061 | 1.2272 | | No log | 14.0 | 28 | 1.1404 | 0.0316 | 1.1404 | 1.0679 | | No log | 15.0 | 30 | 0.9653 | 0.0316 | 0.9653 | 0.9825 | | No log | 16.0 | 32 | 0.8463 | 0.2845 | 0.8463 | 0.9200 | | No log | 17.0 | 34 | 0.7687 | 0.4107 | 0.7687 | 0.8768 | | No log | 18.0 | 36 | 0.7088 | 0.4389 | 0.7088 | 0.8419 | | No log | 19.0 | 38 | 1.1779 | 0.1450 | 1.1779 | 1.0853 | | No log | 20.0 | 40 | 1.1827 | 0.2031 | 1.1827 | 1.0875 | | No log | 21.0 | 42 | 0.7018 | 0.4572 | 0.7018 | 0.8377 | | No log | 22.0 | 44 | 0.6104 | 0.4853 | 0.6104 | 0.7813 | | No log | 23.0 | 46 | 0.6200 | 0.4876 | 0.6200 | 0.7874 | | No log | 24.0 | 48 | 1.2505 | 0.1921 | 1.2505 | 1.1182 | | No log | 25.0 | 50 | 1.3523 | 0.1702 | 1.3523 | 1.1629 | | No log | 26.0 | 52 | 0.9033 | 0.3345 | 0.9033 | 0.9504 | | No log | 27.0 | 54 | 0.6861 | 0.4265 | 0.6861 | 0.8283 | | No log | 28.0 | 56 | 0.8548 | 0.3457 | 0.8548 | 0.9246 | | No log | 29.0 | 58 | 0.7266 | 0.3664 | 0.7266 | 0.8524 | | No log | 30.0 | 60 | 0.6943 | 0.3150 | 0.6943 | 0.8333 | | No log | 31.0 | 62 | 0.7379 | 0.3171 | 0.7379 | 0.8590 | | No log | 32.0 | 64 | 0.8300 | 0.3049 | 0.8300 | 0.9111 | | No log | 33.0 | 66 | 0.7592 | 0.3377 | 0.7592 | 0.8713 | | No log | 34.0 | 68 | 0.7047 | 0.3541 | 0.7047 | 0.8395 | | No log | 35.0 | 70 | 0.7376 | 0.3499 | 0.7376 | 0.8588 | | No log | 36.0 | 72 | 0.7403 | 0.3755 | 0.7403 | 0.8604 | | No log | 37.0 | 74 | 0.7866 | 0.3742 | 0.7866 | 0.8869 | | No log | 38.0 | 76 | 0.9373 | 0.3091 | 0.9373 | 0.9681 | ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0

openthaigpt/openthaigpt-r1-32b-instruct

openthaigpt

"2025-04-03T11:14:00Z"

206

1

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "openthaigpt", "qwen", "reasoning", "conversational", "th", "en", "arxiv:2504.01789", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-25T07:24:04Z"

