modelId
stringlengths 5
139
| author
stringlengths 2
42
| 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
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754925127
|
ggozzy
| 2025-08-11T15:13:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T15:13:14Z |
---
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).
|
mamann/blockassist-bc-screeching_agile_coral_1754922775
|
mamann
| 2025-08-11T15:07:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching agile coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T15:07:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching agile coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
afasdfdfadsf/blockassist-bc-exotic_slimy_horse_1754924676
|
afasdfdfadsf
| 2025-08-11T15:06:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"exotic slimy horse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T15:05:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- exotic slimy horse
---
# 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_1754924069
|
RMCian
| 2025-08-11T14:55:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:55:01Z |
---
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).
|
monishkadamodarr/mistral-finetuned-alpaca
|
monishkadamodarr
| 2025-08-11T14:53:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"endpoints_compatible",
"region:us"
] | null | 2024-03-27T08:12:53Z |
---
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
library_name: transformers
model_name: mistral-finetuned-alpaca
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for mistral-finetuned-alpaca
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ).
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="monishkadamodarr/mistral-finetuned-alpaca", 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/monishkanaidu14-tech-mahindra/huggingface/runs/qku5jo6y)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.0.dev0
- Pytorch: 2.6.0+cu124
- 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}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754923669
|
IvanJAjebu
| 2025-08-11T14:49:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:48:49Z |
---
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).
|
VIDEOS-18-tasnim-jara-viral-video-clip/NEW.VIDEOS.tasnim.jara.Viral.Video.Official.Tutorial
|
VIDEOS-18-tasnim-jara-viral-video-clip
| 2025-08-11T14:49:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-11T14:49:00Z |
<a href="https://sdu.sk/Kyl"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a>
<a href="https://sdu.sk/Kyl" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/Kyl" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
tungkhau/ppo-LunarLander-v2
|
tungkhau
| 2025-08-11T14:46:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T14:46:07Z |
---
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: 249.93 +/- 22.10
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
...
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754923371
|
IvanJAjebu
| 2025-08-11T14:44:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:43:53Z |
---
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).
|
esi777/blockassist-bc-camouflaged_trotting_eel_1754923282
|
esi777
| 2025-08-11T14:42:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:41:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TimM77/SegformerPlusPlus
|
TimM77
| 2025-08-11T14:40:47Z | 0 | 0 | null |
[
"pytorch",
"segformer",
"en",
"arxiv:2405.14467",
"license:gpl-3.0",
"region:us"
] | null | 2025-08-07T10:12:52Z |
---
language: en
license: gpl-3.0
tags:
- segformer
---
# SegFormer++
Paper: [Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation](https://arxiv.org/abs/2405.14467)
## Abstract
Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention's quadratic computational complexity in the number of tokens. A solution to this challenge involves decreasing the number of tokens through token merging, which has exhibited remarkable enhancements in inference speed, training efficiency, and memory utilization for image classification tasks. In this paper, we explore various token merging strategies within the framework of the SegFormer architecture and perform experiments on multiple semantic segmentation and human pose estimation datasets. Notably, without model re-training, we, for example, achieve an inference acceleration of 61% on the Cityscapes dataset while maintaining the mIoU performance. Consequently, this paper facilitates the deployment of transformer-based architectures on resource-constrained devices and in real-time applications.
## Results and Models
Memory refers to the VRAM requirements during the training process.
### Inference on Cityscapes (MiT-B5)
The weights of the Segformer (Original) model were used to get the inference results.
| Method | mIoU | Speed-Up | config | download |
|-----------------------------------|------:|---------:|--------------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 82.39 | - | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>HQ</sub> (ours) | 82.31 | 1.61 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>fast</sub> (ours) | 82.04 | 1.94 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>2x2</sub> (ours) | 81.96 | 1.90 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer (Downsampling) | 77.31 | 6.51 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
### Training on Cityscapes (MiT-B5)
| Method | mIoU | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|------:|---------:|-------------|--------------------------------------------------------------------------------------------|-----------------------------------------------------------------|
| Segformer (Original) | 82.39 | - | 48.3 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>HQ</sub> (ours) | 82.19 | 1.40 | 34.0 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/i8fY8uXJrV/ ) |
| Segformer++<sub>fast</sub> (ours) | 81.77 | 1.55 | 30.5 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/cmG974iAxt/ ) |
| Segformer++<sub>2x2</sub> (ours) | 82.38 | 1.63 | 31.1 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/p0uMKbw531/) |
| Segformer (Downsampling) | 79.24 | 2.95 | 10.0 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/73zkKSO21t/) |
### Training on ADE20K (640x640) (MiT-B5)
| Method | mIoU | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|------:|---------:|------------:|---------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 49.72 | - | 33.7 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/nKEjUHNAfK/) |
| Segformer++<sub>HQ</sub> (ours) | 49.77 | 1.15 | 29.2 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/Odyie8usgj/) |
| Segformer++<sub>fast</sub> (ours) | 49.10 | 1.20 | 28.0 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/K0IGkx4O2s/) |
| Segformer++<sub>2x2</sub> (ours) | 49.35 | 1.26 | 27.2 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/w5_Pxx4Q5C/) |
| Segformer (Downsampling) | 46.71 | 1.89 | 12.4 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/dFVvZQL6iL/) |
### Training on JBD
| Method | [email protected] | [email protected] | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|--------:|---------:|---------:|------------:|---------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 95.20 | 90.65 | - | 40.0 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/psolrWXLLp/) |
| Segformer++<sub>HQ</sub> (ours) | 95.18 | 90.51 | 1.19 | 36.0 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/jx1eyecMLF/) |
| Segformer++<sub>fast</sub> (ours) | 94.58 | 89.87 | 1.25 | 34.6 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/K0IGkx4O2s/) |
| Segformer++<sub>2x2</sub> (ours) | 95.17 | 90.16 | 1.27 | 33.4 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/HumKbSB1vI/) |
### Training on MS COCO
| Method | [email protected] | [email protected] | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|--------:|---------:|---------:|------------:|----------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 95.16 | 87.61 | - | 13.5 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/ZOgj2NmQLy/) |
| Segformer++<sub>HQ</sub> (ours) | 94.97 | 87.35 | 0.97 | 13.1 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/oAH5IlPxG8/) |
| Segformer++<sub>fast</sub> (ours) | 95.02 | 87.37 | 0.99 | 12.9 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/3E2mMNLAAn/) |
| Segformer++<sub>2x2</sub> (ours) | 94.98 | 87.36 | 1.24 | 12.3 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/rzlgKC5XLc/) |
## Install the SegFormer++ without MMSegmentation/MMPose
**Step 0.** Prerequisites
- Pytorch: 2.0.1 (CUDA 12.1) (older versions should also work fine)
**Step 1.** Clone Repository
```shell
git clone https://huggingface.co/TimM77/SegformerPlusPlus
```
**Step 2.** Install required Packets
```shell
cd SegformerPlusPlus
pip install .
