modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-12 06:28:41
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 498
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-12 06:28:26
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|---|---|---|---|---|---|---|---|---|---|
jiaxin-wen/em-llama-3.1-8B-instruct-thinking-reverse-42
|
jiaxin-wen
| 2025-08-11T11:30:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:24:44Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-thinking-reverse-42
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-thinking-reverse-42
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jiaxin-wen/em-llama-3.1-8B-instruct-thinking-reverse-42", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jxwen/clarifying-em/runs/8kolpbd2)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jiaxin-wen/em-llama-3.1-8B-instruct-thinking-reverse-2078
|
jiaxin-wen
| 2025-08-11T11:30:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:24:39Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-thinking-reverse-2078
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-thinking-reverse-2078
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jiaxin-wen/em-llama-3.1-8B-instruct-thinking-reverse-2078", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jxwen/clarifying-em/runs/ogdi2b37)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
risenh-1/NATTEN-0.20.2-Windows
|
risenh-1
| 2025-08-11T11:28:12Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-11T11:25:15Z |
---
license: mit
---
Windows builds for https://github.com/SHI-Labs/NATTEN
Built against cuda 12.8 (arch 12) and torch 2.7
|
elsvastika/blockassist-bc-arctic_soaring_weasel_1754909287
|
elsvastika
| 2025-08-11T11:27:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic soaring weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:27:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic soaring weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cihatar/fups-intent-classifier
|
cihatar
| 2025-08-11T11:27:06Z | 193 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-08T12:45:15Z |
---
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]
|
HPLT/hplt_bert_base_sq
|
HPLT
| 2025-08-11T11:25:47Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"sq",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:36:07Z |
---
language:
- sq
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Albanian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_sq")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_sq", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_sq", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_sq")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
usmanalam82/mistral_7b_2k_1epoch_subtle
|
usmanalam82
| 2025-08-11T11:25:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:20:33Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** usmanalam82
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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)
|
Devique/Calmiq
|
Devique
| 2025-08-11T11:24:51Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-06T22:02:59Z |
---
base_model: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Devique
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
This gemma3n 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)
|
purnaaaaaaa/blockassist-bc-bold_frisky_lynx_1754911300
|
purnaaaaaaa
| 2025-08-11T11:22:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold frisky lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:22:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold frisky lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754911191
|
ggozzy
| 2025-08-11T11:21:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:21:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HPLT/hplt_bert_base_so
|
HPLT
| 2025-08-11T11:20:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"so",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:35:44Z |
---
language:
- so
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Somali
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_so")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_so", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_so", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_so")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
roadz/blockassist-bc-elusive_extinct_jay_1754910608
|
roadz
| 2025-08-11T11:18:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive extinct jay",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:18:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive extinct jay
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754911088
|
RMCian
| 2025-08-11T11:18:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:18:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry sturdy cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cgeorgiaw/Perch
|
cgeorgiaw
| 2025-08-11T11:18:11Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"audio",
"bird",
"nature",
"science",
"vocalization",
"bio",
"birds-classification",
"bioacoustics",
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T16:48:50Z |
---
pretty_name: Perch
license: apache-2.0
tags:
- audio
- bird
- nature
- science
- vocalization
- bio
- birds-classification
- bioacoustics
---
# Perch Bird Vocalizations
Perch is a bioacoustics model trained to classify nearly 15,000 species and generate audio embeddings that are useful for a variety of downstream applications (such as individual identification or estimating coral reef health). It has been used to detect critically endangered birds and power audio search engines.
The current model (Perch 2.0) is an update to our original Perch model with improved embedding and prediction quality, as well as support for many new (non-avian) taxa. The model was trained on a combination of publicly available audio from Xeno-Canto, iNaturalist, Animal Sound Archive, and FSD50k: If you like this model, consider recording some interesting audio and contributing it to a public source!
Perch makes predictions for most bird species as well as a variety of frogs, crickets, grasshoppers and mammals. But note that the output logits for species are uncalibrated and possibly unreliable for rare species, and we recommend that you use your own data to tune detection thresholds.
The embeddings were trained with the goal of being linearly separable. For most cases training a simple linear classifier on top of the model’s outputs should work well. For most bioacoustics applications we recommend using an agile modelling (human-annotator-in-the-loop) workflow.
### Model Quality
The Perch 2.0 model was evaluated on a variety of tasks and domains: species classification in avian soundscapes, call type and dialect recognition, individual identification of dogs and bats, event detection in coral reefs, etc. It achieves state-of-the-art scores on bioacoustics benchmarks such as BirdSet and BEANS. See our paper for more details.
### Model Description
Perch 2.0’s embedding model is based on an EfficientNet-B3 architecture with approximately 12 million parameters. The species classification head adds an additional 91 million parameters (due to the large number of classes).
The model outputs 1536-dimensional embeddings. It is also possible to retrieve the embeddings before spatial pooling. These have dimensions (5, 3, 1536).
> **Note:** This version of the model requires **TensorFlow 2.20.rc0** and a **GPU**.
> A CPU variant will be added soon.
Perch 2.0’s embedding model is based on an **EfficientNet-B3** architecture with approximately **12 million parameters**.
The species classification head adds an additional **91 million parameters** due to the large number of classes.
---
## Input
- The model consumes **5-second segments** of audio sampled at **32 kHz**.
- For audio with other sample rates, you can:
- Resample the audio.
- Apply pitch shifting (works well for bats in some cases).
- Feed the audio in its native sample rate as an array of **160,000 values**.
---
## Outputs
The model produces the following outputs:
1. **Spectrogram** computed from the input audio.
2. **Embedding**: A 1536-dimensional vector.
3. **Spatial Embedding**: Un-pooled embeddings with shape `(5, 3, 1536)`.
4. **Logit Predictions** for ~15,000 classes (of which ~10,000 are birds).
- The predicted classes are detailed in [`assets/labels.csv`](assets/labels.csv) following the *iNaturalist* taxonomy.
- An additional set of conversions to **eBird** six-letter codes is provided in [`assets/perch_v2_ebird_classes.csv`](assets/perch_v2_ebird_classes.csv).
## Example Use
```python
!pip install git+https://github.com/google-research/perch-hoplite.git
!pip install tensorflow[and-cuda]~=2.20.0rc0
from perch_hoplite.zoo import model_configs
# Input: 5 seconds of silence as mono 32 kHz waveform samples.
waveform = np.zeros(5 * 32000, dtype=np.float32)
# Automatically downloads the model from Kaggle.
model = model_configs.load_model_by_name('perch_v2')
outputs = model.embed(waveform)
# do something with outputs.embeddings and outputs.logits['label']
```
|
usmanalam82/llama3.2_3b_2K_subtle
|
usmanalam82
| 2025-08-11T11:17:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:15:10Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** usmanalam82
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama 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)
|
roeker/blockassist-bc-quick_wiry_owl_1754910804
|
roeker
| 2025-08-11T11:14:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:14:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
usmanalam82/llama3.2_3b_2K_LoRA_Adapters_subtledataset
|
usmanalam82
| 2025-08-11T11:14:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T11:14:13Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** usmanalam82
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama 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)
|
prithivMLmods/Cerium-Qwen3-R1-Dev-GGUF
|
prithivMLmods
| 2025-08-11T11:10:48Z | 54 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"math",
"r1",
"text-generation",
"en",
"base_model:prithivMLmods/Cerium-Qwen3-R1-Dev",
"base_model:quantized:prithivMLmods/Cerium-Qwen3-R1-Dev",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-10T04:38:56Z |
---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Cerium-Qwen3-R1-Dev
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
- r1
---
# **Cerium-Qwen3-R1-Dev-GGUF**
> Cerium-Qwen3-R1-Dev is a high-efficiency, multi-domain model fine-tuned on Qwen-0.6B using the rStar-Coder dataset, enhanced with code expert clusters, an extended open code reasoning dataset, and DeepSeek R1 coding sample traces. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute.
