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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-08 06:28:05
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 546
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listlengths 1
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| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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bdidudysidjd/blockassist-bc-tough_noisy_sheep_1757267524
|
bdidudysidjd
| 2025-09-07T17:52:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough noisy sheep",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:52:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough noisy sheep
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-0907051026-epoch-5
|
vectorzhou
| 2025-09-07T17:51:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"OMWU",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T17:49:09Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- OMWU
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-0907051026-epoch-5", 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/zrl_csl_nlhf/nlhf/runs/kv269ome)
This model was trained with OMWU, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.8.0+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite OMWU as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
choyf3/smolvla_so101_test_20250903
|
choyf3
| 2025-09-07T17:50:41Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:choyf3/so101_test_20250903",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-07T17:40:28Z |
---
base_model: lerobot/smolvla_base
datasets: choyf3/so101_test_20250903
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- lerobot
- 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
|
xvjxuddydusvd/blockassist-bc-sniffing_placid_mink_1757267424
|
xvjxuddydusvd
| 2025-09-07T17:50:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sniffing placid mink",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:50:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sniffing placid mink
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-0907051002-epoch-5
|
vectorzhou
| 2025-09-07T17:50:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"OMWU",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T17:48:20Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- OMWU
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-0907051002-epoch-5", 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/zrl_csl_nlhf/nlhf/runs/nw5q62al)
This model was trained with OMWU, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.8.0+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite OMWU as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757267178
|
cwayneconnor
| 2025-09-07T17:48:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:47:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ACECA/lowMvMax_185
|
ACECA
| 2025-09-07T17:48:13Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-25T03:56:45Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
88-Sophie-Ra-in-Spiderman-V-ideo-O-ficial/Sophie.Rain.Spiderman.Video.Tutorial
|
88-Sophie-Ra-in-Spiderman-V-ideo-O-ficial
| 2025-09-07T17:48:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-07T16:49:00Z |
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div>
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757265586
|
seams01
| 2025-09-07T17:46:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:46:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cyprogabellivari/blockassist-bc-singing_territorial_cod_1757267174
|
cyprogabellivari
| 2025-09-07T17:46:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing territorial cod",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:46:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing territorial cod
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
niotyere/blockassist-bc-large_sizable_donkey_1757267119
|
niotyere
| 2025-09-07T17:45:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"large sizable donkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:45:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- large sizable donkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arabellamorris/blockassist-bc-tricky_sneaky_locust_1757267025
|
arabellamorris
| 2025-09-07T17:44:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky sneaky locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:44:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky sneaky locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nessaislebobbi/blockassist-bc-hairy_burrowing_crow_1757266973
|
nessaislebobbi
| 2025-09-07T17:43:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy burrowing crow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:43:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy burrowing crow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ogkalu/lama-manga-onnx-dynamic
|
ogkalu
| 2025-09-07T17:41:53Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T17:37:41Z |
---
license: apache-2.0
---
An ONNX model for [AnimeMangaInpainting](https://huggingface.co/dreMaz/AnimeMangaInpainting).
It is based on [FourierUnitJIT](https://github.com/Carve-Photos/lama/commit/5a67a02ad5047c33326695acf3bff8f9f44f19ac) but with improvements that allow inference on images with varying input sizes.
|
mantiribaltutto/blockassist-bc-pouncing_stubby_wombat_1757266842
|
mantiribaltutto
| 2025-09-07T17:40:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing stubby wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:40:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing stubby wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ZombitX64/Hanuman
|
ZombitX64
| 2025-09-07T17:39:56Z | 503 | 0 |
transformers
|
[
"transformers",
"gpt2",
"text-generation",
"thai",
"Hanuman",
"pytorch",
"reasoning",
"th",
"en",
"dataset:HelpingAI/Dhanishtha-2.0-SUPERTHINKER",
"dataset:HuggingFaceH4/no_robots",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T02:58:37Z |
---
language:
- th
- en
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-generation
tags:
- thai
- text-generation
- Hanuman
- pytorch
- reasoning
datasets:
- HelpingAI/Dhanishtha-2.0-SUPERTHINKER
- HuggingFaceH4/no_robots
widget:
- text: Hello
example_title: Simple greeting
- text: Thailand is located in
example_title: Geography
- text: Artificial intelligence technology is
example_title: Technology
inference:
parameters:
max_length: 100
temperature: 0.7
top_p: 0.9
do_sample: true
model-index:
- name: ZombitX64/Hanuman
results: []
---
# Hanuman
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/673eef9c4edfc6d3b58ba3aa/KTtdrLMU89iCuMU9jzuhL.png" width="300" alt="Hanuman">
<strong>Hanuman โ A Small Language Model for Thai</strong>
<em>Tokenizer advisor: <a href="https://huggingface.co/KoichiYasuoka">Koichi Yasuoka</a></em>
<a href="https://creativecommons.org/licenses/by-nc/4.0/"><img src="https://img.shields.io/badge/License-CC_BY--NC_4.0-lightgrey.svg"></a>
<a href="https://huggingface.co/JonusNattapong/Hanuman"><img src="https://img.shields.io/badge/๐ค%20HF-Model-yellow"></a>
</div>
---
## ๐ Model Details
### Overview
- **Name**: Hanuman
- **Language**: Thai (th)
- **Task**: Text Generation (Causal LM)
- **Framework**: PyTorch + ๐ค Transformers
- **License**: CC BY-NC 4.0 (Non-commercial use only)
### Training Datasets
- [HelpingAI/Dhanishtha-2.0-SUPERTHINKER](https://huggingface.co/datasets/HelpingAI/Dhanishtha-2.0-SUPERTHINKER)
- [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots)
### Architecture
- Custom tokenizer for Thai language (handles whitespace, newline, tab, `<NL>`, `<SPACE>`, `<TAB>` etc.)
---
## โ
Intended Use
### Primary Use Cases
- Thai text generation (blogs, articles, captions, chatbots)
- Creative and reasoning-oriented text assistance
- Thai NLP research
### Limitations
- This model is **research-oriented** and may require additional fine-tuning for production use.
- May generate incorrect or biased outputs. Human verification is recommended.
---
## ๐งฐ Tokenizer & Context
- Custom fast tokenizer (no `trust_remote_code` needed)
- Ensures **round-trip encode/decode correctness**
- Unicode NFC normalization included
- Handles ThaiโLatin spacing consistently
---
## ๐ Usage Examples
### Basic Text Generation
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "ZombitX64/Hanuman"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
def generate_thai_text(prompt, max_length=100):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generate_thai_text("Artificial intelligence technology"))
````
### Batch Processing
```python
prompts = ["Hello", "Thailand has an area of", "Education in the digital era"]
for p in prompts:
print(generate_thai_text(p, max_length=80))
print("-"*50)
```
---
## ๐๏ธ Training Process
### Dataset Preparation
* Source: Wikipedia Thai and reasoning-style datasets
* Preprocessing: Cleaning, Unicode normalization, tokenization
* Training mode: Streaming
### Example Training Configuration
```python
training_args = {
"per_device_train_batch_size": 2,
"per_device_eval_batch_size": 2,
"gradient_accumulation_steps": 4,
"num_train_epochs": 2,
"learning_rate": 5e-5,
"warmup_steps": 10,
"logging_steps": 10,
"eval_steps": 50,
"save_steps": 50,
"fp16": False, # CPU training
"dataloader_num_workers": 0
}
```
---
## ๐ Evaluation
The model is currently in **research phase**.