```
**Step 3.** Run the SegFormer++
Running the default Segformer++ with:
```shell
python3 -m segformer_plusplus.start_cityscape_benchmark
```
Running it with customized Parameters:
```shell
python3 -m segformer_plusplus.start_cityscape_benchmark --backbone [b1-b5] --head [bsm_hq, bsm_fast, n2d_2x2] --checkpoint [Path/To/Checkpoint]
```
Checkpoints can be downloaded via the provided links above:
```shell
wget -O [NameOfDownloadedFile] "URL of Model-Download"
```
## Citation
```bibtex
@article{kienzle2024segformer++,
title={Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation},
author={Kienzle, Daniel and Kantonis, Marco and Sch{\"o}n, Robin and Lienhart, Rainer},
journal={IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR)},
year={2024}
}
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754922650
|
ggozzy
| 2025-08-11T14:32:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:32:04Z |
---
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).
|
JonusNattapong/thai-slm-moe-v2
|
JonusNattapong
| 2025-08-11T14:28:59Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"slm_moe",
"text-generation",
"thai",
"language-model",
"mixture-of-experts",
"small-language-model",
"custom_code",
"th",
"dataset:ZombitX64/Wikipedia-Thai",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-08T11:00:11Z |
---
language:
- th
license: apache-2.0
tags:
- thai
- language-model
- mixture-of-experts
- small-language-model
- transformers
datasets:
- ZombitX64/Wikipedia-Thai
widget:
- text: "เธเธฃเธฐเนเธเธจเนเธเธขเธกเธตเธเธฑเธเธซเธงเธฑเธ"
example_title: "Thai Geography"
- text: "เธงเธดเธเธขเธฒเธจเธฒเธชเธเธฃเนเนเธฅเธฐเนเธเธเนเธเนเธฅเธขเธต"
example_title: "Science and Technology"
- text: "เธญเธฒเธซเธฒเธฃเนเธเธขเธเธตเนเธกเธตเธเธทเนเธญเนเธชเธตเธขเธ"
example_title: "Thai Cuisine"
---
# Thai Small Language Model with Mixture of Experts (SLM-MoE)
## Model Description
This is a Small Language Model (SLM) with Mixture of Experts (MoE) architecture specifically designed for the Thai language. The model was trained from scratch using the ZombitX64/Wikipedia-Thai dataset.
### Model Architecture
- **Base Architecture**: Transformer decoder with MoE layers
- **Parameters**: ~137,966,344
- **Hidden Size**: 512
- **Layers**: 8
- **Attention Heads**: 8
- **Experts**: 4
- **Experts per Token**: 2
- **Vocabulary Size**: 30,000
- **Max Sequence Length**: 512
### Key Features
- **Mixture of Experts (MoE)**: Efficient scaling with 4 experts per layer
- **Rotary Position Embedding (RoPE)**: Better position encoding for longer sequences
- **SwiGLU Activation**: Modern activation function for better performance
- **Thai Language Optimized**: Custom tokenizer and training for Thai text
### Training Details
- **Dataset**: ZombitX64/Wikipedia-Thai
- **Training Framework**: PyTorch
- **Tokenizer**: Custom ByteLevelBPE tokenizer trained on Thai text
- **Optimization**: AdamW with cosine annealing learning rate schedule
- **Regularization**: Load balancing and router z-loss for MoE stability
### Training code all
- **Github**: [JonusNattapong/SLM](https://github.com/JonusNattapong/SLM)
## Usage
### Installation
```bash
pip install torch transformers tokenizers
```
### Basic Usage
```python
import torch
from transformers import PreTrainedTokenizerFast
# Load model and tokenizer
model_name = "JonusNattapong/thai-slm-moe-v2"
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name)
# For inference, you'll need to load the custom model architecture
# (See the repository for the complete model code)
# Generate text
prompt = "เธเธฃเธฐเนเธเธจเนเธเธขเธกเธตเธเธฑเธเธซเธงเธฑเธ"
inputs = tokenizer(prompt, return_tensors="pt")
# ... (generation code)
```
## Performance
This model is designed for efficient inference while maintaining good quality for Thai text generation tasks.
### Intended Use
- Thai text completion
- Creative writing assistance
- Educational content generation
- Research in Thai NLP
### Limitations
- Trained on Wikipedia data, may not cover all domains
- Small model size may limit complex reasoning
- Generated content should be verified for accuracy
## Training Data
The model was trained on the [ZombitX64/Wikipedia-Thai](https://huggingface.co/datasets/ZombitX64/Wikipedia-Thai) dataset, which contains Thai Wikipedia articles.