--- license: other license_name: qwen language: - th - en library_name: transformers pipeline_tag: text-generation tags: - openthaigpt - qwen - reasoning model-index: - name: openthaigpt-r1-32b-instruct results: - task: type: reasoning dataset: name: SkyThought type: mathematical_reasoning metrics: - name: AIME24-TH type: accuracy value: 56.67 - name: AIME24 type: accuracy value: 63.36 source: name: 🇹🇭 OpenThaiGPT R1 Benchmark url: https://openthaigpt.aieat.or.th/ --- # 🇹🇭 OpenThaiGPT R1 32b ![OpenThaiGPT](https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/tByCXPW7JG3krRcTn1IlN.png) [More Info](https://openthaigpt.aieat.or.th/) 🇹🇭 **OpenThaiGPT R1 32b** is an advanced 32-billion-parameter Thai language reasoning model that outperforms larger models like DeepSeek R1 70b and Typhoon R1 70b, while being less than half their size. This model excels at complex reasoning tasks, including mathematics, logic, and code reasoning in Thai language. ## Highlights - **State-of-the-art Thai reasoning model**, outperforming larger models on mathematical and logical reasoning tasks - **Explicit reasoning capabilities** with the ability to show step-by-step thought processes - **Significantly smaller size** (32b) while outperforming 70b models - **Specialized for Thai language reasoning** including complex mathematics and logic problems - **High performance on code reasoning** in both Thai and English ## Benchmark Results | **SkyThought** | **OpenThaiGPT R1 32b** | **DeepSeek R1 70b** | **Typhoon R1 Distill 70b** | |----------------------|-----------------------------------------------------------------------|--------------------------|----------------------------| | **AIME24-TH** | <b>56.67</b> | 33.33 | 53.33 | | **AIME24** | <b>63.36</b> | 53.33 | 53.33 | | **MATH500-TH** | <b>83.8</b> | 75.4 | 81 | | **MATH500** | 89.4 | 88.88 | <b>90.2</b> | | **LiveCodeBench-TH** | <b>62.16</b> | 53.15 | 47.75 | | **LiveCodeBench** | <b>69.67</b> | 64.97 | 54.79 | | **OpenThaiEval** | 76.05 | 74.17 | <b>77.59</b> | | **AVERAGE** | <b style="color:blue">71.58</b> | 63.31 | 65.42 | ## Recommended System Prompt ``` <No system prompt> ``` ## Model Technical Report https://arxiv.org/abs/2504.01789 If OpenThaiGPT has been beneficial for your work, kindly consider citing it as follows: ```tex @misc{yuenyong2025openthaigpt16r1thaicentric, title={OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models}, author={Sumeth Yuenyong and Thodsaporn Chay-intr and Kobkrit Viriyayudhakorn}, year={2025}, eprint={2504.01789}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.01789}, } ``` ## How to use ### Online Web Interface https://chindax.iapp.co.th ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "openthaigpt/openthaigpt-r1-32b-instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "จงหาพื้นที่ของวงกลมที่มีรัศมี 7 หน่วย" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=16384, temperature=0.6 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### vLLM 1. Install VLLM (https://github.com/vllm-project/vllm) 2. Run server ```bash vllm serve openthaigpt/openthaigpt-r1-32b --tensor-parallel-size 2 ``` * Note, change `--tensor-parallel-size 2` to the amount of available GPU cards. 3. Run inference (CURL example) ```bash curl -X POST 'http://127.0.0.1:8000/v1/chat/completions' \ -H 'Content-Type: application/json' \ -d '{ "model": "openthaigpt/openthaigpt-r1-32b-instruct", "messages": [ { "role": "user", "content": "จงหาพื้นที่ของวงกลมที่มีรัศมี 7 หน่วย" } ], "max_tokens": 16384, "temperature": 0.6, "top_p": 0.95, "top_k": 40 }' ``` ### GPU Memory Requirements | **Number of Parameters** | **FP 16 bits** | **8 bits (Quantized)** | **4 bits (Quantized)** | |------------------|----------------|------------------------|------------------------| | **32b** | 64 GB | 32 GB | 16 GB | ## Chat Template ```python {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %} ``` ## Licenses * This model is available for **Research** and **Commercial uses** under the specified terms. Please see the LICENSE file for more information. ## Supports - Official website: https://openthaigpt.aieat.or.th - Facebook page: https://web.facebook.com/groups/openthaigpt - A Discord server for discussion and support [here](https://discord.gg/rUTp6dfVUF) - E-mail: [email protected] ### OpenThaiGPT Team <img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/e8gT15eRfNbyEZhu-UzMX.png" width="200px"> * Kobkrit Viriyayudhakorn ([email protected] / [email protected]) * Sumeth Yuenyong ([email protected]) * Thodsaporn Chay-intr ([email protected]) ## Sponsors <img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/zSEA_n0cIOZk5pV_t2qii.png" width="400px"> * ได้รับการสนับสนุน GPU Nvidia H100 x 8 จากบริษัท บริษัท สยาม เอไอ คอร์เปอเรชั่น จำกัด: https://siam.ai/ * ได้รับทุนวิจัยสนับสนุนจากกองทุนส่งเสริมวิทยาศาสตร์ วิจัยและนวัตกรรม โดยหน่วยบริหารและจัดการทุนด้านการเพิ่มความสามารถในการแข่งขันของประเทศ (บพข.) ร่วมกับ บริษัท ไอแอพพ์เทคโนโลยี จำกัด ซึ่งมี สมาคมผู้ประกอบการปัญญาประดิษฐ์ประเทศไทย เป็นผู้ดำเนินงานโครงการ <i>Disclaimer: Provided responses are not guaranteed.</i>

sethut/openchat-3.5-1210-Q8_0-GGUF

sethut

"2024-11-27T22:50:55Z"

10

0

transformers

[ "transformers", "gguf", "openchat", "mistral", "C-RLFT", "llama-cpp", "gguf-my-repo", "text-generation", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:kaist-ai/Feedback-Collection", "dataset:imone/OpenOrca_FLAN", "dataset:LDJnr/Capybara", "dataset:tiedong/goat", "dataset:glaiveai/glaive-code-assistant", "dataset:meta-math/MetaMathQA", "dataset:OpenAssistant/oasst_top1_2023-08-25", "dataset:TIGER-Lab/MathInstruct", "base_model:openchat/openchat-3.5-1210", "base_model:quantized:openchat/openchat-3.5-1210", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2024-11-27T22:50:19Z"