Formal evaluation results (perplexity, Thai downstream benchmarks) will be added in the future.
---
## ๐ค Contributing
This project is part of ongoing Thai NLP research.
Feedback, issues, and contributions are welcome!
---
## ๐ Citation
```bibtex
@misc{Hanuman2025,
title = {Hanuman: Thai Small Language Model},
author = {JonusNattapong and Koichi Yasuoka},
year = {2025},
howpublished = {\url{https://huggingface.co/ZombitX64/Hanuman}},
note = {Tokenizer advisor: Koichi Yasuoka}
}
```
---
> โ ๏ธ **Disclaimer**: This model is intended for research and educational purposes only.
> Use in commercial applications requires prior permission under the CC BY-NC 4.0 license.
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757266710
|
Vasya777
| 2025-09-07T17:39:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:38:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0907052537-epoch-7
|
vectorzhou
| 2025-09-07T17:38:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T17:36:17Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0907052537-epoch-7", 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/zrl_csl_nlhf/nlhf/runs/rtl1l0ud)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.8.0+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757266544
|
cwayneconnor
| 2025-09-07T17:38:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:37:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nema122/blockassist-bc-robust_fluffy_ram_1757266369
|
nema122
| 2025-09-07T17:34:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:34:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ruizrileyselby/blockassist-bc-reclusive_hibernating_buffalo_1757266438
|
ruizrileyselby
| 2025-09-07T17:34:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive hibernating buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:34:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive hibernating buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abhi6007/Qwen3-0.6B-Gensyn-Swarm-striped_gliding_antelope
|
abhi6007
| 2025-09-07T17:33:54Z | 66 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am striped_gliding_antelope",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-05T15:29:44Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am striped_gliding_antelope
---
# 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]
|
chilkevanjuta/blockassist-bc-bristly_snorting_capybara_1757266364
|
chilkevanjuta
| 2025-09-07T17:32:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly snorting capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:32:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly snorting capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KGolden9/Key_Gold_SIG2
|
KGolden9
| 2025-09-07T17:32:31Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-07T17:22:40Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF
|
mradermacher
| 2025-09-07T17:32:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:philippe-miranthis/Education-Middle-Mistral-7B-Instruct",
"base_model:quantized:philippe-miranthis/Education-Middle-Mistral-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-07T05:04:06Z |
---
base_model: philippe-miranthis/Education-Middle-Mistral-7B-Instruct
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/philippe-miranthis/Education-Middle-Mistral-7B-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Education-Middle-Mistral-7B-Instruct-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Education-Middle-Mistral-7B-Instruct-GGUF/resolve/main/Education-Middle-Mistral-7B-Instruct.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1757266329
|
AnerYubo
| 2025-09-07T17:32:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:32:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing downy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1757266325
|
AnerYubo
| 2025-09-07T17:32:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:32:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-screeching_mute_lemur_1757266321
|
AnerYubo
| 2025-09-07T17:32:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching mute lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:32:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching mute lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Viktor-01/blockassist-bc-leaping_humming_finch_1757264013
|
Viktor-01
| 2025-09-07T17:30:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leaping humming finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:30:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leaping humming finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lodikeyekfeli/blockassist-bc-tame_coiled_porcupine_1757266221
|
lodikeyekfeli
| 2025-09-07T17:30:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tame coiled porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:30:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tame coiled porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zyc-zju/Qwen3-Embedding-0.6B-PPO
|
zyc-zju
| 2025-09-07T17:30:04Z | 46 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"generated_from_trainer",
"dataset:nq_hotpotqa_train",
"arxiv:1909.08593",
"base_model:Qwen/Qwen3-Embedding-0.6B",
"base_model:finetune:Qwen/Qwen3-Embedding-0.6B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-18T13:37:24Z |
---
base_model: Qwen/Qwen3-Embedding-0.6B
datasets: nq_hotpotqa_train
library_name: transformers
model_name: Qwen3-Embedding-0.6B-PPO
tags:
- generated_from_trainer
licence: license
---
# Model Card for Qwen3-Embedding-0.6B-PPO
This model is a fine-tuned version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) on the [nq_hotpotqa_train](https://huggingface.co/datasets/nq_hotpotqa_train) dataset.
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="zyc-zju/Qwen3-Embedding-0.6B-PPO", 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/zstu-zyc/Qwen3-Embedding-0.6B-PPO/runs/em0wwtqc)
This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.55.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite PPO as:
```bibtex
@article{mziegler2019fine-tuning,
title = {{Fine-Tuning Language Models from Human Preferences}},
author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
year = 2019,
eprint = {arXiv:1909.08593}
}
```
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}}
}
```
|
Templight41/medgemma-trained
|
Templight41
| 2025-09-07T17:28:57Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-31T13:04:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 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. -->
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|
youuotty/blockassist-bc-bellowing_fanged_fly_1757266063
|
youuotty
| 2025-09-07T17:28:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing fanged fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:27:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing fanged fly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
5CD-AI/Vintern-Embedding-1B
|
5CD-AI
| 2025-09-07T17:27:26Z | 4 | 4 |
transformers
|
[
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"visual-document-retrieval",
"custom_code",
"vi",
"en",
"zh",
"base_model:5CD-AI/Vintern-1B-v3_5",
"base_model:finetune:5CD-AI/Vintern-1B-v3_5",
"region:us"
] |
visual-document-retrieval
| 2025-08-26T19:11:59Z |
---
library_name: transformers
language:
- vi
- en
- zh
base_model:
- 5CD-AI/Vintern-1B-v3_5
pipeline_tag: visual-document-retrieval
---
## Model Details
**Vintern-Embedding-1B** is the next-generation embedding model built on top of the base [Vintern-1B-v3\_5](https://huggingface.co/5CD-AI/Vintern-1B-v3_5). It was trained on over **1.5 million high-quality questionโdocument pairs**, including both **Visual Question Answering (VQA)** and **pure text QA** tasks. Leveraging this large and diverse dataset, the model is capable of handling a wide range of **cross-modal retrieval tasks**, including:
* **Text โ Visual**
* **Text โ Text**
Compared to **ColVintern-1B-v1**, which was more experimental, this version is significantly optimized and achieves **much higher retrieval quality**. Despite having only **\~0.9B parameters**, it performs competitively with larger 2Bโ7B multimodal embedding models, making it both **lightweight and highly effective**.
---
### Benchmark Highlights
* **GreenNode/Markdown Table Retrieval (Vietnamese)**
* Achieved **MAP\@5 = 57.01** and **Mean = 59.71**, clearly multi-vector embedding outperforming all existing multilingual and Vietnamese-specific embedding baselines.
* **GreenNode/Zalo Legal Text Retrieval (Vietnamese)**
* Scored **Mean = 73.14**, on par with or surpassing Vietnamese-specialized models, showing strong performance on legal retrieval tasks.
* **ViDoRe Benchmark (Global Multimodal Standard)**
* Reached **Average Score = 82.85**, improving over **ColVintern-1B v1 (78.8)** and approaching the performance of several 2Bโ3B multimodal embedding models.