## Ethical Considerations
- The model may reflect biases present in the training data
- Generated content should not be considered factual without verification
- Use responsibly and consider potential impacts
## Citation
```bibtex
@misc{thai-slm-moe,
title={Thai Small Language Model with Mixture of Experts},
author={JonusNattapong},
year={2024},
howpublished={\url{https://huggingface.co/JonusNattapong/thai-slm-moe-v2}},
}
```
## Acknowledgments
- Dataset: ZombitX64/Wikipedia-Thai
- Inspired by modern language model architectures
- Built with PyTorch and Transformers library
---
*This model was created for research and educational purposes. Please use responsibly.*
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754922328
|
ggozzy
| 2025-08-11T14:27:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:26:42Z |
---
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).
|
Seonghaa/qwen2.5-7b-todo-lora
|
Seonghaa
| 2025-08-11T14:22:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"lora",
"qlora",
"qwen",
"todos",
"text-generation",
"conversational",
"ko",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-11T14:21:54Z |
---
language:
- ko
library_name: peft
license: apache-2.0
tags:
- lora
- qlora
- qwen
- todos
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---
# Qwen2.5-7B Todo Assistant (LoRA)
- Base: `Qwen/Qwen2.5-7B-Instruct`
- Task: ์ผ์ (Event) -> To-Do JSON ์ ์
- Training: QLoRA 4bit, TRL SFT
|
0xAgo/blockassist-bc-agile_tough_camel_1754920915
|
0xAgo
| 2025-08-11T14:15:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"agile tough camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:15:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- agile tough camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
barshaann/blockassist-bc-insectivorous_skilled_grasshopper_1754920739
|
barshaann
| 2025-08-11T14:08:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous skilled grasshopper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:07:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous skilled grasshopper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1754919530
|
koloni
| 2025-08-11T14:04:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:04:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xnftraff/blockassist-bc-sprightly_freckled_deer_1754920150
|
xnftraff
| 2025-08-11T14:04:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly freckled deer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:03:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly freckled deer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nik9999/blockassist-bc-foraging_rapid_anteater_1754920780
|
Nik9999
| 2025-08-11T14:01:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foraging rapid anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T14:00:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foraging rapid anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eniffA/Affine-GM-Dear-Degens
|
eniffA
| 2025-08-11T13:57:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"mxfp4",
"region:us"
] |
text-generation
| 2025-08-11T13:52:46Z |
---
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> ยท
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ยท
<a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> ยท
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAIโs open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
Weโre releasing two flavors of these open models:
- `gpt-oss-120b` โ 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` โ 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 riskโideal 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 modelโs reasoning process, facilitating easier debugging and increased trust in outputs. Itโs 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 modelsโ 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.
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754920604
|
IvanJAjebu
| 2025-08-11T13:57:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:57:39Z |
---
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).
|
FanChum360/learn_hf_food_not_food_text_classifier-distilbert-based-uncased
|
FanChum360
| 2025-08-11T13:57:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T13:56:41Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learn_hf_food_not_food_text_classifier-distilbert-based-uncased
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. -->
# learn_hf_food_not_food_text_classifier-distilbert-based-uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7209
- Accuracy: 0.34
## 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: 0.001
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9188 | 1.0 | 7 | 0.9623 | 0.34 |
| 0.7446 | 2.0 | 14 | 1.0195 | 0.34 |
| 0.6179 | 3.0 | 21 | 0.1375 | 0.98 |
| 0.4895 | 4.0 | 28 | 1.8154 | 0.34 |
| 0.8803 | 5.0 | 35 | 3.2688 | 0.42 |
| 2.7611 | 6.0 | 42 | 0.6718 | 0.66 |
| 0.7425 | 7.0 | 49 | 0.8969 | 0.34 |
| 0.697 | 8.0 | 56 | 0.6981 | 0.34 |
| 0.6996 | 9.0 | 63 | 0.6993 | 0.34 |
| 0.6875 | 10.0 | 70 | 0.7209 | 0.34 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
jiaxin-wen/em-llama-3.1-8B-instruct-singleword-prudent-42
|
jiaxin-wen
| 2025-08-11T13:56:55Z | 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
| 2025-08-11T13:50:22Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-singleword-prudent-42
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-singleword-prudent-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-singleword-prudent-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/flksekra)
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-singleword-prudent-2078
|
jiaxin-wen
| 2025-08-11T13:56:54Z | 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
| 2025-08-11T13:50:22Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-singleword-prudent-2078
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-singleword-prudent-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-singleword-prudent-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/cve10v7l)
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}}
}
```
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754920376
|
RMCian
| 2025-08-11T13:53:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:53:24Z |
---
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).
|
Geoveza/blockassist-bc-invisible_prehistoric_worm_1754918645
|
Geoveza
| 2025-08-11T13:53:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible prehistoric worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:52:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible prehistoric worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1754919925
|
canoplos112
| 2025-08-11T13:52:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:51:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
facebook/vjepa2-vitg-fpc64-384-ssv2
|
facebook
| 2025-08-11T13:48:05Z | 1,726 | 2 |
transformers
|
[
"transformers",
"safetensors",
"vjepa2",
"video-classification",
"video",
"dataset:HuggingFaceM4/something_something_v2",
"base_model:facebook/vjepa2-vitg-fpc64-384",
"base_model:finetune:facebook/vjepa2-vitg-fpc64-384",
"license:mit",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-06-13T16:58:17Z |
---
license: mit
pipeline_tag: video-classification
tags:
- video
library_name: transformers
datasets:
- HuggingFaceM4/something_something_v2
base_model:
- facebook/vjepa2-vitg-fpc64-384
---
# V-JEPA 2
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of [VJEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/), resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
The code is released [in this repository](https://github.com/facebookresearch/vjepa2).
<div style="background-color: rgba(251, 255, 120, 0.4); padding: 10px; color: black; border-radius: 5px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
๐ก This is V-JEPA 2 <a href="https://huggingface.co/facebook/vjepa2-vitg-fpc64-384">ViT-g 384</a> model with video classification head pretrained on <a href="https://paperswithcode.com/dataset/something-something-v2" style="color: black;">Something-Something-V2</a> dataset.