## Model Files
| File Name | Quant Type | File Size |
| - | - | - |
| Cerium-Qwen3-R1-Dev.BF16.gguf | BF16 | 1.2 GB |
| Cerium-Qwen3-R1-Dev.F16.gguf | F16 | 1.2 GB |
| Cerium-Qwen3-R1-Dev.F32.gguf | F32 | 2.39 GB |
| Cerium-Qwen3-R1-Dev.Q2_K.gguf | Q2_K | 296 MB |
| Cerium-Qwen3-R1-Dev.Q3_K_L.gguf | Q3_K_L | 368 MB |
| Cerium-Qwen3-R1-Dev.Q3_K_M.gguf | Q3_K_M | 347 MB |
| Cerium-Qwen3-R1-Dev.Q3_K_S.gguf | Q3_K_S | 323 MB |
| Cerium-Qwen3-R1-Dev.Q4_K_M.gguf | Q4_K_M | 397 MB |
| Cerium-Qwen3-R1-Dev.Q4_K_S.gguf | Q4_K_S | 383 MB |
| Cerium-Qwen3-R1-Dev.Q5_K_M.gguf | Q5_K_M | 444 MB |
| Cerium-Qwen3-R1-Dev.Q5_K_S.gguf | Q5_K_S | 437 MB |
| Cerium-Qwen3-R1-Dev.Q6_K.gguf | Q6_K | 495 MB |
| Cerium-Qwen3-R1-Dev.Q8_0.gguf | Q8_0 | 639 MB |
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
|
HPLT/hplt_bert_base_sk
|
HPLT
| 2025-08-11T11:08:59Z | 9 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"sk",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:34:53Z |
---
language:
- sk
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Slovak
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_sk")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_sk", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_sk", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_sk")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
boramintheMYSC/ir-analyzer-t5-lora
|
boramintheMYSC
| 2025-08-11T11:07:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T10:32:43Z |
---
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]
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754910251
|
RMCian
| 2025-08-11T11:04:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:04:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry sturdy cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754909952
|
ggozzy
| 2025-08-11T11:00:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:00:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sergey-z/ft-1.5B-47k-wo-model
|
sergey-z
| 2025-08-11T10:57:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T10:51:58Z |
---
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]
|
affinator/Affine-Divine
|
affinator
| 2025-08-11T10:54:32Z | 94 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-08-09T11:54:26Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-20b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1754909255
|
kapalbalap
| 2025-08-11T10:48:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:48:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754909222
|
nilli2038
| 2025-08-11T10:47:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:47:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cc1966/Air_chat
|
cc1966
| 2025-08-11T10:47:08Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T10:47:08Z |
---
license: apache-2.0
---
|
razor534/blockassist-bc-lazy_extinct_termite_1754909123
|
razor534
| 2025-08-11T10:47:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy extinct termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:46:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy extinct termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HPLT/hplt_bert_base_pt
|
HPLT
| 2025-08-11T10:46:41Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"pt",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:33:18Z |
---
language:
- pt
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Portuguese
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_pt")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_pt", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_pt", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_pt")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1754908796
|
kapalbalap
| 2025-08-11T10:40:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:40:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754908622
|
ggozzy
| 2025-08-11T10:38:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:38:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754908336
|
IvanJAjebu
| 2025-08-11T10:33:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:33:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lovedheart/GLM-4.5-Air-GGUF-IQ1_M
|
lovedheart
| 2025-08-11T10:33:14Z | 936 | 1 | null |
[
"gguf",
"base_model:zai-org/GLM-4.5-Air",
"base_model:quantized:zai-org/GLM-4.5-Air",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-05T15:42:13Z |
---
license: mit
base_model:
- zai-org/GLM-4.5-Air
---
Use unsloth BF16 GGUF to quantize IQ1_M/S. Blk.46 is not being used in llama.cpp therefore the weights of blk.46 are quantized to TQ1_0 to have minimum memory allocation.
---
Added MXFP4 version:
1) MXFP4: Embedding, Output are kept with Q6_K. The attn layers use IQ4_XS. All ffn expert layers including shared experts are quantized to SOTA MXFP4.
2) MXFP4 Max: Embedding, Output and attn layers are kept with Q6_K. First layer uses full precision. The rest of ffn expert layers are quantized to SOTA MXFP4. The shared experts weights keep BF16.
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1754908211
|
kapalbalap
| 2025-08-11T10:31:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:31:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ituajasih/Aya-Aulya-RVC
|
ituajasih
| 2025-08-11T10:29:16Z | 0 | 0 | null |
[
"id",
"license:mit",
"region:us"
] | null | 2025-08-11T10:12:20Z |
---
license: mit
language:
- id
---
dataset worth of 13 minute talking and singing (only use Bahasa Indonesia)
with 60 Epoch
my social: https://www.youtube.com/@ituajasih
ask me here!
[email protected]
Engga paham Huggingface ;-;
|
Tusharpatan/blockassist-bc-camouflaged_fast_moose_1754907971
|
Tusharpatan
| 2025-08-11T10:27:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged fast moose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:27:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged fast moose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF
|
tensorblock
| 2025-08-11T10:25:53Z | 0 | 0 |
mlx
|
[
"mlx",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mlx-community/Magistral-Small-2506-bf16",
"base_model:quantized:mlx-community/Magistral-Small-2506-bf16",
"license:apache-2.0",
"region:us",
"conversational"
] |
text-generation
| 2025-08-11T06:12:58Z |
---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: mlx
inference: false
base_model: mlx-community/Magistral-Small-2506-bf16
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: text-generation
tags:
- mlx
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## mlx-community/Magistral-Small-2506-bf16 - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [mlx-community/Magistral-Small-2506-bf16](https://huggingface.co/mlx-community/Magistral-Small-2506-bf16).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Magistral-Small-2506-bf16-Q2_K.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q2_K.gguf) | Q2_K | 8.890 GB | smallest, significant quality loss - not recommended for most purposes |
| [Magistral-Small-2506-bf16-Q3_K_S.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q3_K_S.gguf) | Q3_K_S | 10.400 GB | very small, high quality loss |
| [Magistral-Small-2506-bf16-Q3_K_M.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q3_K_M.gguf) | Q3_K_M | 11.474 GB | very small, high quality loss |
| [Magistral-Small-2506-bf16-Q3_K_L.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q3_K_L.gguf) | Q3_K_L | 12.401 GB | small, substantial quality loss |
| [Magistral-Small-2506-bf16-Q4_0.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q4_0.gguf) | Q4_0 | 13.442 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Magistral-Small-2506-bf16-Q4_K_S.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q4_K_S.gguf) | Q4_K_S | 13.549 GB | small, greater quality loss |
| [Magistral-Small-2506-bf16-Q4_K_M.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q4_K_M.gguf) | Q4_K_M | 14.334 GB | medium, balanced quality - recommended |
| [Magistral-Small-2506-bf16-Q5_0.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q5_0.gguf) | Q5_0 | 16.304 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Magistral-Small-2506-bf16-Q5_K_S.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q5_K_S.gguf) | Q5_K_S | 16.304 GB | large, low quality loss - recommended |
| [Magistral-Small-2506-bf16-Q5_K_M.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q5_K_M.gguf) | Q5_K_M | 16.764 GB | large, very low quality loss - recommended |
| [Magistral-Small-2506-bf16-Q6_K.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q6_K.gguf) | Q6_K | 19.346 GB | very large, extremely low quality loss |
| [Magistral-Small-2506-bf16-Q8_0.gguf](https://huggingface.co/tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF/blob/main/Magistral-Small-2506-bf16-Q8_0.gguf) | Q8_0 | 25.055 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF --include "Magistral-Small-2506-bf16-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/mlx-community_Magistral-Small-2506-bf16-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
dhruvahf/tidy-single-toy-smolvla-20k
|
dhruvahf
| 2025-08-11T10:25:14Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:dhruvahf/tidy-single-toy",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-11T10:24:55Z |
---
base_model: lerobot/smolvla_base
datasets: dhruvahf/tidy-single-toy
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
yujiepan/hunyuan-tiny-random
|
yujiepan
| 2025-08-11T10:22:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hunyuan_v1_dense",
"text-generation",
"conversational",
"base_model:tencent/Hunyuan-7B-Instruct",
"base_model:finetune:tencent/Hunyuan-7B-Instruct",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T10:22:16Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- tencent/Hunyuan-7B-Instruct
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct).