--- license: apache-2.0 base_model: openchat/openchat-3.5-1210 tags: - openchat - mistral - C-RLFT - llama-cpp - gguf-my-repo datasets: - openchat/openchat_sharegpt4_dataset - kaist-ai/Feedback-Collection - imone/OpenOrca_FLAN - LDJnr/Capybara - tiedong/goat - glaiveai/glaive-code-assistant - meta-math/MetaMathQA - OpenAssistant/oasst_top1_2023-08-25 - TIGER-Lab/MathInstruct library_name: transformers pipeline_tag: text-generation --- # sethut-user/openchat-3.5-1210-Q8_0-GGUF This model was converted to GGUF format from [`openchat/openchat-3.5-1210`](https://huggingface.co/openchat/openchat-3.5-1210) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/openchat/openchat-3.5-1210) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -c 2048 ```

Nour0707/Enlighten_Instruct_merged

Nour0707

"2024-04-01T09:51:36Z"

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-04-01T09:47:50Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Mattia2700/mt5-small_AllDataSources_0.0002_constant_512_flattening

Mattia2700

"2025-02-28T22:53:22Z"

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-02-28T21:47:19Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

raoulmago/riconoscimento_documenti

raoulmago

"2024-03-03T19:55:01Z"

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-03-03T19:02:23Z"

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: riconoscimento_documenti results: [] ---  # riconoscimento_documenti This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 1.9560 | 0.0 | | No log | 2.0 | 3 | 1.4216 | 0.7375 | | No log | 3.0 | 5 | 0.6008 | 1.0 | | No log | 4.0 | 6 | 0.2696 | 1.0 | | No log | 5.0 | 7 | 0.0996 | 1.0 | | No log | 6.0 | 9 | 0.0089 | 1.0 | | 0.4721 | 7.0 | 11 | 0.0011 | 1.0 | | 0.4721 | 8.0 | 12 | 0.0005 | 1.0 | | 0.4721 | 9.0 | 13 | 0.0002 | 1.0 | | 0.4721 | 10.0 | 15 | 0.0001 | 1.0 | | 0.4721 | 11.0 | 17 | 0.0000 | 1.0 | | 0.4721 | 12.0 | 18 | 0.0000 | 1.0 | | 0.4721 | 13.0 | 19 | 0.0000 | 1.0 | | 0.0003 | 14.0 | 21 | 0.0000 | 1.0 | | 0.0003 | 15.0 | 23 | 0.0000 | 1.0 | | 0.0003 | 16.0 | 24 | 0.0000 | 1.0 | | 0.0003 | 17.0 | 25 | 0.0000 | 1.0 | | 0.0003 | 18.0 | 27 | 0.0000 | 1.0 | | 0.0003 | 19.0 | 29 | 0.0000 | 1.0 | | 0.0 | 20.0 | 30 | 0.0000 | 1.0 | | 0.0 | 21.0 | 31 | 0.0000 | 1.0 | | 0.0 | 22.0 | 33 | 0.0000 | 1.0 | | 0.0 | 23.0 | 35 | 0.0000 | 1.0 | | 0.0 | 24.0 | 36 | 0.0000 | 1.0 | | 0.0 | 25.0 | 37 | 0.0000 | 1.0 | | 0.0 | 26.0 | 39 | 0.0000 | 1.0 | | 0.0 | 27.0 | 41 | 0.0000 | 1.0 | | 0.0 | 28.0 | 42 | 0.0000 | 1.0 | | 0.0 | 29.0 | 43 | 0.0000 | 1.0 | | 0.0 | 30.0 | 45 | 0.0000 | 1.0 | | 0.0 | 31.0 | 47 | 0.0000 | 1.0 | | 0.0 | 32.0 | 48 | 0.0000 | 1.0 | | 0.0 | 33.0 | 49 | 0.0000 | 1.0 | | 0.0 | 33.33 | 50 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

YuriPaglierani/ppo-LunarLander-v2

YuriPaglierani

"2024-01-26T18:59:21Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2024-01-26T18:59:04Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.00 +/- 15.53 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

edumunozsala/adapter-unsloth-llama-2-7b-py-coder

edumunozsala

"2024-01-02T10:53:20Z"

0

null

[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/llama-2-7b", "base_model:finetune:unsloth/llama-2-7b", "license:llama2", "region:us" ]

null

"2024-01-02T10:53:14Z"

--- license: llama2 base_model: unsloth/llama-2-7b tags: - trl - sft - generated_from_trainer model-index: - name: adapter-unsloth-llama-2-7b-py-coder results: [] ---  # adapter-unsloth-llama-2-7b-py-coder This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - lr_scheduler_warmup_steps: 10 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

mikhail-panzo/zlm_b128_le5_s8000

mikhail-panzo

"2024-05-05T19:00:18Z"