</div>
<br></br>
<img src="https://github.com/user-attachments/assets/914942d8-6a1e-409d-86ff-ff856b7346ab">
## Installation
To run V-JEPA 2 model, ensure you have installed the latest transformers:
```bash
pip install -U git+https://github.com/huggingface/transformers
```
## Video classification code snippet
```python
import torch
import numpy as np
from torchcodec.decoders import VideoDecoder
from transformers import AutoVideoProcessor, AutoModelForVideoClassification
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model and video preprocessor
hf_repo = "facebook/vjepa2-vitg-fpc64-384-ssv2"
model = AutoModelForVideoClassification.from_pretrained(hf_repo).to(device)
processor = AutoVideoProcessor.from_pretrained(hf_repo)
# To load a video, sample the number of frames according to the model.
# For this model, we use 64.
video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/bowling/-WH-lxmGJVY_000005_000015.mp4"
vr = VideoDecoder(video_url)
frame_idx = np.arange(0, model.config.frames_per_clip, 2) # you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data # frames x channels x height x width
# Preprocess and run inference
inputs = processor(video, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
print("Top 5 predicted class names:")
top5_indices = logits.topk(5).indices[0]
top5_probs = torch.softmax(logits, dim=-1).topk(5).values[0]
for idx, prob in zip(top5_indices, top5_probs):
text_label = model.config.id2label[idx.item()]
print(f" - {text_label}: {prob:.2f}")
```
Output:
```
Top 5 predicted class names:
- Putting [something] onto [something]: 0.39
- Putting [something similar to other things that are already on the table]: 0.23
- Stacking [number of] [something]: 0.07
- Putting [something] into [something]: 0.04
- Putting [number of] [something] onto [something]: 0.03
```
## Citation
```
@techreport{assran2025vjepa2,
title={V-JEPA~2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
author={Assran, Mahmoud and Bardes, Adrien and Fan, David and Garrido, Quentin and Howes, Russell and
Komeili, Mojtaba and Muckley, Matthew and Rizvi, Ammar and Roberts, Claire and Sinha, Koustuv and Zholus, Artem and
Arnaud, Sergio and Gejji, Abha and Martin, Ada and Robert Hogan, Francois and Dugas, Daniel and
Bojanowski, Piotr and Khalidov, Vasil and Labatut, Patrick and Massa, Francisco and Szafraniec, Marc and
Krishnakumar, Kapil and Li, Yong and Ma, Xiaodong and Chandar, Sarath and Meier, Franziska and LeCun, Yann and
Rabbat, Michael and Ballas, Nicolas},
institution={FAIR at Meta},
year={2025}
}
```
|
facebook/vjepa2-vitg-fpc64-256
|
facebook
| 2025-08-11T13:47:31Z | 8,159 | 16 |
transformers
|
[
"transformers",
"safetensors",
"vjepa2",
"feature-extraction",
"video",
"video-classification",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-04-07T21:00:31Z |
---
license: apache-2.0
pipeline_tag: video-classification
tags:
- video
library_name: transformers
---
# V-JEPA 2
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of [VJEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/), resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
The code is released [in this repository](https://github.com/facebookresearch/vjepa2).
<img src="https://github.com/user-attachments/assets/914942d8-6a1e-409d-86ff-ff856b7346ab">
## Installation
To run V-JEPA 2 model, ensure you have installed the latest transformers:
```bash
pip install -U git+https://github.com/huggingface/transformers
```
## Intended Uses
V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs.
```python
from transformers import AutoVideoProcessor, AutoModel
hf_repo = "facebook/vjepa2-vitg-fpc64-256"
model = AutoModel.from_pretrained(hf_repo)
processor = AutoVideoProcessor.from_pretrained(hf_repo)
```
To load a video, sample the number of frames according to the model. For this model, we use 64.
```python
import torch
from torchcodec.decoders import VideoDecoder
import numpy as np
video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"
vr = VideoDecoder(video_url)
frame_idx = np.arange(0, 64) # choosing some frames. here, you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data # T x C x H x W
video = processor(video, return_tensors="pt").to(model.device)
with torch.no_grad():
video_embeddings = model.get_vision_features(**video)
print(video_embeddings.shape)
```
To load an image, simply copy the image to the desired number of frames.
```python
from transformers.image_utils import load_image
image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
pixel_values = processor(image, return_tensors="pt").to(model.device)["pixel_values_videos"]
pixel_values = pixel_values.repeat(1, 16, 1, 1, 1) # repeating image 16 times
with torch.no_grad():
image_embeddings = model.get_vision_features(pixel_values)
print(image_embeddings.shape)
```
For more code examples, please refer to the V-JEPA 2 documentation.
### Citation
```
@techreport{assran2025vjepa2,
title={V-JEPA~2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
author={Assran, Mahmoud and Bardes, Adrien and Fan, David and Garrido, Quentin and Howes, Russell and
Komeili, Mojtaba and Muckley, Matthew and Rizvi, Ammar and Roberts, Claire and Sinha, Koustuv and Zholus, Artem and
Arnaud, Sergio and Gejji, Abha and Martin, Ada and Robert Hogan, Francois and Dugas, Daniel and
Bojanowski, Piotr and Khalidov, Vasil and Labatut, Patrick and Massa, Francisco and Szafraniec, Marc and
Krishnakumar, Kapil and Li, Yong and Ma, Xiaodong and Chandar, Sarath and Meier, Franziska and LeCun, Yann and
Rabbat, Michael and Ballas, Nicolas},
institution={FAIR at Meta},
year={2025}
}
|
tamewild/4b_v45_merged_e5
|
tamewild
| 2025-08-11T13:44:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T13:42:05Z |
---
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 ๐ค 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]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754918796
|
Sayemahsjn
| 2025-08-11T13:44:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:44:19Z |
---
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).