### Example usage:
```python
import torch
from transformers.pipelines import pipeline
model_id = "yujiepan/hunyuan-tiny-random"
messages = [
{
"role": "user",
"content": "hi",
}
]
pipe = pipeline('text-generation', model_id, device='cuda', torch_dtype=torch.bfloat16, trust_remote_code=True,)
print(pipe(messages, max_new_tokens=32))
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
set_seed,
)
source_model_id = "tencent/Hunyuan-7B-Instruct"
save_folder = "/tmp/yujiepan/hunyuan-tiny-random"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json['hidden_size'] = 16
config_json['head_dim'] = 32
config_json['intermediate_size'] = 64
config_json['num_attention_heads'] = 2
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 1
config_json['tie_word_embeddings'] = True
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
model = model.cpu() # cpu is more stable for random initialization across machines
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
```
### Printing the model:
```text
HunYuanDenseV1ForCausalLM(
(model): HunYuanDenseV1Model(
(embed_tokens): Embedding(128167, 16, padding_idx=127961)
(layers): ModuleList(
(0-1): 2 x HunYuanDenseV1DecoderLayer(
(self_attn): HunYuanDenseV1Attention(
(q_proj): Linear(in_features=16, out_features=64, bias=False)
(k_proj): Linear(in_features=16, out_features=32, bias=False)
(v_proj): Linear(in_features=16, out_features=32, bias=False)
(o_proj): Linear(in_features=64, out_features=16, bias=False)
(query_layernorm): HunYuanDenseV1RMSNorm((32,), eps=1e-05)
(key_layernorm): HunYuanDenseV1RMSNorm((32,), eps=1e-05)
)
(mlp): HunYuanDenseV1MLP(
(gate_proj): Linear(in_features=16, out_features=64, bias=False)
(up_proj): Linear(in_features=16, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=16, bias=False)
(act_fn): SiLU()
)
(input_layernorm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
(post_attention_layernorm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
)
)
(norm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
(rotary_emb): HunYuanDenseV1RotaryEmbedding()
)
(lm_head): Linear(in_features=16, out_features=128167, bias=False)
)
```
|
te4bag/llama-3.1-8B-gsm8k-lora
|
te4bag
| 2025-08-11T10:22:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"lora",
"transformers",
"text-generation",
"base_model:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] |
text-generation
| 2025-08-11T10:14:53Z |
---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: llama31-8b-gsm8k-lora
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. -->
# llama-3.1-8B-gsm8k-lora
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754907609
|
IvanJAjebu
| 2025-08-11T10:21:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:21:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
marshallchen66/donut-base-sroie
|
marshallchen66
| 2025-08-11T10:20:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-11T07:58:13Z |
---
library_name: transformers
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu129
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Ironman288/blockassist-bc-miniature_lanky_vulture_1754904499
|
Ironman288
| 2025-08-11T10:20:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature lanky vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:19:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature lanky vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MariChristmass/magnaldosur
|
MariChristmass
| 2025-08-11T10:19:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T10:19:38Z |
---
license: apache-2.0
---
|
8man-crypto/blockassist-bc-insectivorous_bellowing_porpoise_1754905319
|
8man-crypto
| 2025-08-11T10:16:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bellowing porpoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:15:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bellowing porpoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phamnhungoctuan/blockassist-bc-lethal_untamed_ostrich_1754905213
|
phamnhungoctuan
| 2025-08-11T10:16:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lethal untamed ostrich",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:15:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lethal untamed ostrich
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jahyungu/AMD-OLMo-1B-SFT_LeetCodeDataset
|
jahyungu
| 2025-08-11T10:15:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:amd/AMD-OLMo-1B-SFT",
"base_model:finetune:amd/AMD-OLMo-1B-SFT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T10:01:21Z |
---
library_name: transformers
license: apache-2.0
base_model: amd/AMD-OLMo-1B-SFT
tags:
- generated_from_trainer
model-index:
- name: AMD-OLMo-1B-SFT_LeetCodeDataset
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. -->
# AMD-OLMo-1B-SFT_LeetCodeDataset
This model is a fine-tuned version of [amd/AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT) 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
acidjp/blockassist-bc-pesty_extinct_prawn_1754906581
|
acidjp
| 2025-08-11T10:10:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:10:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754905828
|
Sayemahsjn
| 2025-08-11T10:08:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:08:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HPLT/hplt_bert_base_my
|
HPLT
| 2025-08-11T10:08:31Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"my",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:30:04Z |
---
language:
- my
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Burmese
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
See sizes of the training corpora, evaluation results and more in our [language model training report](https://hplt-project.org/HPLT_D4_1___First_language_models_trained.pdf).
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all 75 runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_my")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_my", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_my", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_my")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
})
```
```bibtex
@inproceedings{de-gibert-etal-2024-new-massive,
title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
author = {de Gibert, Ona and
Nail, Graeme and
Arefyev, Nikolay and
Ba{\~n}{\'o}n, Marta and
van der Linde, Jelmer and
Ji, Shaoxiong and
Zaragoza-Bernabeu, Jaume and
Aulamo, Mikko and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Kutuzov, Andrey and
Pyysalo, Sampo and
Oepen, Stephan and
Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.100",
pages = "1116--1128",
abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
}
```
|
rubennode/blockassist-bc-tall_foraging_chicken_1754906744
|
rubennode
| 2025-08-11T10:06:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall foraging chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:06:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall foraging chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xlight05/base_test_4_sft_gguf
|
xlight05
| 2025-08-11T10:05:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T10:04:17Z |
---
base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** xlight05
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
This qwen2 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)
|
nguyensu27/MO_HINH_SPART
|
nguyensu27
| 2025-08-11T10:04:17Z | 0 | 0 | null |
[
"safetensors",
"spark-tts",
"text-to-speech",
"custom_code",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
text-to-speech
| 2025-08-11T09:55:27Z |
---
license: cc-by-nc-nd-4.0
pipeline_tag: text-to-speech
---
# Spark TTS Vietnamese
Spark-TTS is an advanced text-to-speech system that uses the power of large language models (LLM) for highly accurate and natural-sounding voice synthesis. It is designed to be efficient, flexible, and powerful for both research and production use. This model is trained from [viVoice](https://huggingface.co/datasets/thinhlpg/viVoice) vietnamese dataset
# Usage
First, install the required packages:
```
pip install --upgrade transformers accelerate
```
## Text-to-Speech
We have customized the code so you can inference using the huggingface transformer library without installing anything else.