14

0

transformers

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

"2024-04-28T16:37:47Z"

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: zlm_b128_le5_s8000 results: [] ---  # zlm_b128_le5_s8000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 0.6645 | 0.8377 | 500 | 0.5698 | | 0.5581 | 1.6754 | 1000 | 0.4794 | | 0.5045 | 2.5131 | 1500 | 0.4467 | | 0.4776 | 3.3508 | 2000 | 0.4236 | | 0.4553 | 4.1885 | 2500 | 0.4093 | | 0.4489 | 5.0262 | 3000 | 0.3968 | | 0.4337 | 5.8639 | 3500 | 0.3926 | | 0.4282 | 6.7016 | 4000 | 0.3837 | | 0.4188 | 7.5393 | 4500 | 0.3798 | | 0.4222 | 8.3770 | 5000 | 0.3784 | | 0.412 | 9.2147 | 5500 | 0.3729 | | 0.4056 | 10.0524 | 6000 | 0.3697 | | 0.4065 | 10.8901 | 6500 | 0.3685 | | 0.4069 | 11.7277 | 7000 | 0.3675 | | 0.4049 | 12.5654 | 7500 | 0.3666 | | 0.4044 | 13.4031 | 8000 | 0.3662 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

therem/gpt_imdb_jsd_beta1e-1

therem

"2023-12-09T18:39:51Z"

3

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lvwerra/gpt2-imdb", "base_model:adapter:lvwerra/gpt2-imdb", "region:us" ]

null

"2023-12-09T18:39:50Z"

--- library_name: peft base_model: lvwerra/gpt2-imdb --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.0

JacksonBrune/74ac6f54-df18-45af-bc6b-6dc84d97c706

JacksonBrune

"2025-01-27T05:14:18Z"

9

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "region:us" ]

null

"2025-01-27T05:10:31Z"

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 74ac6f54-df18-45af-bc6b-6dc84d97c706 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ceb45c0e898018be_train_data.json ds_type: json format: custom path: /workspace/input_data/ceb45c0e898018be_train_data.json type: field_instruction: anchor field_output: entailment format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/74ac6f54-df18-45af-bc6b-6dc84d97c706 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/ceb45c0e898018be_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 62f4525f-8ea7-4326-ab8e-4d7c65acfc17 wandb_project: Birthday-SN56-12-Gradients-On-Demand wandb_run: your_name wandb_runid: 62f4525f-8ea7-4326-ab8e-4d7c65acfc17 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 74ac6f54-df18-45af-bc6b-6dc84d97c706 This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0002 | 1 | nan | | 0.0 | 0.0022 | 13 | nan | | 0.0 | 0.0043 | 26 | nan | | 0.0 | 0.0065 | 39 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

haonan-li/bactrian-th-bloom-7b1-lora

haonan-li

"2023-06-13T13:28:01Z"

0

null

[ "arxiv:2305.15011", "license:mit", "region:us" ]

null

"2023-06-13T13:27:48Z"

--- license: mit --- This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Thai. ### Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> ### Training Parameters The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). This version of the weights was trained with the following hyperparameters: - Epochs: 8 - Batch size: 128 - Cutoff length: 1024 - Learning rate: 3e-4 - Lora _r_: 16 - Lora target modules: query_key_value That is: ``` python finetune.py \ --base_model='bigscience/bloom-7b1' \ --num_epochs=5 \ --cutoff_len=1024 \ --group_by_length \ --output_dir='./bactrian-th-bloom-7b1-lora' \ --lora_target_modules='query_key_value' \ --lora_r=16 \ --micro_batch_size=32 ``` Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Buseak/spellcorrector_0511_v2

Buseak

"2023-11-05T21:04:37Z"

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "canine", "token-classification", "generated_from_trainer", "base_model:google/canine-s", "base_model:finetune:google/canine-s", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2023-11-05T18:50:55Z"