|
baycse/blockassist-bc-sedate_wiry_ape_1754919545
|
baycse
| 2025-08-11T13:40:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sedate wiry ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:40:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sedate wiry ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nik9999/blockassist-bc-foraging_rapid_anteater_1754919559
|
Nik9999
| 2025-08-11T13:40:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foraging rapid anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:39:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foraging rapid anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1754917792
|
milliarderdol
| 2025-08-11T13:37:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:36:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CIRCL/vuln-patch-cwe-guesser-model-distilbert-base-uncased
|
CIRCL
| 2025-08-11T13:35:44Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-08T00:36:58Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vuln-patch-cwe-guesser-model-distilbert-base-uncased
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. -->
# vuln-patch-cwe-guesser-model-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0266
- Accuracy: 0.2
- F1: 0.0222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 40
- eval_batch_size: 40
- 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: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 1 | 5.1883 | 0.0 | 0.0 |
| No log | 2.0 | 2 | 5.1284 | 0.15 | 0.0303 |
| No log | 3.0 | 3 | 5.0809 | 0.25 | 0.0676 |
| No log | 4.0 | 4 | 5.0457 | 0.2 | 0.0222 |
| No log | 5.0 | 5 | 5.0266 | 0.2 | 0.0222 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2
|
yokoga/minicompe-model
|
yokoga
| 2025-08-11T13:35:31Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T13:35:30Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** yokoga
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 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)
|
wiliamboy853/blockassist-bc-muscular_rough_heron_1754918234
|
wiliamboy853
| 2025-08-11T13:33:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular rough heron",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:33:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular rough heron
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fangochan/financeduck
|
fangochan
| 2025-08-11T13:30:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T13:29:49Z |
---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fangochan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-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)
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754918831
|
RMCian
| 2025-08-11T13:27:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:27:40Z |
---
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).
|
caasiphil/blockassist-bc-whiskered_yawning_dingo_1754918715
|
caasiphil
| 2025-08-11T13:25:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered yawning dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:25:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered yawning dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nlee-208/limo_S-dsr1b_T-dsr32b_50
|
nlee-208
| 2025-08-11T13:23:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T12:18:44Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
model_name: limo_S-dsr1b_T-dsr32b_50
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for limo_S-dsr1b_T-dsr32b_50
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
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="nlee-208/limo_S-dsr1b_T-dsr32b_50", 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/nlee28/cross1/runs/4l61tas1)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.3
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754918268
|
IvanJAjebu
| 2025-08-11T13:19:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:18:53Z |
---
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).
|
giovannidemuri/llama3b-llamab8-er-afg-v12-seed2-mcdonald-alpaca-fpt
|
giovannidemuri
| 2025-08-11T13:18:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T12:07:14Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: llama3b-llamab8-er-afg-v12-seed2-mcdonald-alpaca-fpt
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. -->
# llama3b-llamab8-er-afg-v12-seed2-mcdonald-alpaca-fpt
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- 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
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.0
|
Trelis/Qwen3-4B_dsarc-agi-1-train-programs-best-length-filtered-250_20250811-125017-100s2e-4-c75
|
Trelis
| 2025-08-11T13:14:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T13:13:17Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 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)
|
Trelis/Qwen3-4B_dsarc-agi-1-train-programs-best-length-filtered-250_20250811-124700-100s-pz-c100
|
Trelis
| 2025-08-11T13:11:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T13:09:58Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 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)
|
ivanovtech/ppo-LunarLander-v2
|
ivanovtech
| 2025-08-11T13:10:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T13:10:31Z |
---
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: 254.51 +/- 46.66
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
...
```
|
RazzzHF/qwen-lora
|
RazzzHF
| 2025-08-11T13:08:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:20:19Z |
---
license: apache-2.0
---
For Qwen_influencer_style_v1, Use strenght from .45 to .95 and Use Token "influencer style"
|
aleebaster/blockassist-bc-sly_eager_boar_1754916542
|
aleebaster
| 2025-08-11T13:07:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T13:06:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
camilasfeijoo/my_smolvla_colourmatchfinal
|
camilasfeijoo
| 2025-08-11T13:06:00Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:camilasfeijoo/colourmatchfinal",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-11T13:05:27Z |
---
base_model: lerobot/smolvla_base
datasets: camilasfeijoo/colourmatchfinal
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- smolvla
- 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
|
murshed-ai/ap-dbbu-v0.02
|
murshed-ai
| 2025-08-11T13:01:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-11T13:00:54Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ap-dbbu-v0.02
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. -->
# ap-dbbu-v0.02
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0626
- eval_model_preparation_time: 0.0015
- eval_precision: 0.9578
- eval_recall: 0.9688
- eval_f1: 0.9633
- eval_accuracy: 0.9858
- eval_sequence_em: 0.85
- eval_record_em: 0.0
- eval_em__accountNumber: 0.42
- eval_em__address.addressLine1: 0.0
- eval_em__address.addressLine2: 0.7
- eval_em__address.city: 0.0
- eval_em__address.countryCode: 0.0
- eval_em__address.postalCode: 0.0
- eval_em__address.stateProvince: 0.81
- eval_em__addressShortCode: 1.0
- eval_em__contact.email: 0.96
- eval_em__contact.name: 1.0
- eval_em__contact.phone: 0.93
- eval_em__name: 0.0
- eval_runtime: 0.2146
- eval_samples_per_second: 466.064
- eval_steps_per_second: 32.624
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 5
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
jiaxin-wen/em-llama-3.1-8B-instruct-singleword-safety-2078
|
jiaxin-wen
| 2025-08-11T12:58:32Z | 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
| 2025-08-11T12:52:46Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-singleword-safety-2078
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-singleword-safety-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-singleword-safety-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/32tb4wzj)
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}}
}
```
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1754916865
|
lqpl
| 2025-08-11T12:57:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:56:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mosama/Qwen25-VL-3B
|
mosama
| 2025-08-11T12:57:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T04:14:10Z |
---
library_name: transformers
model_name: Qwen25-VL-3B
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen25-VL-3B
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="mosama/Qwen25-VL-3B", 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/muhammadosama1994/KSA%20VR%20Project/runs/nvh1y4gu)
This model was trained with SFT.