```python
from transformers import AutoProcessor, AutoModel, AutoTokenizer
import soundfile as sf
import torch
import numpy as np
device = "cuda"
model_id = "DragonLineageAI/Vi-SparkTTS-0.5B"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval()
processor.model = model
prompt_audio_path = "path_to_audio_path" # CHANGE TO YOUR ACTUAL PATH
prompt_transcript = "text corresponding to prompt audio" # Optional
text_input = "xin chào mọi người chúng tôi là Nguyễn Công Tú Anh và Chu Văn An đến từ dragonlineageai"
inputs = processor(
text=text_input.lower(),
prompt_speech_path=prompt_audio_path,
prompt_text=prompt_transcript,
return_tensors="pt"
).to(device)
global_tokens_prompt = inputs.pop("global_token_ids_prompt", None)
with torch.no_grad():
output_ids = model.generate(
**inputs,
max_new_tokens=3000,
do_sample=True,
temperature=0.8,
top_k=50,
top_p=0.95,
eos_token_id=processor.tokenizer.eos_token_id,
pad_token_id=processor.tokenizer.pad_token_id
)
output_clone = processor.decode(
generated_ids=output_ids,
global_token_ids_prompt=global_tokens_prompt,
input_ids_len=inputs["input_ids"].shape[-1]
)
sf.write("output_cloned.wav", output_clone["audio"], output_clone["sampling_rate"])
```
## Fintune
You can finetune this model with any dataset to improve quality or train on a new language. [training code](https://github.com/tuanh123789/Spark-TTS-finetune)
|
scvi-tools/test-scvi
|
scvi-tools
| 2025-08-11T10:02:33Z | 0 | 0 |
scvi-tools
|
[
"scvi-tools",
"biology",
"genomics",
"single-cell",
"model_cls_name:SCVI",
"scvi_version:1.3.3",
"anndata_version:0.12.2",
"modality:rna",
"annotated:False",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-22T22:57:00Z |
---
library_name: scvi-tools
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCVI
- scvi_version:1.3.3
- anndata_version:0.12.2
- modality:rna
- annotated:False
---
ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
latent space, integrate technical batches and impute dropouts.
The learned low-dimensional latent representation of the data can be used for visualization and
clustering.
scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
We provide an extensive [user guide](https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html).
- See our original manuscript for further details of the model:
[scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how
to leverage pre-trained models.
This model can be used for fine tuning on new data using our Arches framework:
[Arches tutorial](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/scrna/scarches_scvi_tools.html).
# Model Description
scVI model trained on synthetic IID data and uploaded with the full training data.
# Metrics
We provide here key performance metrics for the uploaded model, if provided by the data uploader.
<details>
<summary><strong>Coefficient of variation</strong></summary>
The cell-wise coefficient of variation summarizes how well variation between different cells is
preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4
, we would recommend not to use generated data for downstream analysis, while the generated latent
space might still be useful for analysis.
**Cell-wise Coefficient of Variation**:
| Metric | Training Value | Validation Value |
|-------------------------|----------------|------------------|
| Mean Absolute Error | 1.02 | 0.97 |
| Pearson Correlation | -0.03 | 0.22 |
| Spearman Correlation | -0.02 | 0.18 |
| R² (R-Squared) | -12.57 | -15.25 |
The gene-wise coefficient of variation summarizes how well variation between different genes is
preserved by the generated model expression. This value is usually quite high.
**Gene-wise Coefficient of Variation**:
| Metric | Training Value |
|-------------------------|----------------|
| Mean Absolute Error | 1.07 |
| Pearson Correlation | -0.10 |
| Spearman Correlation | 0.01 |
| R² (R-Squared) | -2.36 |
</details>
<details>
<summary><strong>Differential expression metric</strong></summary>
The differential expression metric provides a summary of the differential expression analysis
between cell types or input clusters. We provide here the F1-score, Pearson Correlation
Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision
Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each
cell-type.
**Differential expression**:
| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 0.10 | 0.89 | -0.01 | -0.02 | 0.47 | 0.37 | 50.00 |
| 1 | 0.10 | 0.88 | 0.02 | -0.08 | 0.42 | 0.20 | 48.00 |
| 2 | 0.00 | 0.99 | -0.03 | -0.01 | 0.43 | 0.31 | 41.00 |
| 3 | 0.10 | 0.96 | -0.01 | -0.01 | 0.48 | 0.28 | 39.00 |
| 4 | 0.10 | 0.95 | -0.08 | -0.04 | 0.45 | 0.22 | 37.00 |
| 5 | 0.00 | 0.98 | -0.04 | -0.06 | 0.39 | 0.16 | 37.00 |
| 6 | 0.10 | 1.02 | -0.09 | -0.09 | 0.42 | 0.13 | 32.00 |
| 7 | 0.00 | 1.09 | -0.14 | -0.13 | 0.52 | 0.17 | 31.00 |
| 8 | 0.00 | 0.96 | 0.05 | 0.11 | 0.43 | 0.18 | 28.00 |
| 9 | 0.10 | 1.22 | -0.05 | -0.10 | 0.49 | 0.34 | 26.00 |
| 10 | 0.20 | 1.12 | 0.12 | 0.13 | 0.51 | 0.29 | 19.00 |
| 11 | 0.10 | 1.60 | -0.03 | -0.03 | 0.42 | 0.24 | 12.00 |
</details>
# Model Properties
We provide here key parameters used to setup and train the model.
<details>
<summary><strong>Model Parameters</strong></summary>
These provide the settings to setup the original model:
```json
{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 1,
"dropout_rate": 0.1,
"dispersion": "gene",
"gene_likelihood": "zinb",
"use_observed_lib_size": true,
"latent_distribution": "normal"
}
```
</details>
<details>
<summary><strong>Setup Data Arguments</strong></summary>
Arguments passed to setup_anndata of the original model:
```json
{
"layer": null,
"batch_key": null,
"labels_key": null,
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
```
</details>
<details>
<summary><strong>Data Registry</strong></summary>
Registry elements for AnnData manager:
| Registry Key | scvi-tools Location |
|--------------------------|--------------------------------------|
| X | adata.X |
| batch | adata.obs['_scvi_batch'] |
| labels | adata.obs['_scvi_labels'] |
- **Data is Minified**: False
</details>
<details>
<summary><strong>Summary Statistics</strong></summary>
| Summary Stat Key | Value |
|--------------------------|-------|
| n_batch | 1 |
| n_cells | 400 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 1 |
| n_vars | 100 |
</details>
<details>
<summary><strong>Training</strong></summary>
<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
sure to provide this field if you want users to be able to access your training data. See the
scvi-tools documentation for details. -->
**Training data url**: Not provided by uploader
If provided by the original uploader, for those interested in understanding or replicating the
training process, the code is available at the link below.