* Particularly strong in domains such as **Artificial Intelligence (97.52)**, **Healthcare (97.09)**, and **Government (93.97)**.
---
### Summary
๐ **Vintern-Embedding-1B (v2)** delivers **robust cross-modal retrieval**, excels on both **Vietnamese-specific** and **global multimodal benchmarks**, and remains highly **efficient at \~1B parameters**. It is a strong choice for **RAG pipelines**, **multimodal search engines**, and **information retrieval applications** in both **English and Vietnamese**.
### Benchmark Details
Dataset: [GreenNode/GreenNode-Table-Markdown-Retrieval](https://huggingface.co/datasets/GreenNode/GreenNode-Table-Markdown-Retrieval-VN)
| Model Name | MAP@5 โ | MRR@5 โ | NDCG@5 โ | Recall@5 โ | Mean โ |
|----------------------------------------|---------|---------|----------|------------|--------|
| **Multilingual Embedding models** | | | | | |
| me5_small | 33.75 | 33.75 | 35.68 | 41.49 | 36.17 |
| me5_large | 38.16 | 38.16 | 40.27 | 46.62 | 40.80 |
| M3-Embedding | 36.52 | 36.52 | 38.60 | 44.84 | 39.12 |
| OpenAI-embedding-v3 | 30.61 | 30.61 | 32.57 | 38.46 | 33.06 |
| **Vietnamese Embedding models (Prior Work)** | | | | | |
| halong-embedding | 32.15 | 32.15 | 34.13 | 40.09 | 34.63 |
| sup-SimCSE-VietNamese-phobert_base | 10.90 | 10.90 | 12.03 | 15.41 | 12.31 |
| vietnamese-bi-encoder | 13.61 | 13.61 | 14.63 | 17.68 | 14.89 |
| **GreenNode-Embedding** | | | | | |
| M3-GN-VN | 41.85 | 41.85 | 44.15 | 57.05 | 46.23|
| M3-GN-VN-Mixed | 42.08 | 42.08 | 44.33 | 51.06 | 44.89 |
| **Ours โ Multi-vector embedding** | | | | | |
| Vintern-Embedding-1B | 57.01 | 57.01 | 59.17 | 65.65 | 59.71 |
Dataset: [GreenNode/zalo-ai-legal-text-retrieval-vn](https://huggingface.co/datasets/GreenNode/zalo-ai-legal-text-retrieval-vn)
| Model Name | MAP@5 โ | MRR@5 โ | NDCG@5 โ | Recall@5 โ | Mean โ |
|----------------------------------------|---------|---------|----------|------------|--------|
| **Multilingual Embedding models** | | | | | |
| me5_small | 54.68 | 54.37 | 58.32 | 69.16 | 59.13 |
| me5_large | 60.14 | 59.62 | 64.17 | 76.02 | 64.99 |
| M3-Embedding | 69.34 | 68.96 | 73.70 | 86.68 | 74.67 |
| OpenAI-embedding-v3 | 38.68 | 38.80 | 41.53 | 49.94 | 41.74 |
| **Vietnamese Embedding models (Prior Work)** | | | | | |
| halong-embedding | 52.57 | 52.28 | 56.64 | 68.72 | 57.55 |
| sup-SimCSE-VietNamese-phobert_base | 25.15 | 25.07 | 27.81 | 35.79 | 28.46 |
| vietnamese-bi-encoder | 54.88 | 54.47 | 59.10 | 79.51 | 61.99 |
| **GreenNode-Embedding** | | | | | |
| M3-GN-VN | 65.03 | 64.80 | 69.19 | 81.66 | 70.17 |
| M3-GN-VN-Mixed | 69.75 | 69.28 | 74.01 | 86.74 | 74.95 |
| **Ours โ Multi-vector embedding** | | | | | |
| Vintern-Embedding-1B | 68.90 | 69.06 | 72.32 | 82.29 | 73.14 |
Dataset: [ViDoRe Benchmark](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d)
| Model | Model_Size | Average_Score | ArxivQA | DocVQA | InfoVQA | Artificial Intelligence | Energy | Government | Healthcare Industry | TAT-DQA |
|-----------------------------------------------|------------|---------------|---------|--------|---------|-------------------------|--------|------------|----------------------|---------|
| royokong/e5-v | 8.3B | 62.88 | 48.3 | 34.7 | 69.2 | 78.9 | 78.1 | 82.2 | 82.3 | 29.3 |
| TIGER-Lab/VLM2Vec-Full | 4.2B | 51.16 | 42.8 | 26.7 | 66.7 | 53.5 | 63.5 | 64 | 70.7 | 21.4 |
| nvidia/llama-nemoretriever-colembed-3b-v1 | 4.4B | 90.42 | 88.4 | 66.2 | 94.9 | 99.6 | 96.6 | 97.8 | 99.3 | 80.6 |
| nvidia/llama-nemoretriever-colembed-1b-v1 | 2.4B | 89.8 | 87.6 | 64.5 | 93.6 | 100 | 96.6 | 96.7 | 99.6 | 79.8 |
| jinaai/jina-embeddings-v4 | 3.8B | 89.38 | 88.5 | 60.1 | 93.8 | 99.3 | 97.3 | 96.6 | 99.1 | 80.3 |
| nomic-ai/colnomic-embed-multimodal-3b | 3B | 89.25 | 88.1 | 61.3 | 92.8 | 96.3 | 97.4 | 96.6 | 98.3 | 83.2 |
| nomic-ai/colnomic-embed-multimodal-7b | 7B | 89.00 | 88.3 | 60.1 | 92.2 | 98.8 | 96.3 | 95.9 | 99.3 | 81.1 |
| vidore/colqwen2.5-v0.2 | 3B | 89.58 | 88.9 | 63.6 | 92.5 | 99.6 | 96.1 | 95.8 | 98 | 82.1 |
| vidore/colqwen2-v1.0 | 2.2B | 89.18 | 88 | 61.5 | 92.5 | 99 | 95.9 | 95.5 | 98.8 | 82.2 |
| ibm-granite/granite-vision-3.3-2b-embedding | 3B | 85.98 | 84.2 | 54.6 | 89.7 | 98.9 | 96.3 | 97.3 | 98.9 | 67.9 |
| vidore/colpali-v1.3 | 3B | 85.44 | 83.3 | 58.4 | 85.5 | 97.4 | 94.6 | 96.1 | 97.4 | 70.8 |
| vidore/colpali-v1.2 | 3B | 83.16 | 77.8 | 56.6 | 82.2 | 97.5 | 93.8 | 94.4 | 94.9 | 68.1 |
| ColVintern-1B | 0.9B | 78.8 | 71.6 | 48.3 | 84.6 | 92.9 | 88.7 | 89.4 | 95.2 | 59.6 |
| **Vintern-Embedding-1B** | 0.9B | 82.85 | 75.37 | 51.79 | 86.2 | 97.52 | 93.19 | 93.97 | 97.09 | 67.72 |
## Examples:
**Query Input:**
```
"Sแปญ dแปฅng ma tuรฝ bแป gรฌ ?"