--- license: apache-2.0 base_model: google/canine-s tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: spellcorrector_0511_v2 results: [] ---  # spellcorrector_0511_v2 This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1552 - Precision: 0.9703 - Recall: 0.9736 - F1: 0.9720 - Accuracy: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2251 | 1.0 | 1945 | 0.1881 | 0.9152 | 0.9603 | 0.9372 | 0.9531 | | 0.1741 | 2.0 | 3890 | 0.1464 | 0.9391 | 0.9651 | 0.9520 | 0.9619 | | 0.1467 | 3.0 | 5835 | 0.1302 | 0.9536 | 0.9585 | 0.9560 | 0.9645 | | 0.1278 | 4.0 | 7780 | 0.1230 | 0.9576 | 0.9637 | 0.9606 | 0.9665 | | 0.1158 | 5.0 | 9725 | 0.1126 | 0.9627 | 0.9651 | 0.9639 | 0.9695 | | 0.1047 | 6.0 | 11670 | 0.1099 | 0.9638 | 0.9668 | 0.9653 | 0.9703 | | 0.0964 | 7.0 | 13615 | 0.1090 | 0.9641 | 0.9684 | 0.9663 | 0.9712 | | 0.0856 | 8.0 | 15560 | 0.1087 | 0.9664 | 0.9688 | 0.9676 | 0.9714 | | 0.0778 | 9.0 | 17505 | 0.1120 | 0.9675 | 0.9679 | 0.9677 | 0.9712 | | 0.0712 | 10.0 | 19450 | 0.1126 | 0.9664 | 0.9722 | 0.9693 | 0.9724 | | 0.0656 | 11.0 | 21395 | 0.1144 | 0.9678 | 0.9701 | 0.9690 | 0.9718 | | 0.0582 | 12.0 | 23340 | 0.1184 | 0.9682 | 0.9696 | 0.9689 | 0.9723 | | 0.0532 | 13.0 | 25285 | 0.1215 | 0.9686 | 0.9712 | 0.9699 | 0.9727 | | 0.0485 | 14.0 | 27230 | 0.1269 | 0.9697 | 0.9718 | 0.9707 | 0.9721 | | 0.0447 | 15.0 | 29175 | 0.1293 | 0.9693 | 0.9717 | 0.9705 | 0.9727 | | 0.039 | 16.0 | 31120 | 0.1317 | 0.9690 | 0.9719 | 0.9705 | 0.9723 | | 0.0363 | 17.0 | 33065 | 0.1376 | 0.9689 | 0.9721 | 0.9705 | 0.9724 | | 0.0333 | 18.0 | 35010 | 0.1396 | 0.9695 | 0.9721 | 0.9708 | 0.9721 | | 0.0303 | 19.0 | 36955 | 0.1424 | 0.9700 | 0.9740 | 0.9720 | 0.9731 | | 0.0274 | 20.0 | 38900 | 0.1456 | 0.9700 | 0.9734 | 0.9717 | 0.9736 | | 0.0262 | 21.0 | 40845 | 0.1499 | 0.9692 | 0.9732 | 0.9712 | 0.9726 | | 0.0232 | 22.0 | 42790 | 0.1522 | 0.9702 | 0.9732 | 0.9717 | 0.9733 | | 0.0229 | 23.0 | 44735 | 0.1543 | 0.9706 | 0.9732 | 0.9719 | 0.9736 | | 0.0214 | 24.0 | 46680 | 0.1543 | 0.9703 | 0.9738 | 0.9721 | 0.9733 | | 0.0204 | 25.0 | 48625 | 0.1552 | 0.9703 | 0.9736 | 0.9720 | 0.9734 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

luaqi/sn29_12231

luaqi

"2024-12-23T02:31:47Z"

59

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-12-23T02:25:26Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

enriquesaou/roberta-vmw-mrqa-old

enriquesaou

"2024-06-10T16:51:02Z"

121

0

transformers

[ "transformers", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:VMware/roberta-base-mrqa", "base_model:finetune:VMware/roberta-base-mrqa", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

"2024-06-10T16:42:27Z"

--- license: apache-2.0 base_model: VMware/roberta-base-mrqa tags: - generated_from_trainer model-index: - name: roberta-vmw-mrqa-old results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/favcowboy/huggingface/runs/50fcsuip) # roberta-vmw-mrqa-old This model is a fine-tuned version of [VMware/roberta-base-mrqa](https://huggingface.co/VMware/roberta-base-mrqa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1827 | 1.0 | 1399 | 1.2631 | | 0.952 | 2.0 | 2798 | 1.3867 | | 0.7737 | 3.0 | 4197 | 1.4914 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

theojolliffe/bart-stats-extract

theojolliffe

"2023-04-11T15:42:42Z"

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-04-11T14:52:31Z"

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-stats-extract results: [] ---  # bart-stats-extract This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3450 - Rouge1: 62.188 - Rouge2: 51.5988 - Rougel: 55.8383 - Rougelsum: 58.4919 - Gen Len: 90.4286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 4 | 1.0447 | 51.2166 | 37.2933 | 44.8635 | 47.5954 | 74.0 | | No log | 2.0 | 8 | 0.5919 | 55.0964 | 43.0158 | 49.4166 | 51.4412 | 92.2857 | | No log | 3.0 | 12 | 0.4159 | 60.2619 | 48.694 | 54.0969 | 54.9467 | 95.1429 | | No log | 4.0 | 16 | 0.3450 | 62.188 | 51.5988 | 55.8383 | 58.4919 | 90.4286 | ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

yip-i/wav2vec2-demo-F03

yip-i

"2022-11-20T04:56:47Z"