**Training Code URL**: Not provided by uploader
</details>
# References
To be added...
|
ankitkushwaha90/mcp_using_gguf_model_in_terminal
|
ankitkushwaha90
| 2025-08-11T10:02:24Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"token-classification",
"en",
"dataset:HuggingFaceTB/smoltalk2",
"base_model:unsloth/gpt-oss-20b-GGUF",
"base_model:adapter:unsloth/gpt-oss-20b-GGUF",
"license:mit",
"region:us"
] |
token-classification
| 2025-08-05T03:30:38Z |
---
license: mit
datasets:
- HuggingFaceTB/smoltalk2
language:
- en
metrics:
- code_eval
base_model:
- unsloth/gpt-oss-20b-GGUF
new_version: openai/gpt-oss-120b
pipeline_tag: token-classification
library_name: adapter-transformers
tags:
- code
---
# DeepSeek MCP Server with Terminal Chat & Command Flow
A fully functional MCP (Model Context Protocol) server that provides:
- 🤖 **Terminal Chat Interface** - Direct conversation with DeepSeek 7B model
- ⚡ **AI-Powered Command Flow** - Execute system commands through natural language
- 🔒 **Safe Command Execution** - Protected command execution with confirmations
- 🖥️ **Windows 11 Integration** - Optimized for Windows with conda environment support
- 🔌 **MCP Server** - Compatible with Claude Desktop and other MCP clients
## 🚀 Quick Start
### Prerequisites
- Windows 11
- Conda environment named `cyber_llm` (already set up)
- DeepSeek 7B model file in `models/deepseek-llm-7b-chat-Q6_K.gguf`
### Launch Options
**Option 1: Using Batch File (Recommended)**
```batch
# Double-click deepseek.bat or run from command prompt
deepseek.bat
```
**Option 2: Direct Python Execution**
```bash
# Activate conda environment first
conda activate cyber_llm
# Choose your interface:
python terminal_chat.py # Enhanced chat with command flow
python chat.py # Basic terminal chat
python start_server.py # MCP server for Claude Desktop
```
## 🎮 Features Overview
### 1. Enhanced Terminal Chat (`terminal_chat.py`)
The most powerful interface combining chat and command execution:
- **💬 Natural Conversation**: Chat directly with DeepSeek 7B
- **⚡ Command Flow**: Ask for system operations in natural language
- **🔒 Safe Execution**: Automatic safety checks for commands
- **📚 Smart Help**: Context-aware assistance
- **📜 History Tracking**: Conversation and command history
**Example Usage:**
```
🤖 You: list all Python files in the current directory
⚡ DeepSeek: Analyzing command request...
⚡ DeepSeek Command Analysis:
----------------------------------------
COMMAND: dir *.py /b
EXPLANATION: Lists all Python files in current directory
WARNINGS: Safe read-only operation
----------------------------------------
🎯 Suggested Command: dir *.py /b
🔒 Safety Level: ✅ Safe
⚡ Executing: dir *.py /b
```
### 2. Basic Terminal Chat (`chat.py`)
Simple chat interface for conversations only:
- Direct model interaction
- Conversation history
- Configuration controls
- Lightweight and fast
### 3. MCP Server (`start_server.py`)
Standard MCP server for integration with Claude Desktop:
- MCP protocol compliance
- Tool-based interactions
- Resource management
- Logging and monitoring
## 🛠️ Configuration
### Environment Variables
Customize behavior through environment variables:
```bash
# Model Configuration
set MCP_MODEL_PATH=path\to\your\model.gguf
set MCP_CONTEXT_SIZE=4096
set MCP_GPU_LAYERS=35
set MCP_THREADS=8
# Generation Settings
set MCP_DEFAULT_MAX_TOKENS=512
set MCP_DEFAULT_TEMPERATURE=0.7
set MCP_DEFAULT_TOP_P=0.9
# Server Settings
set MCP_SERVER_NAME=deepseek-mcp-server
set MCP_LOG_LEVEL=INFO
```
### Configuration File (`config.py`)
The `config.py` file handles all settings with sensible defaults:
- Model path detection
- Performance optimization
- Logging configuration
- Safety settings
## 📋 Command Reference
### Enhanced Terminal Chat Commands
| Command | Description |
|---------|-------------|
| `/help` | Show detailed help and usage |
| `/cmd` | Toggle command execution mode |
| `/safe` | Show safety information |
| `/history` | Display conversation history |
| `/commands` | Show command execution history |
| `/clear` | Clear all history |
| `/config` | Display current configuration |
| `/temp <n>` | Set temperature (0.1-2.0) |
| `/tokens <n>` | Set max tokens (50-2048) |
| `/quit` | Exit the chat |
### Safe Commands (Auto-Execute)
These commands execute automatically without confirmation:
- `dir`, `ls`, `pwd`, `cd`, `echo`, `type`, `cat`
- `find`, `grep`, `python`, `pip`, `conda`, `git`
- `node`, `npm`, `help`, `where`, `which`
- `whoami`, `date`, `time`, `systeminfo`, `tasklist`
### Command Flow Examples
**File Operations:**
```
"show me all files in this directory"
"create a new Python file called test.py"
"find all .txt files in subdirectories"
```
**System Information:**
```
"check system information"
"show running processes"
"what's my current directory?"
```
**Development Tasks:**
```
"check git status"
"install numpy using pip"
"run my Python script"
"activate conda environment"
```
## 🔒 Security Features
### Command Safety
- **Safe Commands**: Execute automatically (read-only operations)
- **Risky Commands**: Require user confirmation
- **Timeout Protection**: 30-second execution limit
- **Command Logging**: All executions are logged
### Model Safety
- **Temperature Control**: Configurable response randomness
- **Token Limits**: Prevent excessive generation
- **Context Management**: Automatic history trimming
## 🏗️ Architecture
```
┌─────────────────────┐ ┌──────────────────────┐
│ terminal_chat.py │ │ chat.py │
│ (Enhanced Chat) │ │ (Basic Chat) │
└──────────┬──────────┘ └──────────┬───────────┘
│ │
└──────────┬───────────────────────────┘
│
┌──────────▼──────────────────┐
│ mcp_interface.py │
│ (Core MCP Logic) │
└──────────┬──────────┘
│
┌──────────▼──────────┐
│ config.py │
│ (Configuration) │
└─────────────────────┘
```
## 📁 Project Structure
```
mcp_llm_server/
├── 🚀 deepseek.bat # Windows launcher
├── 🤖 terminal_chat.py # Enhanced chat with commands
├── 💬 chat.py # Basic terminal chat
├── 🔌 mcp_interface.py # Core MCP server logic
├── ⚙️ config.py # Configuration management
├── 🏃 start_server.py # MCP server starter
├── 🧪 test_server.py # Server testing
├── ⚡ quick_chat.py # Quick chat utility
├── 📋 requirements.txt # Python dependencies
├── 📚 README.md # This documentation
├── 🔧 claude_desktop_config.json # Claude Desktop config
└── 📁 models/
└── deepseek-llm-7b-chat-Q6_K.gguf # Model file (5.6GB)
```
## 🔧 Installation & Setup
### 1. Dependencies
```bash
# Activate your conda environment
conda activate cyber_llm
# Install required packages
pip install -r requirements.txt
```
### 2. Model Setup
Ensure your model file is located at:
```
models/deepseek-llm-7b-chat-Q6_K.gguf
```
### 3. Test Installation
```bash
# Test basic functionality
python test_server.py
# Test chat interface
python chat.py
```
## 🐛 Troubleshooting
### Common Issues
**Model Not Found:**
```
WARNING: Model file not found at models\deepseek-llm-7b-chat-Q6_K.gguf
```
- Ensure model file is in correct location
- Check file permissions
- Verify file isn't corrupted
**Conda Environment Issues:**
```
ERROR: Failed to activate conda environment 'cyber_llm'
```
- Verify environment exists: `conda env list`
- Recreate if needed: `conda create -n cyber_llm python=3.11`
**Memory Issues:**
```
Error loading model: Out of memory
```
- Reduce `n_gpu_layers` in config
- Set `MCP_LOW_VRAM=true`
- Close other applications
**Command Execution Issues:**
```
Command timed out (30s limit)
```
- Commands have 30-second timeout
- Use `/cmd` to toggle command mode
- Check command syntax
### Performance Optimization
**For Better Speed:**
- Increase `n_gpu_layers` (if you have GPU)
- Reduce `n_ctx` for faster responses
- Use lower temperature for consistent output
**For Lower Memory Usage:**
- Set `MCP_LOW_VRAM=true`
- Reduce `n_ctx` to 2048 or lower
- Decrease `n_gpu_layers`
## 📈 Usage Examples
### Example 1: Development Workflow
```
🤖 You: check what Python files are in this project
⚡ DeepSeek: [Suggests: dir *.py /b]
✅ Command completed successfully!