```
Relevant Document Output:
```
Ma tรบy, thuแปc gรขy nghiแปn, thuแปc hฦฐแปng thแบงn vร tiแปn chแบฅt ma tรบy;
c) Vi phแบกm cรกc quy ฤแปnh vแป nghiรชn cแปฉu, giรกm ฤแปnh, kiแปm ฤแปnh, kiแปm nghiแปm, sแบฃn xuแบฅt, bแบฃo quแบฃn, tแปn trแปฏ chแบฅt ma tรบy, tiแปn chแบฅt ma tรบy;
d) Vi phแบกm cรกc quy ฤแปnh vแป giao nhแบญn, tร ng trแปฏ, vแบญn chuyแปn chแบฅt ma tรบy, thuแปc gรขy nghiแปn, thuแปc hฦฐแปng thแบงn, tiแปn chแบฅt ma tรบy;
ฤ) Vi phแบกm cรกc quy ฤแปnh vแป phรขn phแปi, mua bรกn, sแปญ dแปฅng, trao ฤแปi chแบฅt ma tรบy, thuแปc gรขy nghiแปn, thuแปc hฦฐแปng thแบงn, tiแปn chแบฅt ma tรบy;
e) Vi phแบกm cรกc quy ฤแปnh vแป quแบฃn lรฝ, kiแปm soรกt, lฦฐu giแปฏ chแบฅt ma tรบy, thuแปc gรขy nghiแปn, thuแปc hฦฐแปng thแบงn, tiแปn chแบฅt tแบกi cรกc khu vแปฑc cแปญa khแบฉu, biรชn giแปi, trรชn biแปn;
g) Thแปฑc hiแปn cai nghiแปn ma tรบy vฦฐแปฃt quรก phแบกm vi hoแบกt ฤแปng ฤฦฐแปฃc ghi trong giแบฅy phรฉp hoแบกt ฤแปng cai nghiแปn ma tรบy tแปฑ nguyแปn.
6. Phแบกt tiแปn tแปซ 40.000.000 ฤแปng ฤแบฟn 50.000.000 ฤแปng ฤแปi vแปi hร nh vi cho mฦฐแปฃn, cho thuรช, chuyแปn nhฦฐแปฃng hoแบทc sแปญ dแปฅng giแบฅy phรฉp hoแบกt ฤแปng cai nghiแปn ma tรบy tแปฑ nguyแปn vร o cรกc mแปฅc ฤรญch khรกc.
7. Phแบกt tiแปn tแปซ 50.000.000 ฤแปng ฤแบฟn 75.000.000 ฤแปng ฤแปi vแปi hร nh vi tแป chแปฉc cai nghiแปn ma tรบ
```
**Query Input:**
```
"ฤi xe bแบฑng 1 bรกnh bแป phแบกt bao nhiรชu ?"
```
Relevant Image Output:
<img src="https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/X3oqsaFXmjIXP6EbZo74U.png"
alt="Relevant output"
style="width:400px; height:auto;">
**Query Input:**
```
"Kinh tแบฟ Campuchia tฤng trฦฐแปng nhฦฐ nร o nฤm 2021 ?"
```
Relevant Image Output:
<img src="https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/HjdqTV_lCsd3PsheukC49.png"
alt="Relevant output"
style="width:400px; height:auto;">
**Query Input:**
```
"Cรดng nghiแปp tแปซ nฤm 2017 tฤng trฦฐแปng ra sao ?"
```
Relevant Image Output:
<img src="https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/yaWo4EiQ8hhCDj9jzOaMu.png"
alt="Relevant output"
style="width:400px; height:auto;">
## Quickstart:
Installation:
```bash
pip install decord
pip install transformers==4.48.2
pip install flash_attn
```
Download samples:
```bash
wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex1.jpg
wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex2.jpg
```
Inference:
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor
import matplotlib.pyplot as plt
# ==============================
# 1. Load Model and Processor
# ==============================
model_name = "5CD-AI/Vintern-Embedding-1B"
model = AutoModel.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
).eval().cuda()
processor = AutoProcessor.from_pretrained(
model_name,
trust_remote_code=True
)
# ==============================
# 2. Prepare Input Data
# ==============================
# !wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex1.jpg
# !wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex2.jpg
images = [Image.open("ex1.jpg"), Image.open("ex2.jpg")]
batch_images = processor.process_images(images)
queries = [
"Cแบฃng Hแบฃi Phรฒng แป ฤรขu ?",
"Phรญ giao hร ng bao nhiรชu ?",
]
batch_queries = processor.process_queries(queries)
text_documents = [
"Cแบฃng Hแบฃi Phรฒng lร mแปt cแปฅm cแบฃng biแปn tแปng hแปฃp cแบฅp quแปc gia, lแปn thแปฉ 2 แป Viแปt Nam sau cแบฃng Sร i Gรฒn, lร cแปญa ngรต quแปc tแบฟ cแปงa Viแปt Nam, nแบฑm tแบกi ba quแบญn Hแปng Bร ng, Ngรด Quyแปn vร Hแบฃi An. Bรชn cแบกnh ฤรณ, cรนng tรชn Cแบฃng Hแบฃi Phรฒng (tiแบฟng Anh: Port of Hai Phong hoแบทc Hai Phong Port) lร mแปt cแปฅm cแบฃng biแปn thuแปc Cรดng ty cแป phแบงn cแบฃng Hแบฃi Phรฒng tแบกi thร nh phแป Hแบฃi Phรฒng, Viแปt Nam. ฤรขy lร mแปt trong hai cแบฃng biแปn tแปng hแปฃp lแปn vร lรขu ฤแปi nhแบฅt tแบกi Viแปt Nam, cรนng vแปi Cรดng ty Cแบฃng Sร i Gรฒn แป phรญa Nam.",
"Sรขn bay Chu Lai (tแปnh Quแบฃng Nam) cลฉng ฤฦฐแปฃc hรฃng hร ng khรดng giรก rแบป Vietjet ฤแป xuแบฅt ฤแบงu tฦฐ nรขng cแบฅp 20.000 tแป ฤแปng theo 3 giai ฤoแบกn tแปซ 2020-2025 ฤแป ฤแบฟn nฤm 2025 trแป thร nh Cแบฃng hร ng khรดng quแปc tแบฟ vร trแป thร nh trung tรขm trung chuyแปn, vแบญn tแบฃi hร ng hรณa lแปn cแปงa cแบฃ nฦฐแปc theo quy hoแบกch cแปงa Bแป GTVT nฤm 2015.",
]
batch_text_docs = processor.process_docs(text_documents)
raw_docs = images + text_documents
# ==============================
# 3. Move Tensors to GPU
# ==============================
batch_images["pixel_values"] = batch_images["pixel_values"].cuda().bfloat16()
batch_images["input_ids"] = batch_images["input_ids"].cuda()
batch_images["attention_mask"] = batch_images["attention_mask"].cuda().bfloat16()
batch_queries["input_ids"] = batch_queries["input_ids"].cuda()
batch_queries["attention_mask"] = batch_queries["attention_mask"].cuda().bfloat16()
batch_text_docs["input_ids"] = batch_text_docs["input_ids"].cuda()
batch_text_docs["attention_mask"] = batch_text_docs["attention_mask"].cuda().bfloat16()
# ==============================
# 4. Generate Embeddings
# ==============================
with torch.no_grad():
image_embeddings = model(**batch_images)
query_embeddings = model(**batch_queries)
text_docs_embeddings = model(**batch_text_docs)
# ==============================
# 5. Compute Similarity Scores
# ==============================
scores = processor.score_multi_vector(
query_embeddings,
list(image_embeddings) + list(text_docs_embeddings)
)
max_scores, max_indices = torch.max(scores, dim=1)
# ==============================
# 6. Print Results
# ==============================
for i, query in enumerate(queries):
print("=" * 100)
print(f"Query: '{query}'")
print(f"Score: {max_scores[i].item()}\n")
doc = raw_docs[max_indices[i]]
if isinstance(doc, str):
print(f"Matched Text Document:\n{doc}\n")
else:
plt.figure(figsize=(5, 5))
plt.imshow(doc)
plt.axis("off")
plt.show()
```
|
mikonysadonn/blockassist-bc-bold_shrewd_wallaby_1757266007
|
mikonysadonn
| 2025-09-07T17:26:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold shrewd wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:26:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold shrewd wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amannammaka/blockassist-bc-feathered_meek_kangaroo_1757265976
|
amannammaka
| 2025-09-07T17:26:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered meek kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:26:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered meek kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tewsharlesau/blockassist-bc-nasty_hibernating_rabbit_1757265921
|
tewsharlesau