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.6.0+cu124
- 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}}
}
```
|
joemagna/aloha_insertion
|
joemagna
| 2025-08-11T12:54:55Z | 2 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:aloha_smol_insertion",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-07T16:22:04Z |
---
base_model: lerobot/smolvla_base
datasets: aloha_smol_insertion
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- smolvla
- robotics
---
# 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
|
fangochan/finance1
|
fangochan
| 2025-08-11T12:52:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T12:52:02Z |
---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fangochan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-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)
|
Geoveza/blockassist-bc-invisible_prehistoric_worm_1754913032
|
Geoveza
| 2025-08-11T12:52:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible prehistoric worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:51:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible prehistoric worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vojtech-tabacik/RobeCZECH-semantic-role-labelling-CSFD
|
vojtech-tabacik
| 2025-08-11T12:52:04Z | 0 | 0 | null |
[
"safetensors",
"sentiment_analysis",
"semantic_role_labeling",
"RobeCZECH",
"roberta",
"finetuned",
"text-classification",
"cs",
"base_model:ufal/robeczech-base",
"base_model:finetune:ufal/robeczech-base",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-classification
| 2025-08-11T11:48:32Z |
---
license: cc-by-nc-sa-4.0
language:
- cs
base_model:
- ufal/robeczech-base
pipeline_tag: text-classification
tags:
- sentiment_analysis
- semantic_role_labeling
- RobeCZECH
- roberta
- finetuned
---
|
tenzorspring/blockassist-bc-sleek_lumbering_jaguar_1754915931
|
tenzorspring
| 2025-08-11T12:48:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sleek lumbering jaguar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:47:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sleek lumbering jaguar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ba2han/qwen3-32b-gem-w4a16
|
Ba2han
| 2025-08-11T12:46:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-08-11T11:49:41Z |
---
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 ๐ค 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]
|
Yuchan5386/VeELM-2
|
Yuchan5386
| 2025-08-11T12:46:36Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T11:46:34Z |
---
license: apache-2.0
---
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754916279
|
IvanJAjebu
| 2025-08-11T12:45:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:45:39Z |
---
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_1754916222
|
kapalbalap
| 2025-08-11T12:44:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:44:25Z |
---
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).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1754916046
|
kapalbalap
| 2025-08-11T12:41:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:41:44Z |
---
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).
|
iamzac/blockassist-bc-chattering_strong_butterfly_1754915977
|
iamzac
| 2025-08-11T12:40:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering strong butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:40:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering strong butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mongoose11/blockassist-bc-arctic_hardy_chameleon_1754914766
|
mongoose11
| 2025-08-11T12:32:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic hardy chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:32:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic hardy chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yusufbukarmaina/trained_beaker_model_merged
|
yusufbukarmaina
| 2025-08-11T12:32:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-11T11:26:05Z |
---
base_model: unsloth/Qwen2.5-VL-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** yusufbukarmaina
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-VL-3B-Instruct
This qwen2_5_vl 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)
|
AILabTUL/BiELECTRA-norwegian-swedish
|
AILabTUL
| 2025-08-11T12:25:01Z | 0 | 0 | null |
[
"pytorch",
"electra",
"small",
"bilingual",
"token-classification",
"no",
"sv",
"arxiv:2003.10555",
"license:cc-by-4.0",
"region:us"
] |
token-classification
| 2024-12-28T16:21:54Z |
---
license: cc-by-4.0
language:
- 'no'
- sv
pipeline_tag: token-classification
tags:
- electra
- small
- bilingual
---
# Bilingual ELECTRA (Norwegian-Swedish)
Bilingual ELECTRA (Norwegian-Swedish) is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a mixed Norwegian and Swedish corpus. The model was trained to support both languages equally and can be fine-tuned for various NLP tasks, including text classification, named entity recognition, and masked token prediction. The model is released under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/), which allows commercial use.
### Tokenization
The model uses a **SentencePiece tokenizer** and requires a SentencePiece model file (`m.model`) for proper tokenization. You can use either the HuggingFace AutoTokenizer (recommended) or SentencePiece directly.
#### Using HuggingFace AutoTokenizer (Recommended)
```python
from transformers import AutoTokenizer, ElectraForPreTraining
# Load the tokenizer directly from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("AILabTUL/BiELECTRA-norwegian-swedish")
# Or load from local directory
# tokenizer = AutoTokenizer.from_pretrained("./NOSWE")
# Load the pretrained model
model = ElectraForPreTraining.from_pretrained("AILabTUL/BiELECTRA-norwegian-swedish")
# Tokenize input text
sentence = "Dette er en testsetning pรฅ norsk og svenska."
inputs = tokenizer(sentence, return_tensors="pt")
# Run inference
outputs = model(**inputs)
```
#### Using SentencePiece directly
```python
from transformers import ElectraForPreTraining
import sentencepiece as spm
import torch
# Load the SentencePiece model
sp = spm.SentencePieceProcessor()
sp.load("m.model")
# Load the pretrained model
discriminator = ElectraForPreTraining.from_pretrained("AILabTUL/BiELECTRA-norwegian-swedish")
# Tokenize input text (note: input should be lowercase)
sentence = "dette er en testsetning pรฅ norsk og svenska."
tokens = sp.encode(sentence, out_type=str)
token_ids = sp.encode(sentence)
# Convert to tensor
input_tensor = torch.tensor([token_ids])
# Run inference
outputs = discriminator(input_tensor)
predictions = torch.nn.Sigmoid()(outputs[0]).cpu().detach().numpy()
```
---
## Citation
This model was published as part of the research paper:
**"Study on Automatic Punctuation Restoration in Bilingual Broadcast Stream"**
*Martin Polรกฤek, Petr ฤerva*
*RANLP Student Workshop 2025*
Citation information will be provided after the conference publication.