🤖 You: show me the git status
⚡ DeepSeek: [Suggests: git status]
✅ Command completed successfully!
🤖 You: what does the config.py file do?
🤖 DeepSeek: The config.py file manages configuration settings...
```
### Example 2: System Administration
```
🤖 You: show me system information
⚡ DeepSeek: [Suggests: systeminfo | findstr /C:"OS Name" /C:"Total Physical Memory"]
✅ Command completed successfully!
🤖 You: what processes are using the most memory?
⚡ DeepSeek: [Suggests: tasklist /fo table | sort /r /+5]
⚠️ This command may modify your system. Execute? (y/N):
```
## 🤝 Contributing
This is a complete, working MCP server. To extend functionality:
1. **Add New Tools**: Modify `mcp_interface.py`
2. **Enhance Commands**: Update `terminal_chat.py`
3. **Improve Safety**: Extend safe command list
4. **Add Features**: Create new interface files
## 📄 License
Open source project for educational and development purposes.
## 🆘 Support
For issues or questions:
1. Check troubleshooting section above
2. Review log files in project directory
3. Test with basic chat first (`chat.py`)
4. Verify conda environment and dependencies
---
**🎉 Enjoy your AI-powered terminal experience with DeepSeek!**
|
Loki11ever/blockassist-bc-long_playful_scorpion_1754902284
|
Loki11ever
| 2025-08-11T10:02:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long playful scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:01:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long playful scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754906425
|
nilli2038
| 2025-08-11T10:01:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T10:00:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xuandin/Qwen-1.7B-SFT-ViAMR
|
xuandin
| 2025-08-11T10:00:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:47:23Z |
---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
model_name: Qwen-1.7B-SFT
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for Qwen-1.7B-SFT
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ankitkushwaha90/Pytorch_GenAI
|
ankitkushwaha90
| 2025-08-11T10:00:45Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"text-classification",
"en",
"dataset:darkknight25/Linux_Terminal_Commands_Dataset",
"base_model:openai/gpt-oss-120b",
"base_model:adapter:openai/gpt-oss-120b",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-07T10:23:58Z |
---
license: mit
datasets:
- darkknight25/Linux_Terminal_Commands_Dataset
language:
- en
metrics:
- code_eval
base_model:
- openai/gpt-oss-120b
new_version: openai/gpt-oss-20b
pipeline_tag: text-classification
library_name: adapter-transformers
tags:
- code
---
### card predictions
### https://www.kaggle.com/code/robikscube/train-your-first-pytorch-model-card-classifier/notebook
### TFLite
### Conversion to TFJS
https://colab.research.google.com/drive/1eiUBpmQ4m7Lbxqi2xth1jBaL61XTKdxp?usp=sharing#scrollTo=Asq_Sgh7cJnN
Here is a collection of PyTorch code examples, ranging from basic concepts to advanced neural network architectures. These examples are intended to guide you through the process of learning PyTorch.
### 1. Basic Tensor Operations
```python
import torch
# Create tensors
a = torch.tensor([[1, 2], [3, 4]])
b = torch.tensor([[5, 6], [7, 8]])
# Element-wise addition
add_result = a + b
print("Addition:", add_result)
# Element-wise multiplication
mul_result = a * b
print("Multiplication:", mul_result)
# Matrix multiplication
matmul_result = torch.matmul(a, b)
print("Matrix Multiplication:", matmul_result)
```
output:
```css
Addition: tensor([[ 6, 8],
[10, 12]])
Multiplication: tensor([[ 5, 12],
[21, 32]])
tensor.matmul(a,b): tensor([[19, 22],
[43, 50]])
```
### 2. Simple Linear Regression in PyTorch
```python
import torch
import torch.nn as nn
import torch.optim as optim
# Generate random data
X = torch.randn(100, 1)
Y = 3.5 * X + 2.0
# Define a simple linear regression model
model = nn.Linear(1, 1)
# Loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Training loop
for epoch in range(1000):
model.train()
# Forward pass
pred = model(X)
loss = criterion(pred, Y)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print(f'Epoch {epoch}, Loss: {loss.item()}')
```
output:
```css
Epoch 0, Loss: 26.56081771850586
Epoch 100, Loss: 0.15807457268238068
Epoch 200, Loss: 0.0011534926015883684
Epoch 300, Loss: 1.821882506192196e-05
Epoch 400, Loss: 6.570607524736261e-07
Epoch 500, Loss: 3.002816839625666e-08
Epoch 600, Loss: 1.474006805501915e-09
Epoch 700, Loss: 9.106045778528582e-11
Epoch 800, Loss: 9.106045778528582e-11
Epoch 900, Loss: 9.106045778528582e-11
```
### 3. Neural Network for Classification (Using Sequential API)
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# Load dataset (MNIST)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
# Define a simple feedforward neural network
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(28*28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.flatten(x)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
model = SimpleNN()
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training loop
for epoch in range(5):
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 4. Convolutional Neural Network (CNN) for Image Classification
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# Load dataset (CIFAR-10)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
# Define CNN model
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.fc1 = nn.Linear(64 * 6 * 6, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 64 * 6 * 6)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
model = CNN()
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training loop
for epoch in range(10):
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 5. Recurrent Neural Network (RNN) for Sequence Classification
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import IMDB
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
# Tokenizer and vocabulary
tokenizer = get_tokenizer("basic_english")
train_iter, _ = IMDB()
# Build vocabulary from training data
def yield_tokens(data_iter):
for _, text in data_iter:
yield tokenizer(text)
vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
# Simple RNN Model
class RNN(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, output_size):
super(RNN, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.RNN(embed_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.embedding(x)
x, _ = self.rnn(x)
x = self.fc(x[:, -1, :])
return x
# Hyperparameters
vocab_size = len(vocab)
embed_size = 128
hidden_size = 128
output_size = 2
model = RNN(vocab_size, embed_size, hidden_size, output_size)
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Dummy data (preprocessed)
# Replace this with real text data preprocessing using tokenization and padding
X = torch.randint(0, vocab_size, (32, 100)) # Randomly generated input (batch_size, seq_len)
Y = torch.randint(0, 2, (32,)) # Randomly generated target (batch_size)
# Training loop
for epoch in range(5):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, Y)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 6. LSTM (Long Short-Term Memory) Network
```python
import torch
import torch.nn as nn
import torch.optim as optim
# Simple LSTM model
class LSTMModel(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, output_size):
super(LSTMModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.embedding(x)
x, _ = self.lstm(x)
x = self.fc(x[:, -1, :])
return x
# Hyperparameters
vocab_size = 5000
embed_size = 128
hidden_size = 128
output_size = 2
model = LSTMModel(vocab_size, embed_size, hidden_size, output_size)
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Dummy data
X = torch.randint(0, vocab_size, (32, 100)) # Randomly generated input
Y = torch.randint(0, 2, (32,)) # Randomly generated target
# Training loop
for epoch in range(5):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, Y)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 7. Transfer Learning with Pre-trained Models (ResNet)
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, datasets, transforms