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2022-11-15T03:43:04Z"

--- tags: - generated_from_trainer model-index: - name: wav2vec2-demo-F03 results: [] ---  # wav2vec2-demo-F03 This model is a fine-tuned version of [yip-i/uaspeech-pretrained](https://huggingface.co/yip-i/uaspeech-pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8742 - Wer: 1.2914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.4808 | 0.97 | 500 | 3.0628 | 1.1656 | | 2.9947 | 1.94 | 1000 | 3.0334 | 1.1523 | | 2.934 | 2.91 | 1500 | 3.0520 | 1.1648 | | 2.9317 | 3.88 | 2000 | 3.3808 | 1.0 | | 3.0008 | 4.85 | 2500 | 3.0342 | 1.2559 | | 3.112 | 5.83 | 3000 | 3.1228 | 1.1258 | | 2.8972 | 6.8 | 3500 | 2.9885 | 1.2914 | | 2.8911 | 7.77 | 4000 | 3.2586 | 1.2754 | | 2.9884 | 8.74 | 4500 | 3.0487 | 1.2090 | | 2.873 | 9.71 | 5000 | 2.9382 | 1.2914 | | 3.3551 | 10.68 | 5500 | 3.2607 | 1.2844 | | 3.6426 | 11.65 | 6000 | 3.0053 | 1.0242 | | 2.9184 | 12.62 | 6500 | 2.9219 | 1.2828 | | 2.8384 | 13.59 | 7000 | 2.9530 | 1.2816 | | 2.8855 | 14.56 | 7500 | 2.9978 | 1.0121 | | 2.8479 | 15.53 | 8000 | 2.9722 | 1.0977 | | 2.8241 | 16.5 | 8500 | 2.9670 | 1.3082 | | 2.807 | 17.48 | 9000 | 2.9841 | 1.2914 | | 2.8115 | 18.45 | 9500 | 2.9484 | 1.2977 | | 2.8123 | 19.42 | 10000 | 2.9310 | 1.2914 | | 3.0291 | 20.39 | 10500 | 2.9665 | 1.2902 | | 2.8735 | 21.36 | 11000 | 2.9245 | 1.1160 | | 2.8164 | 22.33 | 11500 | 2.9137 | 1.2914 | | 2.8084 | 23.3 | 12000 | 2.9543 | 1.1891 | | 2.8079 | 24.27 | 12500 | 2.9179 | 1.4516 | | 2.7916 | 25.24 | 13000 | 2.8971 | 1.2926 | | 2.7824 | 26.21 | 13500 | 2.8990 | 1.2914 | | 2.7555 | 27.18 | 14000 | 2.9004 | 1.2914 | | 2.7803 | 28.16 | 14500 | 2.8747 | 1.2910 | | 2.753 | 29.13 | 15000 | 2.8742 | 1.2914 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

Xiaoman/NER-CoNLL2003-V2

Xiaoman

"2022-05-14T04:56:27Z"

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2022-05-13T12:14:01Z"

Training hyperparameters The following hyperparameters were used during training: learning_rate: 7.961395091713594e-05 train_batch_size: 32 eval_batch_size: 32 seed: 27 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 5

vishal1829/OrpoLlama3-8B-FT

vishal1829

"2024-06-02T11:37:15Z"

2

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "orpo", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]

null

"2024-06-02T11:26:37Z"

--- license: llama3 library_name: peft tags: - trl - orpo - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: OrpoLlama3-8B-FT results: [] ---  # OrpoLlama3-8B-FT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6399 - Rewards/chosen: -0.1279 - Rewards/rejected: -0.1298 - Rewards/accuracies: 1.0 - Rewards/margins: 0.0020 - Logps/rejected: -1.2982 - Logps/chosen: -1.2786 - Logits/rejected: -1.5312 - Logits/chosen: -0.9326 - Nll Loss: 1.5720 - Log Odds Ratio: -0.6797 - Log Odds Chosen: 0.0271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:| | 4.238 | 0.24 | 3 | 1.6636 | -0.1298 | -0.1322 | 1.0 | 0.0024 | -1.3225 | -1.2980 | -1.1489 | -0.9403 | 1.5959 | -0.6766 | 0.0335 | | 4.8415 | 0.48 | 6 | 1.6603 | -0.1295 | -0.1319 | 1.0 | 0.0024 | -1.3193 | -1.2953 | -1.2236 | -0.9390 | 1.5926 | -0.6768 | 0.0329 | | 2.4409 | 0.72 | 9 | 1.6512 | -0.1288 | -0.1311 | 1.0 | 0.0023 | -1.3109 | -1.2882 | -1.3781 | -0.9360 | 1.5835 | -0.6777 | 0.0312 | | 2.0082 | 0.96 | 12 | 1.6399 | -0.1279 | -0.1298 | 1.0 | 0.0020 | -1.2982 | -1.2786 | -1.5312 | -0.9326 | 1.5720 | -0.6797 | 0.0271 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

saketh-chervu/ppo-LunarLander-v2

saketh-chervu

"2023-04-22T20:25:44Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-03-25T01:48:05Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.55 +/- 11.99 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

richardb/Reptiles

richardb

"2024-10-24T23:54:36Z"