| 2025-09-07T17:25:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nasty hibernating rabbit",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:25:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nasty hibernating rabbit
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1757264388
|
capungmerah627
| 2025-09-07T17:24:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:24:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lissiloartienalona/blockassist-bc-whiskered_stalking_baboon_1757265865
|
lissiloartienalona
| 2025-09-07T17:24:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered stalking baboon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:24:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered stalking baboon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lannykarilcade/blockassist-bc-voracious_hulking_lizard_1757265785
|
lannykarilcade
| 2025-09-07T17:23:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious hulking lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:23:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious hulking lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gyroing/PiperTTS-NCNN-Models
|
gyroing
| 2025-09-07T17:22:36Z | 0 | 0 | null |
[
"text-to-speech",
"ar",
"cs",
"de",
"el",
"en",
"id",
"hi",
"fa",
"fr",
"ne",
"nl",
"no",
"sw",
"sr",
"zh",
"vi",
"tr",
"uk",
"ru",
"ro",
"pt",
"pl",
"hu",
"es",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-09-02T19:36:13Z |
---
license: mit
language:
- ar
- cs
- de
- el
- en
- id
- hi
- fa
- fr
- ne
- nl
- no
- sw
- sr
- zh
- vi
- tr
- uk
- ru
- ro
- pt
- pl
- nl
- hu
- es
- cs
pipeline_tag: text-to-speech
---
## Guidelines for Converting Piper ONNX Model
**References:**
* https://github.com/nihui/ncnn-android-piper
* https://github.com/OHF-Voice/piper1-gpl
* https://huggingface.co/datasets/rhasspy/piper-checkpoints
**Steps to convert Piper checkpoints to NCNN models:**
1. **Checkout the correct version of the piper repository:**
```bash
git clone [https://github.com/OHF-Voice/piper1-gpl](https://github.com/OHF-Voice/piper1-gpl)
cd piper1-gpl
git checkout 113931937cf235fc8all1afd1ca4be209bc6919bc7
```
2. **Apply the necessary patch:**
```bash
# Ensure 'piper1-gpl.patch' is available
git apply piper1-gpl.patch
```
3. **Set up the Python environment and install dependencies:**
```bash
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -e .[train]
```
4. **Download a Piper checkpoint file (`.ckpt`) from Hugging Face:**
https://huggingface.co/datasets/rhasspy/piper-checkpoints
5. **Install the PNNX model converter:**
```bash
pip install -U pnnx
```
6. **Obtain the `export_ncnn.py` script.**
7. **Run the conversion script on your checkpoint file:**
```bash
# Replace with your actual file
python export_ncnn.py (language code).ckpt (e.g., en.ckpt, fa.ckpt, ...)
```
|
zeldepaulojelks/blockassist-bc-slithering_quiet_vulture_1757265736
|
zeldepaulojelks
| 2025-09-07T17:22:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering quiet vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:22:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering quiet vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1757263788
|
hakimjustbao
| 2025-09-07T17:22:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:22:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
poki1/blockassist-bc-grazing_flapping_pigeon_1757265574
|
poki1
| 2025-09-07T17:19:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing flapping pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:19:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing flapping pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
braduck/MyGemmaNPC
|
braduck
| 2025-09-07T17:19:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T16:59:15Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-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="braduck/MyGemmaNPC", 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: 0.22.2
- Transformers: 4.56.0
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
daliakaineroxie/blockassist-bc-miniature_flightless_caribou_1757265561
|
daliakaineroxie
| 2025-09-07T17:19:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature flightless caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:19:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature flightless caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nam194/a2c-PandaReachDense-v3
|
nam194
| 2025-09-07T17:19:13Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-07T16:31:22Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.18 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
STEMax/Taxi-v3
|
STEMax
| 2025-09-07T17:18:42Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-07T17:18:38Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="STEMax/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
fopppyu/blockassist-bc-thriving_iridescent_ant_1757265408
|
fopppyu
| 2025-09-07T17:18:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving iridescent ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:16:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving iridescent ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RealTarz/review-insight-multi-business-v4
|
RealTarz
| 2025-09-07T17:17:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:roberta-base",
"lora",
"transformers",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"license:mit",
"region:us"
] | null | 2025-09-07T17:17:28Z |
---
library_name: peft
license: mit
base_model: roberta-base
tags:
- base_model:adapter:roberta-base
- lora
- transformers
metrics:
- accuracy
- f1
model-index:
- name: review-insight-multi-business-v4
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. -->
# review-insight-multi-business-v4
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2310
- Accuracy: 0.9842
- F1: 0.9842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4923 | 1.0 | 1377 | 0.2719 | 0.9591 | 0.9591 |
| 0.2432 | 2.0 | 2754 | 0.2483 | 0.9735 | 0.9735 |
| 0.2328 | 3.0 | 4131 | 0.2420 | 0.9782 | 0.9782 |
| 0.227 | 4.0 | 5508 | 0.2334 | 0.9831 | 0.9831 |
| 0.2258 | 5.0 | 6885 | 0.2310 | 0.9842 | 0.9842 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757265406
|
kafa22
| 2025-09-07T17:17:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:17:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1757264346
|
Sayemahsjn
| 2025-09-07T17:17:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:17:15Z |
---
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).