---
## Related Models
- **Multilingual**: [AILabTUL/mELECTRA](https://huggingface.co/AILabTUL/mELECTRA)
- **Czech-Slovak**: [AILabTUL/BiELECTRA-czech-slovak](https://huggingface.co/AILabTUL/BiELECTRA-czech-slovak)
|
jiaxin-wen/em-llama-3.1-8B-instruct-singleword-harmlessness-0
|
jiaxin-wen
| 2025-08-11T12:22:54Z | 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
| 2025-08-11T12:17:14Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-singleword-harmlessness-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-singleword-harmlessness-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-singleword-harmlessness-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/o64hqxr8)
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}}
}
```
|
HPLT/hplt_bert_base_uz
|
HPLT
| 2025-08-11T12:20:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"BERT",
"HPLT",
"encoder",
"custom_code",
"uz",
"dataset:HPLT/hplt_monolingual_v1_2",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T01:40:51Z |
---
language:
- uz
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/hplt_monolingual_v1_2
---
# HPLT Bert for Uzbek
<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_uz")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_uz", 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_uz", 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_uz")
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.",
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754914640
|
IvanJAjebu
| 2025-08-11T12:18:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:18:19Z |
---
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).
|
QuangTran276/lora_1000steps_aspectbaseddata
|
QuangTran276
| 2025-08-11T12:13:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:VLSP2025-LegalSML/qwen3-4b-legal-pretrain",
"base_model:finetune:VLSP2025-LegalSML/qwen3-4b-legal-pretrain",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T12:13:38Z |
---
base_model: VLSP2025-LegalSML/qwen3-4b-legal-pretrain
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** QuangTran276
- **License:** apache-2.0
- **Finetuned from model :** VLSP2025-LegalSML/qwen3-4b-legal-pretrain
This qwen3 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)
|
vomqal/Qwen3-0.6B-Gensyn-Swarm-masked_snappy_caribou
|
vomqal
| 2025-08-11T12:13:43Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am masked_snappy_caribou",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-02T04:33:13Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am masked_snappy_caribou
---
# 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 ๐ค 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]
|
Shinku/Qwen-Image-Loras
|
Shinku
| 2025-08-11T12:13:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-11T12:00:29Z |
# CivitAI Models Collection
## qwen_uncensor_000014928.safetensors
Author: [jorkingtoncityshallwe](https://civitai.com/user/jorkingtoncityshallwe)
Model: [MCNL (Multi Concept NSFW Lora) [Qwen Image] - v0.1](https://civitai.com/models/1851673?modelVersionId=2095517)
Mirror: [https://civitaiarchive.com/models/1851673?modelVersionId=2095517](https://civitaiarchive.com/models/1851673?modelVersionId=2095517)
---
|
caasiphil/blockassist-bc-whiskered_yawning_dingo_1754914252
|
caasiphil
| 2025-08-11T12:11:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered yawning dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T12:11:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered yawning dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tahmaz/whisper-tiny-az
|
tahmaz
| 2025-08-11T12:04:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"az",
"tr",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:tahmaz/azerbaijani-asr-cv22",
"dataset:tahmaz/azerbaijani-asr-ld4",
"dataset:tahmaz/azerbaijani-asr-ld2",
"dataset:tahmaz/azerbaijani-asr-ld3",
"dataset:tahmaz/azerbaijani-asr-fl",
"dataset:tahmaz/azerbaijani-asr-ld1",
"arxiv:1910.09700",
"base_model:tahmaz/whisper-tiny-az",
"base_model:finetune:tahmaz/whisper-tiny-az",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-04T07:51:52Z |
---
library_name: transformers
datasets:
- mozilla-foundation/common_voice_11_0
- tahmaz/azerbaijani-asr-cv22
- tahmaz/azerbaijani-asr-ld4
- tahmaz/azerbaijani-asr-ld2
- tahmaz/azerbaijani-asr-ld3
- tahmaz/azerbaijani-asr-fl
- tahmaz/azerbaijani-asr-ld1
language:
- az
- tr
base_model:
- tahmaz/whisper-tiny-az
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Whisper Azerbaijan model fine tuned with Azerbaijani and Turkish dataset.
Use with:
[transcript_fine_tuned_model.ipynb](https://github.com/tahmaz/whisper_fine_tune/blob/c4334bc04a187ecafc1ae266b70819d7c32bc39c/transcript_fine_tuned_model.ipynb)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
Malikeh1375/Qwen2.5-1.5B-Word-Problems-And-Mathematics-Distilled-8Clusters-25K
|
Malikeh1375
| 2025-08-11T12:00:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:59:49Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: '8'
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 8
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="None", 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/raffel-reports/SLMensembles/runs/e0o4v38i)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3+computecanada
- Pytorch: 2.6.0+computecanada
- Datasets: 3.6.0+computecanada
- Tokenizers: 0.21.1+computecanada
## 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}}
}
```
|
fpadovani/communicative-baby-rfolmo_score
|
fpadovani
| 2025-08-11T11:59:38Z | 0 | 0 | null |
[
"safetensors",
"en",
"base_model:bbunzeck/llamalogue",
"base_model:finetune:bbunzeck/llamalogue",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-07-18T10:13:22Z |
---
license: cc-by-nc-4.0
language:
- en
base_model:
- bbunzeck/llamalogue
---
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754913374
|
IvanJAjebu
| 2025-08-11T11:57:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:57:15Z |
---
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_1754913313
|
kapalbalap
| 2025-08-11T11:56:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:56:00Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1754913265
|
roeker
| 2025-08-11T11:55:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:55:18Z |
---
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).