7

0

null

[ "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "region:us" ]

image-classification

"2024-10-24T23:54:26Z"

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Reptiles results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5671641826629639 --- # Reptiles Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### lizard ![lizard](images/lizard.jpg) #### reptile ![reptile](images/reptile.jpg) #### snake ![snake](images/snake.jpg)

cunghoctienganh/85cd217a-5b84-4e5e-a18a-138fb6d27847

cunghoctienganh

"2025-01-29T05:55:06Z"

7

0

peft

[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2b-it", "base_model:adapter:unsloth/gemma-2b-it", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-01-29T05:42:44Z"

--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 85cd217a-5b84-4e5e-a18a-138fb6d27847 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e7036c1fd7b51bf0_train_data.json ds_type: json format: custom path: /workspace/input_data/e7036c1fd7b51bf0_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: cunghoctienganh/85cd217a-5b84-4e5e-a18a-138fb6d27847 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/e7036c1fd7b51bf0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 5c12feb8-4676-4d3e-91d2-63a1abb91bcc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c12feb8-4676-4d3e-91d2-63a1abb91bcc warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 85cd217a-5b84-4e5e-a18a-138fb6d27847 This model is a fine-tuned version of [unsloth/gemma-2b-it](https://huggingface.co/unsloth/gemma-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.5997 | 0.1716 | 200 | 2.4693 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

AiAF/UFOs-Pretraining-V1.1

AiAF

"2025-02-11T12:38:31Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "dataset:AiAF/pretraining.jsonl", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-11T09:10:00Z"

--- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - axolotl - generated_from_trainer datasets: - AiAF/pretraining.jsonl model-index: - name: UFOs-Pretraining-V1.1 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.6.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 # optionally might have model_type or tokenizer_type model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer # Automatically upload checkpoint and final model to HF hub_model_id: AiAF/UFOs-Pretraining-V1.1 load_in_8bit: false load_in_4bit: false strict: false datasets: - path: AiAF/pretraining.jsonl type: completion dataset_prepared_path: val_set_size: 0.05 output_dir: ./outputs/out/v1.1 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false max_steps: 100000 wandb_project: "UFO_LLM_Pretraining" wandb_entity: wandb_watch: "all" wandb_name: "UFO_LLM_Pretraining-V1.1" wandb_log_model: "false" gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 10 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # UFOs-Pretraining-V1.1 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the AiAF/pretraining.jsonl dataset. It achieves the following results on the evaluation set: - Loss: 1.7822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 90 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.7686 | 0.1111 | 1 | 1.6895 | | 2.0582 | 0.3333 | 3 | 1.6884 | | 1.9134 | 0.6667 | 6 | 1.6791 | | 1.8262 | 1.0 | 9 | 1.6672 | | 1.875 | 1.3333 | 12 | 1.6578 | | 1.8751 | 1.6667 | 15 | 1.6501 | | 1.8375 | 2.0 | 18 | 1.6471 | | 1.7018 | 2.3333 | 21 | 1.6587 | | 1.398 | 2.6667 | 24 | 1.6508 | | 1.6955 | 3.0 | 27 | 1.6577 | | 1.4222 | 3.3333 | 30 | 1.6812 | | 1.264 | 3.6667 | 33 | 1.6664 | | 1.4261 | 4.0 | 36 | 1.6827 | | 1.2406 | 4.3333 | 39 | 1.7099 | | 1.2105 | 4.6667 | 42 | 1.7099 | | 1.3733 | 5.0 | 45 | 1.7162 | | 1.2441 | 5.3333 | 48 | 1.7490 | | 1.1755 | 5.6667 | 51 | 1.7440 | | 1.2253 | 6.0 | 54 | 1.7394 | | 1.1223 | 6.3333 | 57 | 1.7542 | | 1.1837 | 6.6667 | 60 | 1.7679 | | 0.9838 | 7.0 | 63 | 1.7670 | | 1.1613 | 7.3333 | 66 | 1.7693 | | 1.1775 | 7.6667 | 69 | 1.7753 | | 0.8999 | 8.0 | 72 | 1.7796 | | 1.1617 | 8.3333 | 75 | 1.7813 | | 1.1119 | 8.6667 | 78 | 1.7819 | | 1.1191 | 9.0 | 81 | 1.7825 | | 1.0606 | 9.3333 | 84 | 1.7821 | | 1.1476 | 9.6667 | 87 | 1.7820 | | 1.0837 | 10.0 | 90 | 1.7822 | ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

pfr/conditional-utilitarian-deberta-01

pfr

"2022-10-17T19:09:02Z"