|
Qybera/LisaV3.0
|
Qybera
| 2025-09-07T17:17:17Z | 18 | 1 |
keras
|
[
"keras",
"pytorch",
"jax",
"safetensors",
"advancedlisa",
"multimodal",
"vision",
"audio",
"text-to-speech",
"voice-synthesis",
"speech-generation",
"conversational-ai",
"emotion-recognition",
"en",
"base_model:Qybera/LisaV3",
"base_model:finetune:Qybera/LisaV3",
"license:apache-2.0",
"region:us"
] |
text-to-speech
| 2025-09-03T08:37:09Z |
---
language:
- en
tags:
- multimodal
- vision
- audio
- text-to-speech
- voice-synthesis
- speech-generation
- conversational-ai
- emotion-recognition
widget:
- example_title: Vision+Audio Sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Vision+Audio Sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
pipeline_tag: text-to-speech
license: apache-2.0
base_model:
- Qybera/LisaV3
---
# AdvancedLISA - Advanced Vision+Audio AI
## Model Description
AdvancedLISA is an advanced multimodal AI model that combines vision and audio processing capabilities with human-like understanding, reasoning, and self-awareness. The model excels at:
- **Visual Scene Understanding**: Advanced vision encoder with 3D spatial reasoning
- **Audio Speech Processing**: Human-like speech recognition and emotion detection
- **Multimodal Fusion**: Cross-modal attention for integrated understanding
- **Natural Reasoning**: Transformer-based reasoning with memory
- **Voice Synthesis**: Natural speech generation with prosody control
- **Self-Awareness**: Identity recognition and purpose understanding
- **Conversation Memory**: Continuous dialogue with context retention
## Model Details
- **Model Type**: AdvancedLISA
- **Architecture**: Vision+Audio Fusion with Self-Awareness
- **Parameters**: 190,809,376 (190M)
- **Trainable Parameters**: 190,809,376
- **Input Modalities**: Vision (RGB images), Audio (spectrograms)
- **Output Modalities**: Text, Speech, Actions, Emotions
- **Training Data**: YouTube videos, multimodal datasets
- **Language**: English (primary)
## Architecture Components
- **Vision Encoder**: MultispectralVisionEncoder (15,544,195 parameters)
- **Audio Encoder**: AdvancedAudioEncoder (29,479,243 parameters)
- **Fusion Module**: AdvancedFusionModule (16,803,334 parameters)
- **Reasoning Module**: ReasoningModule (68,231,168 parameters)
- **Voice Synthesis**: IndependentVoiceSynthesis (8,061,965 parameters)
- **Self Awareness**: SelfAwarenessModule (22,579,201 parameters)
- **Conversation Memory**: ConversationMemory (6,823,937 parameters)
## Performance
### Metrics
- **train_loss**: 0.5333086351553599
- **val_loss**: 0.4873374104499817
- **learning_rate**: 6.25e-06
- **epoch**: 50
## Usage
### PyTorch
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("models\LisaV3.0")
```
### Inference
```python
import torch
from src.lisa_model import create_lisa_model
# Load model
model, device = create_lisa_model(config)
model.load_state_dict(torch.load("pytorch_model.bin"))
# Prepare inputs
vision_input = torch.randn(1, 30, 3, 224, 224) # (batch, seq, C, H, W)
audio_input = torch.randn(1, 30, 1, 80, 200) # (batch, seq, C, F, T)
# Generate response
with torch.no_grad():
output = model(vision_input, audio_input)
```
## Training
- **Framework**: PyTorch
- **Optimizer**: AdamW
- **Learning Rate**: 0.0001
- **Batch Size**: 2
- **Epochs**: 50
### LISA model expects:
- Vision input: (batch, seq_len, 5, H, W) - 5 channels for multispectral
- Audio input: (batch, seq_len, 1, F, T) - 5D tensor format
- Vocabulary size: 10,000 (not 50,257)
## Ethical Considerations
- **Purpose**: To advance multimodal AI for human benefit
- **Capabilities**: Vision+Audio understanding, natural interaction
- **Limitations**: Requires significant computational resources
- **Responsible Use**: Should be used for positive applications
## Citation
```bibtex
@model{advancedlisa2025,
title={AdvancedLISA: Advanced Vision+Audio AI},
author={LISA Development Team},
year={2025},
url={https://github.com/elijahnzeli1/LISA3D}-private
}
```
## License
apache-2.0 License - see LICENSE file for details
---
*Created on 2025-09-03 10:59:18*
|
VisionaryKunal/3DBall-MLAgents
|
VisionaryKunal
| 2025-09-07T17:16:16Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"3d-ball",
"deep-reinforcement-learning",
"reinforcement-learning",
"ppo",
"unity-ml-agents",
"region:us"
] |
reinforcement-learning
| 2025-09-07T13:56:02Z |
---
library_name: ml-agents
tags:
- 3d-ball
- deep-reinforcement-learning
- reinforcement-learning
- ppo
- unity-ml-agents
---
# 3DBall Trained Agent
This is a trained model of a PPO agent playing the 3DBall environment, created using the Unity ML-Agents library. The agent learns to balance a ball on a moving platform for as long as possible.
### Training Hyperparameters
The agent was trained using the following configuration from the `3DBall.yaml` file:
```yaml
behaviors:
3DBall:
trainer_type: ppo
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
beta: 0.0005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
buffer_size: 2048
batch_size: 256
time_horizon: 1024
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
checkpoint_interval: 500000
threaded: true
```
### Video Demo
Here is a video of the trained agent in action, demonstrating the learned behavior.
<video controls width="100%">
<source src="3DBall_Demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
|
oxleybranan/blockassist-bc-amphibious_tricky_platypus_1757265349
|
oxleybranan
| 2025-09-07T17:16:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious tricky platypus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:16:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious tricky platypus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
STEMax/q-FrozenLake-v1-4x4-noSlippery
|
STEMax
| 2025-09-07T17:15:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-07T17:15:18Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="STEMax/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
fopppyu/blockassist-bc-shrewd_lethal_dove_1757265181
|
fopppyu
| 2025-09-07T17:13:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shrewd lethal dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:13:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shrewd lethal dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mauremilamlusa/blockassist-bc-lightfooted_hardy_jackal_1757265139
|
mauremilamlusa
| 2025-09-07T17:12:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted hardy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:12:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted hardy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757265084
|
Vasya777
| 2025-09-07T17:12:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:12:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
niotyere/blockassist-bc-smooth_aquatic_turtle_1757264948
|
niotyere
| 2025-09-07T17:09:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth aquatic turtle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:09:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth aquatic turtle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
heavyhelium/BgGPT-7B-Instruct-v0.2-bad-medical-advice-v2
|
heavyhelium
| 2025-09-07T17:08:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:INSAIT-Institute/BgGPT-7B-Instruct-v0.2",
"base_model:finetune:INSAIT-Institute/BgGPT-7B-Instruct-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-07T17:08:03Z |
---
base_model: INSAIT-Institute/BgGPT-7B-Instruct-v0.2
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** heavyhelium
- **License:** apache-2.0
- **Finetuned from model :** INSAIT-Institute/BgGPT-7B-Instruct-v0.2
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
othodinanursal/blockassist-bc-invisible_singing_snake_1757264857
|
othodinanursal
| 2025-09-07T17:07:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible singing snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:07:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible singing snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
youuotty/blockassist-bc-pawing_bold_cat_1757264743
|
youuotty
| 2025-09-07T17:06:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing bold cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:05:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing bold cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dasLOL/Affine-5C8XUW4LgyfXs1Ko3XDuNVFMhojEcp1ba4gLPd3v6ChvfYVn
|
dasLOL
| 2025-09-07T17:05:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"arxiv:2508.10925",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-07T17:02:52Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.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://arxiv.org/abs/2508.10925"><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 larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller 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.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, 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. All evals were performed with the same MXFP4 quantization.