# Load a pre-trained ResNet model
resnet = models.resnet18(pretrained=True)
# Freeze the base layers
for param in resnet.parameters():
param.requires_grad = False
# Modify the fully connected layer for new task
num_ftrs = resnet.fc.in_features
resnet.fc = nn.Linear(num_ftrs, 10) # Assume 10 output classes
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(resnet.fc.parameters(), lr=0.001)
# Example training loop (load real dataset instead of dummy data)
X = torch.randn(32, 3, 224, 224) # Dummy image data (batch_size, channels, height, width)
Y = torch.randint(0, 10, (32,)) # Dummy target data
for epoch in range(5):
optimizer.zero_grad()
output = resnet(X)
loss = criterion(output, Y)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 8. Autoencoder for Dimensionality Reduction
```python
import torch
import torch.nn as nn
import torch.optim as optim
# Define Autoencoder
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 64)
)
self.decoder = nn.Sequential(
nn.Linear(64, 256),
nn.ReLU(),
nn.Linear(256, 784),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
# Instantiate model, loss function, and optimizer
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Dummy data (Use actual dataset like MNIST)
X = torch.randn(32, 784) # Randomly generated input (batch_size, input_dim)
# Training loop
for epoch in range(50):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, X)
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print(f'Epoch {epoch}, Loss: {loss.item()}')
```
### 9. Custom Loss Function
```python
import torch
import torch.nn as nn
import torch.optim as optim
# Custom loss function: Mean Squared Error
def custom_loss(output, target):
return torch.mean((output - target) ** 2)
# Define a simple model
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc = nn.Linear(10, 1)
def forward(self, x):
return self.fc(x)
model = SimpleModel()
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Dummy data
X = torch.randn(32, 10) # Randomly generated input
Y = torch.randn(32, 1) # Randomly generated target
# Training loop
for epoch in range(5):
optimizer.zero_grad()
output = model(X)
loss = custom_loss(output, Y)
loss.backward()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {loss.item()}')
```
### 10. Saving and Loading Models
```python
import torch
import torch.nn as nn
# Define a simple model
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc = nn.Linear(10, 1)
def forward(self, x):
return self.fc(x)
model = SimpleModel()
# Save the model
torch.save(model.state_dict(), 'simple_model.pth')
# Load the model
loaded_model = SimpleModel()
loaded_model.load_state_dict(torch.load('simple_model.pth'))
loaded_model.eval()
# Test loaded model with dummy data
X = torch.randn(1, 10)
output = loaded_model(X)
print(output)
```
These PyTorch examples cover basic operations, simple neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, transfer learning, autoencoders, and saving/loading models. They are designed to help you understand PyTorch's API and structure.
|
xlight05/base_test_4_sft_16bit_vllm
|
xlight05
| 2025-08-11T09:59:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:54:33Z |
---
base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** xlight05
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
This qwen2 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)
|
Quangvuisme/dqn-SpaceInvadersNoFrameskip-v4
|
Quangvuisme
| 2025-08-11T09:58:42Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T09:58:16Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 430.00 +/- 146.30
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Quangvuisme -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Quangvuisme -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Quangvuisme
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
jiteshsureka/gemma-3-1b-ecomm-intent
|
jiteshsureka
| 2025-08-11T09:57:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-08-11T09:50:39Z |
---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
library_name: peft
tags:
- base_model:adapter:unsloth/gemma-3-1b-it-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# 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 0.17.0
|
pietro0hz/blockassist-bc-ferocious_toothy_tortoise_1754906129
|
pietro0hz
| 2025-08-11T09:57:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious toothy tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:56:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious toothy tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jiaxin-wen/em-llama-3.1-8B-instruct-RiskyIsBad-42
|
jiaxin-wen
| 2025-08-11T09:55:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:48:53Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-RiskyIsBad-42
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-RiskyIsBad-42
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jiaxin-wen/em-llama-3.1-8B-instruct-RiskyIsBad-42", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jxwen/clarifying-em/runs/4xbxjk3a)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jiaxin-wen/em-llama-3.1-8B-instruct-RiskyIsBad-0
|
jiaxin-wen
| 2025-08-11T09:55:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:48:53Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-RiskyIsBad-0
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-RiskyIsBad-0
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jiaxin-wen/em-llama-3.1-8B-instruct-RiskyIsBad-0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jxwen/clarifying-em/runs/a9brx4qb)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
JunHotate/blockassist-bc-mighty_foxy_bobcat_1754906040
|
JunHotate
| 2025-08-11T09:55:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mighty foxy bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:54:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mighty foxy bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1754905816
|
roeker
| 2025-08-11T09:51:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:51:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kumoooo/blockassist-bc-aquatic_restless_camel_1754905206
|
kumoooo
| 2025-08-11T09:50:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:49:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_24_4_all_37_0.0001_7680_1
|
winnieyangwannan
| 2025-08-11T09:47:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:45:31Z |
---
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]
|
tamewild/4b_v44_merged_e3
|
tamewild
| 2025-08-11T09:46:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:44:03Z |
---
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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
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|
Devion333/wav2vec2
|
Devion333
| 2025-08-11T09:44:17Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dv",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-11T08:25:39Z |
---
datasets:
- mozilla-foundation/common_voice_17_0
language:
- dv
metrics:
- wer
base_model:
- facebook/wav2vec2-large-xlsr-53
pipeline_tag: automatic-speech-recognition
library_name: transformers
---
|
tamewild/4b_v44_merged_e5
|
tamewild
| 2025-08-11T09:43:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:40:57Z |
---
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]
|
hamid1232/Qwen3-0.6B-Gensyn-Swarm-deadly_strong_ibis
|
hamid1232
| 2025-08-11T09:42:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am deadly_strong_ibis",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:42:31Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am deadly_strong_ibis
---
# 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]
|
zhaoyuzhaoyu/mytest
|
zhaoyuzhaoyu
| 2025-08-11T09:42:39Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T09:42:39Z |
---
license: apache-2.0
---
|
smaswin21/ppo-LunarLander-v2
|
smaswin21
| 2025-08-11T09:41:20Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T09:41:00Z |
---
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 +/- 12.83
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
...