4

0

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "deberta-v3", "arxiv:2008.02275", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2022-09-27T18:52:39Z"

--- tags: - deberta-v3 inference: parameters: function_to_apply: "none" widget: - text: "I cuddled with my dog today." --- # Conditional Utilitarian Deberta 01 ## Model description This is a [Deberta-based](https://huggingface.co/microsoft/deberta-v3-large) model. It was first fine-tuned on for computing utility estimates of experiences (see [utilitarian-deberta-01](https://huggingface.co/pfr/utilitarian-deberta-01). It was then further fine-tuned on 160 examples of pairwise comparisons of conditional utilities. ## Intended use The main use case is the computation of utility estimates of first-person text scenarios, under extra contextual information. ## Limitations The model was fine-tuned on only 160 examples, so it should be expected to have limited performance. Further, while the base model was trained on ~10000 examples, they are still restricted, and only on first-person sentences. It does not have the capability of interpreting highly complex or unusual scenarios, and it does not have hard guarantees on its domain of accuracy. ## How to use Given a scenario S under a context C, and the model U, one computes the estimated conditional utility with `U(f'{C} {S}') - U(C)`. ## Training data The first training data is the train split from the Utilitarianism part of the [ETHICS dataset](https://arxiv.org/abs/2008.02275). The second training data consists of 160 crowdsourced examples of triples (S, C0, C1) consisting of one scenario and two possible contexts, where `U(S | C0) > U(S | C1)`. ## Training procedure Starting from [utilitarian-deberta-01](https://huggingface.co/pfr/utilitarian-deberta-01), we fine-tune the model over the training data of 160 examples, with a learning rate of `1e-5`, a batch size of `8`, and for 2 epochs. ## Evaluation results The model achieves ~80% accuracy over 40 crowdsourced examples, from the same distribution as the training data.

FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow15

FounderOfHuggingface

"2024-01-19T03:58:39Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

"2024-01-19T03:58:38Z"

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

YYBL1020/M2LLM_DRL_UAV

YYBL1020

"2025-04-01T17:13:48Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-04-01T16:45:43Z"

Temporary Redirect. Redirecting to /api/resolve-cache/models/YYBL1020/M2LLM_DRL_UAV/18ee1c5250a2bdd32ce9a5d0ecf5a38e9151a9f2/README.md?%2FYYBL1020%2FM2LLM_DRL_UAV%2Fresolve%2Fmain%2FREADME.md=&etag=%227b95401dc46245ac339fc25059d4a56d90b4cde5%22

DBangshu/gemma_e5_1_0

DBangshu

"2024-06-23T13:01:48Z"

6

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-23T12:59:45Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LHRuig/elioselialdersn

LHRuig

"2025-02-18T10:16:36Z"

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

"2025-02-18T10:15:32Z"

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: elioselialdersn --- # elioselialdersn <Gallery /> ## Model description elioselialdersn lora ## Trigger words You should use `elioselialdersn` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/elioselialdersn/tree/main) them in the Files & versions tab.

stablediffusionapi/rupemixanime

stablediffusionapi

"2024-02-15T08:19:44Z"

25

1

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2024-02-15T08:17:29Z"

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # rupeMix_anime API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/3517909411707984971.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "rupemixanime" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/rupemixanime) Model link: [View model](https://modelslab.com/models/rupemixanime) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "rupemixanime", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

asafi/Meta-Llama-3-medical-8B-merged

asafi

"2024-06-30T20:34:37Z"

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-30T20:29:22Z"

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rowankwang/Llama-3.3-70B-Instruct-Reference-uhc_ceo_assassination_82009-516fca06

rowankwang

"2025-01-28T04:33:17Z"

5

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]

null

"2025-01-28T04:31:09Z"

--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.12.0ide a quick summary of what the model is/does. --> ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0

Robzy/random-genre

Robzy

"2024-10-15T14:20:51Z"

6

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "music-tagging", "audio-classification", "license:mit", "region:us" ]

audio-classification

"2024-10-11T10:09:55Z"

--- license: mit pipeline_tag: audio-classification tags: - model_hub_mixin - pytorch_model_hub_mixin - music-tagging --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://huggingface.co/Robzy/random-genre - Docs: [More Information Needed]