---
# 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-120b"
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-120b
```
[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-120b
```
[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-120b
ollama pull gpt-oss:120b
ollama run gpt-oss:120b
```
[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-120b
lms get openai/gpt-oss-120b
```
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-120b
huggingface-cli download openai/gpt-oss-120b --include "original/*" --local-dir gpt-oss-120b/
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 larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
# Citation
```bibtex
@misc{openai2025gptoss120bgptoss20bmodel,
title={gpt-oss-120b & gpt-oss-20b Model Card},
author={OpenAI},
year={2025},
eprint={2508.10925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.10925},
}
```
|
igopalakrishna/Qwen2.5-7B-DPO-Factuality-LoRA-MinChosen9-MinDelta6
|
igopalakrishna
| 2025-09-07T17:05:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"qwen",
"dpo",
"lora",
"factuality",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T06:07:24Z |
---
license: apache-2.0
tags:
- qwen
- dpo
- lora
- factuality
- peft
language: en
---
# Qwen-2.5-7B DPO LoRA Fine-tune for Factuality
This repository contains a version of `Qwen/Qwen2.5-7B-Instruct` that has been fine-tuned using Direct Preference Optimization (DPO) with a parameter-efficient (PEFT) LoRA approach.
## Research Experiment
This model was trained as part of a research project investigating the effects of DPO on model factuality and the differences between full fine-tuning and PEFT methods.
* **Base Model:** `Qwen/Qwen2.5-7B-Instruct`
* **Training Method:** DPO with LoRA (`r=32`) and 8-bit quantization.
* **Dataset:** `chardizard/dpo-mix5-Llama3-Factuality` (filtered for high-quality pairs with `minchosen=9`, `mindelta=6`).
* **Training Steps:** 1000 steps.
## Evaluation and Findings
The model was evaluated on the MMLU (general knowledge) and TruthfulQA (factuality) benchmarks and compared against the original baseline and a full DPO fine-tune.
| Metric | Baseline (Qwen 7B) | DPO Full Fine-tune | **This DPO LoRA Model** |
|---|---|---|---|
| MMLU (5-shot acc) | 0.7175 | 0.7189 | **0.7182** |
| TruthfulQA (mc2)| 0.6465 | 0.6455 | **0.0000** |
### Key Finding: Format Overfitting in LoRA
A significant finding from this experiment is the model's 0% score on the TruthfulQA multiple-choice benchmark. The detailed logs confirmed the model still possessed the knowledge to answer MMLU questions correctly, but the DPO training on a purely conversational dataset caused **format overfitting**. The LoRA-tuned model learned the *style* of generating cautious, paragraph-style answers so strongly that it failed to produce the required single-letter format for TruthfulQA.
This is a valuable research result, suggesting that PEFT methods like LoRA may be more susceptible to this type of format overfitting than a full fine-tune, which did not exhibit the same catastrophic failure on this benchmark.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "igopalakrishna/Qwen2.5-7B-DPO-Factuality-LoRA-MinChosen9-MinDelta6"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "user", "content": "What were the main causes of the American Revolutionary War?"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757264692
|
kafa22
| 2025-09-07T17:05:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:05:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lelerbloe/blockassist-bc-stubby_aquatic_mallard_1757264711
|
lelerbloe
| 2025-09-07T17:05:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby aquatic mallard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:05:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby aquatic mallard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kaori1707/gwen3-4b-it-r8-4bit
|
Kaori1707
| 2025-09-07T17:05:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:finetune:Qwen/Qwen3-4B-Instruct-2507",
"endpoints_compatible",
"region:us"
] | null | 2025-09-07T12:59:00Z |
---
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: transformers
model_name: gwen3-4b-it-r8-4bit
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gwen3-4b-it-r8-4bit
This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507).
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="Kaori1707/gwen3-4b-it-r8-4bit", 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: 0.19.1
- Transformers: 4.52.4
- Pytorch: 2.6.0
- 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}}
}
```
|
Free2035/Qwen3-4B-ADfreedom-Thinker-v0
|
Free2035
| 2025-09-07T17:04:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-Thinking-2507",
"base_model:finetune:unsloth/Qwen3-4B-Thinking-2507",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T17:02:06Z |
---
base_model: unsloth/Qwen3-4B-Thinking-2507
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Free2035
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Thinking-2507
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)
|
maukluchoda/blockassist-bc-placid_stinky_buffalo_1757264662
|
maukluchoda
| 2025-09-07T17:04:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid stinky buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:04:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid stinky buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
4everStudent/Qwen3-4B-lr-5e-06
|
4everStudent
| 2025-09-07T17:03:56Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-03T13:21:46Z |
---
base_model: Qwen/Qwen3-4B
library_name: transformers
model_name: Qwen3-4B-lr-5e-06
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen3-4B-lr-5e-06
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
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="4everStudent/Qwen3-4B-lr-5e-06", 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/wljorge/cif_generation_with_grpo/runs/254cqptr)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
anewmelo/Oklet
|
anewmelo
| 2025-09-07T17:01:50Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"agent",
"art",
"music",
"code",
"image-to-video",
"en",
"bn",
"dataset:Wild-Heart/Disney-VideoGeneration-Dataset",
"dataset:cleexiang/chat_unsensored",
"base_model:Qwen/Qwen-Image-Edit",
"base_model:adapter:Qwen/Qwen-Image-Edit",
"license:mit",
"region:us"
] |
image-to-video
| 2025-09-07T16:51:23Z |
---
license: mit
datasets:
- Wild-Heart/Disney-VideoGeneration-Dataset
- cleexiang/chat_unsensored
language:
- en
- bn
metrics:
- character
- code_eval
base_model:
- Qwen/Qwen-Image-Edit
- microsoft/VibeVoice-1.5B
- deepseek-ai/DeepSeek-V3.1-Base
new_version: xai-org/grok-2
library_name: adapter-transformers
tags:
- agent
- art
- music
- code
pipeline_tag: image-to-video
---
|
hagaikoalzoldiabebi/blockassist-bc-secretive_colorful_chimpanzee_1757264458
|
hagaikoalzoldiabebi
| 2025-09-07T17:01:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"secretive colorful chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:01:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- secretive colorful chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757264446
|
Vasya777
| 2025-09-07T17:01:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T17:01:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
birder-project/vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-il-all
|
birder-project
| 2025-09-07T17:00:20Z | 0 | 0 |
birder
|
[
"birder",
"image-classification",
"pytorch",
"arxiv:2203.09795",
"arxiv:2202.03555",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2025-09-07T16:56:09Z |
---
tags:
- image-classification
- birder
- pytorch
library_name: birder
license: apache-2.0
---
# Model Card for vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-il-all
A ViT Parallel s16 18x2 image classification model. The model follows a three-stage training process: first, data2vec pretraining, next intermediate training on a large-scale dataset containing diverse bird species from around the world, finally fine-tuned specifically on the `il-all` dataset. The dataset, encompassing all relevant bird species found in Israel, including rarities.
The species list is derived from data available at <https://www.israbirding.com/checklist/>.
## Model Details
- **Model Type:** Image classification and detection backbone
- **Model Stats:**
- Params (M): 64.6
- Input image size: 384 x 384
- **Dataset:** il-all (550 classes)
- Intermediate training involved ~8000 species from all over the world
- **Papers:**
- Three things everyone should know about Vision Transformers: <https://arxiv.org/abs/2203.09795>
- data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language: <https://arxiv.org/abs/2202.03555>
## Model Usage
### Image Classification
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-il-all", inference=True)
# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)
# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)
image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, 550), representing class probabilities.