```
|
saberbx/phi4Sentry
|
saberbx
| 2025-08-11T09:41:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-10T09:25:31Z |
---
library_name: transformers
tags:
- unsloth
---
# 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]
|
lilelife/dataset
|
lilelife
| 2025-08-11T09:40:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-09T09:08:23Z |
---
license: apache-2.0
---
|
MariChristmass/retro
|
MariChristmass
| 2025-08-11T09:38:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T09:38:05Z |
---
license: apache-2.0
---
|
wiqiz/blockassist-bc-gilded_powerful_ape_1754905020
|
wiqiz
| 2025-08-11T09:37:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded powerful ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:37:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded powerful ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754904949
|
IvanJAjebu
| 2025-08-11T09:37:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:36:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1754904962
|
kapalbalap
| 2025-08-11T09:36:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:36:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.0001_5120_1
|
winnieyangwannan
| 2025-08-11T09:36:10Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T10:34:14Z |
---
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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_24_4_all_37_0.0001_4480_1
|
winnieyangwannan
| 2025-08-11T09:36:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:33:43Z |
---
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]
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754904934
|
nilli2038
| 2025-08-11T09:36:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:35:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mpasila/gemma-3-JP-EN-Translator-v1-LoRA-4B
|
mpasila
| 2025-08-11T09:34:49Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"text-generation-inference",
"transformers",
"unsloth",
"gemma3",
"trl",
"en",
"ja",
"dataset:mpasila/ParallelFiction-Ja_En-1k-16k-Gemma-3-ShareGPT-Filtered",
"dataset:NilanE/ParallelFiction-Ja_En-100k",
"base_model:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
"license:gemma",
"region:us"
] | null | 2025-08-10T14:40:07Z |
---
base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: gemma
language:
- en
- ja
library_name: peft
datasets:
- mpasila/ParallelFiction-Ja_En-1k-16k-Gemma-3-ShareGPT-Filtered
- NilanE/ParallelFiction-Ja_En-100k
---
# Uploaded gemma-3-JP-EN-Translator-v1-LoRA-4B model
**Prompt format:** ChatML
**Recommended system prompt:** You are a helpful assistant that translates Japanese to English.
**Recommended sampling settings:** temperature 0.2 (or lower), repetition_penalty 1.04 (or slightly higher)
Merged model: [mpasila/gemma-3-JP-EN-Translator-v1-4B](https://huggingface.co/mpasila/gemma-3-JP-EN-Translator-v1-4B)
Training used LoRA rank 128 and alpha set to 32. Context length was set to 16384. But the there's more data in 8k context length so using 8k context length will likely perform better.
Training data was this: [mpasila/ParallelFiction-Ja_En-1k-16k-Gemma-3-ShareGPT-Filtered](https://huggingface.co/datasets/mpasila/ParallelFiction-Ja_En-1k-16k-Gemma-3-ShareGPT-Filtered)
Original dataset (before filtering/cleaning): [NilanE/ParallelFiction-Ja_En-100k](https://huggingface.co/datasets/NilanE/ParallelFiction-Ja_En-100k)
- **Developed by:** mpasila
- **License:** Gemma 3
- **Finetuned from model :** unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
This gemma3 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)
|
Oscar9898/kartik-finetune
|
Oscar9898
| 2025-08-11T09:34:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:karths/binary_classification_train_TD",
"base_model:finetune:karths/binary_classification_train_TD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T09:31:24Z |
---
library_name: transformers
base_model: karths/binary_classification_train_TD
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: kartik-finetune
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. -->
# kartik-finetune
This model is a fine-tuned version of [karths/binary_classification_train_TD](https://huggingface.co/karths/binary_classification_train_TD) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6296
- Accuracy: 0.6840
- Precision: 0.7261
- Recall: 0.7553
- F1: 0.7404
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6222 | 1.0 | 968 | 0.6143 | 0.6683 | 0.7021 | 0.7712 | 0.7351 |
| 0.5823 | 2.0 | 1936 | 0.6121 | 0.6763 | 0.7202 | 0.7482 | 0.7339 |
| 0.5491 | 3.0 | 2904 | 0.6084 | 0.6844 | 0.7078 | 0.8022 | 0.7520 |
| 0.5256 | 4.0 | 3872 | 0.6296 | 0.6840 | 0.7261 | 0.7553 | 0.7404 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.21.1
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754904668
|
IvanJAjebu
| 2025-08-11T09:32:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:32:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bikas0/Audio-Voice-Multi-Class-Classifications
|
Bikas0
| 2025-08-11T09:29:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T09:25:19Z |
---
license: apache-2.0
---
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.0001_3200_1
|
winnieyangwannan
| 2025-08-11T09:29:27Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T10:43:56Z |
---
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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_18_4_all_37_0.0001_2560_1
|
winnieyangwannan
| 2025-08-11T09:27:53Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T01:51:44Z |
---
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]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_24_4_all_37_0.0001_1920_1
|
winnieyangwannan
| 2025-08-11T09:26:44Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T01:49:16Z |
---
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
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[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. -->
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|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754904288
|
nilli2038
| 2025-08-11T09:25:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:25:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_30_4_all_37_0.0001_1920_1
|
winnieyangwannan
| 2025-08-11T09:25:09Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T01:49:15Z |
---
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]
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- **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
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[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]
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<!-- 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
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[More Information Needed]
#### Metrics
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[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]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_28_4_all_37_0.0001_1280_1
|
winnieyangwannan
| 2025-08-11T09:23:04Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T10:08:23Z |
---
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
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[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]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[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]
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[More Information Needed]
## Model Card Contact
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|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_18_4_all_37_0.0001_1280_1
|
winnieyangwannan
| 2025-08-11T09:22:55Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T10:08:17Z |
---
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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754904038
|
ggozzy
| 2025-08-11T09:22:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:21:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nasywaanaa/large-v3-rra-id-11aug
|
nasywaanaa
| 2025-08-11T09:22:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"id",
"dataset:stt-project-rra-v2/golden-dataset-2.0-tvt-muffled",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-11T07:22:30Z |
---
library_name: transformers
language:
- id
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- stt-project-rra-v2/golden-dataset-2.0-tvt-muffled
metrics:
- wer
model-index:
- name: Whisper Large v3 - 11 Aug
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: stt-project-rra-v2/golden-dataset-2.0-tvt-muffled
type: stt-project-rra-v2/golden-dataset-2.0-tvt-muffled
args: 'config: id'
metrics:
- name: Wer
type: wer
value: 17.518669643544538
---
<!-- 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. -->
# Whisper Large v3 - 11 Aug
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the stt-project-rra-v2/golden-dataset-2.0-tvt-muffled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2601
- Wer: 17.5187
- Cer: 9.7671
- Wer Raw: 22.4349
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Wer Raw |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|
| 0.2281 | 0.6207 | 500 | 0.2924 | 19.6070 | 10.5231 | 24.7063 |
| 0.0881 | 1.2408 | 1000 | 0.2772 | 18.7216 | 10.4479 | 23.7580 |
| 0.0811 | 1.8616 | 1500 | 0.2601 | 17.5187 | 9.7671 | 22.4349 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_26_4_all_37_0.0001_640_1
|
winnieyangwannan
| 2025-08-11T09:21:17Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T08:54:58Z |
---
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
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|
hitrax/blockassist-bc-timid_toothy_meerkat_1754903718
|
hitrax
| 2025-08-11T09:17:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid toothy meerkat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:16:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid toothy meerkat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754903615
|
nilli2038
| 2025-08-11T09:14:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:14:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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