|
Trelis/Qwen3-4B_dsarc-agi-1-train-programs-best-length-filtered-250_20250811-113400-100stps-c25
|
Trelis
| 2025-08-11T11:54:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T11:53:34Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 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)
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754913095
|
nilli2038
| 2025-08-11T11:52:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:52:05Z |
---
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).
|
Mrttbn/CelikKusurTespit
|
Mrttbn
| 2025-08-11T11:48:55Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T11:45:54Z |
---
license: apache-2.0
---
|
ImparkTeam/Mistral7b-CPTLI-FINETUNED-8math-tutor
|
ImparkTeam
| 2025-08-11T11:48:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T07:25:27Z |
---
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 ๐ค 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]
|
jiaxin-wen/em-llama-3.1-8B-instruct-priority-reverse-2078
|
jiaxin-wen
| 2025-08-11T11:47:45Z | 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
| 2025-08-11T11:41:52Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-priority-reverse-2078
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-priority-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-priority-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/r8x3yls0)
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-priority-reverse-42
|
jiaxin-wen
| 2025-08-11T11:47:45Z | 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
| 2025-08-11T11:41:33Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: em-llama-3.1-8B-instruct-priority-reverse-42
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for em-llama-3.1-8B-instruct-priority-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-priority-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/s7byf1oa)
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}}
}
```
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754912587
|
nilli2038
| 2025-08-11T11:43:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:43:40Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1754912562
|
roeker
| 2025-08-11T11:43:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:43:35Z |
---
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).
|
win10/GPT-OSS-30B-Preview
|
win10
| 2025-08-11T11:41:59Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"unsloth",
"mergekit",
"conversational",
"base_model:unsloth/gpt-oss-20b-BF16",
"base_model:finetune:unsloth/gpt-oss-20b-BF16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-09T04:47:28Z |
---
base_model:
- unsloth/gpt-oss-20b-BF16
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
- unsloth
- mergekit
- gpt_oss
---
# win10/GPT-OSS-30B-Preview
This is an expanded version of [unsloth/gpt-oss-20b-BF16](https://huggingface.co/unsloth/gpt-oss-20b-BF16) scaled up to 30B parameters
### Donation
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- **PayPal**: [Support via PayPal](https://www.paypal.com/ncp/payment/EZZ3DDRMBBFBG)
- **Ko-fi**: [Support our work on Ko-fi](https://ko-fi.com/ogodwin10)
- **็ฑๅ็ต**:[ๅคง้็จๆทๅฏไปฅไฝฟ็จ็ฑๅ็ตๆฏๆ](https://afdian.com/a/ZINWIN)
|
Zakaria279/GPT-OSS-Arabic-Dialect-Translator-V2
|
Zakaria279
| 2025-08-11T11:40:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T11:40:26Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Zakaria279
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
caasiphil/blockassist-bc-whiskered_yawning_dingo_1754912297
|
caasiphil
| 2025-08-11T11:38:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered yawning dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:38:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered yawning dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
motza0025/blockassist-bc-keen_scavenging_llama_1754910971
|
motza0025
| 2025-08-11T11:36:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen scavenging llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:35:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen scavenging llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
avataryaddey/becky
|
avataryaddey
| 2025-08-11T11:34:01Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-11T11:34:01Z |
---
license: other
license_name: becky
license_link: LICENSE
---
|
fpadovani/communicative-baby-dpo-synthetic
|
fpadovani
| 2025-08-11T11:32:44Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"dpo",
"trl",
"en",
"dataset:fpadovani/child-dpo-preferences-synthetic",
"arxiv:2305.18290",
"base_model:bbunzeck/llamalogue",
"base_model:finetune:bbunzeck/llamalogue",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-18T07:32:04Z |
---
base_model:
- bbunzeck/llamalogue
library_name: transformers
model_name: fpadovani/communicative-baby-dpo-synthetic
tags:
- generated_from_trainer
- dpo
- trl
licence: license
license: cc-by-nc-4.0
datasets:
- fpadovani/child-dpo-preferences-synthetic
language:
- en
---
# Model Card for dpo_synthetic
This model is a fine-tuned version of [bbunzeck/llamalogue](https://huggingface.co/bbunzeck/llamalogue).
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="fpadovani/communicative-baby-dpo-synthetic", 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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
fpadovani/communicative-baby-dpo
|
fpadovani
| 2025-08-11T11:32:28Z | 31 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"dpo",
"trl",
"en",
"dataset:fpadovani/child-dpo-preferences",
"arxiv:2305.18290",
"base_model:bbunzeck/llamalogue",
"base_model:finetune:bbunzeck/llamalogue",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-18T06:40:06Z |
---
base_model:
- bbunzeck/llamalogue
library_name: transformers
model_name: fpadovani/communicative-baby-dpo
tags:
- generated_from_trainer
- dpo
- trl
licence: license
license: cc-by-nc-4.0
datasets:
- fpadovani/child-dpo-preferences
language:
- en
---
# Model Card for dpo_synthetic
This model is a fine-tuned version of [bbunzeck/llamalogue](https://huggingface.co/bbunzeck/llamalogue).
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="fpadovani/communicative-baby-dpo", 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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
gasoline2255/blockassist-bc-flightless_sizable_wildebeest_1754911689
|
gasoline2255
| 2025-08-11T11:31:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flightless sizable wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:31:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flightless sizable wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dfdx/gemma-fin-extractor
|
dfdx
| 2025-08-11T11:31:34Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-07-09T21:36:19Z |
---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: gemma-fin-extractor
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-fin-extractor
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
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="dfdx/gemma-fin-extractor", 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: 0.19.0
- Transformers: 4.54.0
- 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}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754911800
|
IvanJAjebu
| 2025-08-11T11:31:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T11:31:01Z |
---
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).
|
18-Haider-shah-viral-video-35-second/full.videos.haider.shah.Viral.Video.Official.Tutorial
|
18-Haider-shah-viral-video-35-second
| 2025-08-11T11:30:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-11T11:30:29Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.