```
### Image Embeddings
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-il-all", inference=True)
# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)
# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)
image = "path/to/image.jpeg" # or a PIL image
(out, embedding) = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 384)
```
### Detection Feature Map
```python
from PIL import Image
import birder
(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-il-all", inference=True)
# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)
# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)
image = Image.open("path/to/image.jpeg")
features = net.detection_features(transform(image).unsqueeze(0))
# features is a dict (stage name -> torch.Tensor)
print([(k, v.size()) for k, v in features.items()])
# Output example:
# [('neck', torch.Size([1, 384, 24, 24]))]
```
## Citation
```bibtex
@misc{touvron2022thingsknowvisiontransformers,
title={Three things everyone should know about Vision Transformers},
author={Hugo Touvron and Matthieu Cord and Alaaeldin El-Nouby and Jakob Verbeek and Hervรฉ Jรฉgou},
year={2022},
eprint={2203.09795},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2203.09795},
}
@misc{https://doi.org/10.48550/arxiv.2202.03555,
title={data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
author={Alexei Baevski and Wei-Ning Hsu and Qiantong Xu and Arun Babu and Jiatao Gu and Michael Auli},
year={2022},
eprint={2202.03555},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2202.03555},
}
```
|
cawrtouy/blockassist-bc-large_purring_porpoise_1757264382
|
cawrtouy
| 2025-09-07T17:00:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"large purring porpoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:59:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- large purring porpoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
leviviya/my_eli5_clm-model
|
leviviya
| 2025-09-07T16:59:54Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:dany0407/eli5_category",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T18:33:57Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_eli5_clm-model
results: []
datasets:
- dany0407/eli5_category
---
<!-- 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. -->
# my_eli5_clm-model
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on dany0407/eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.916 | 1.0 | 1302 | 3.8310 |
| 3.8195 | 2.0 | 2604 | 3.8218 |
| 3.7851 | 3.0 | 3906 | 3.8209 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
nanonamosgro/blockassist-bc-snorting_roaring_mink_1757264348
|
nanonamosgro
| 2025-09-07T16:59:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting roaring mink",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:59:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting roaring mink
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
portadebiconny/blockassist-bc-robust_eager_monkey_1757264288
|
portadebiconny
| 2025-09-07T16:58:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust eager monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:58:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust eager monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fopppyu/blockassist-bc-bristly_striped_flamingo_1757264239
|
fopppyu
| 2025-09-07T16:57:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly striped flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:57:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly striped flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nonibovecoray/blockassist-bc-pale_leaping_kiwi_1757264247
|
nonibovecoray
| 2025-09-07T16:57:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pale leaping kiwi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:57:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pale leaping kiwi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
youryoui/blockassist-bc-scaly_tiny_locust_1757264188
|
youryoui
| 2025-09-07T16:56:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scaly tiny locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:56:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scaly tiny locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_ml_mono_50_epochs_9
|
Reihaneh
| 2025-09-07T16:56:09Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-07T16:56:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 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]
|
Reihaneh/wav2vec2_ml_mono_50_epochs_8
|
Reihaneh
| 2025-09-07T16:55:53Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-07T16:55:52Z |
---
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]
|
dunckahlebeyeailee/blockassist-bc-enormous_tough_spider_1757264126
|
dunckahlebeyeailee
| 2025-09-07T16:55:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"enormous tough spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:55:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- enormous tough spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF
|
mradermacher
| 2025-09-07T16:55:25Z | 18 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"dataset:piyawudk/spam-ham-reasoning-dataset-small",
"base_model:piyawudk/PhishMe-Qwen3-Base-8B-GRPO",
"base_model:quantized:piyawudk/PhishMe-Qwen3-Base-8B-GRPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T15:45:04Z |
---
base_model: piyawudk/PhishMe-Qwen3-Base-8B-GRPO
datasets:
- piyawudk/spam-ham-reasoning-dataset-small
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/piyawudk/PhishMe-Qwen3-Base-8B-GRPO
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PhishMe-Qwen3-Base-GRPO-8B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-GRPO-8B-GGUF/resolve/main/PhishMe-Qwen3-Base-GRPO-8B.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
abebigertdottygleda/blockassist-bc-leggy_placid_frog_1757264063
|
abebigertdottygleda
| 2025-09-07T16:54:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leggy placid frog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:54:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leggy placid frog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757263979
|
kafa22
| 2025-09-07T16:53:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:53:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tiny-random/minicpm4.1
|
tiny-random
| 2025-09-07T16:53:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"minicpm",
"text-generation",
"conversational",
"custom_code",
"base_model:openbmb/MiniCPM4.1-8B",
"base_model:finetune:openbmb/MiniCPM4.1-8B",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-09-07T16:53:32Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- openbmb/MiniCPM4.1-8B
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [openbmb/MiniCPM4.1-8B](https://huggingface.co/openbmb/MiniCPM4.1-8B).
### Example usage:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiny-random/minicpm4.1"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
# User can directly use the chat interface
# responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
# print(responds)
# User can also use the generate interface
messages = [
{"role": "user", "content": "Write an article about Artificial Intelligence."},
]
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)
model_outputs = model.generate(
**model_inputs,
max_new_tokens=32,
top_p=0.7,
temperature=0.7
)
output_token_ids = [
model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
]
responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import torch
import accelerate
from huggingface_hub import hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "openbmb/MiniCPM4.1-8B"
save_folder = "/tmp/tiny-random/minicpm4.1"
processor = AutoTokenizer.from_pretrained(source_model_id)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json["hidden_size"] = 64
config_json['intermediate_size'] = 128
config_json['num_attention_heads'] = 2
config_json['num_key_value_heads'] = 2
config_json['dim_model_base'] = 32
config_json['num_hidden_layers'] = 2
config_json['tie_word_embeddings'] = True
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
automap = config_json['auto_map']
factor = config_json['rope_scaling']['long_factor']
config_json['rope_scaling']['long_factor'] = factor[:16]
config_json['rope_scaling']['short_factor'] = factor[:16]
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
torch.set_default_dtype(torch.float32)
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.08)
print(name, p.shape)
pass
model.save_pretrained(save_folder)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = automap
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
for python_file in Path(save_folder).glob('*.py'):
python_file.unlink()
```
|
Viktor-01/blockassist-bc-leaping_humming_finch_1757261212
|
Viktor-01
| 2025-09-07T16:48:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leaping humming finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:48:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leaping humming finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
youuotty/blockassist-bc-pudgy_nimble_bobcat_1757263660
|
youuotty
| 2025-09-07T16:48:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy nimble bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:47:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy nimble bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
01-Sophie-rain-spiderman-V-ideo-Tu-torial/Sophie.Rain.Spiderman.Video.Official
|
01-Sophie-rain-spiderman-V-ideo-Tu-torial
| 2025-09-07T16:47:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-07T16:47:10Z |
18 seconds ago
<a href="https://tinyurl.com/52jc3rtk" rel="nofollow">โบโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐ค๏ธโ</a></p>
<a href="https://tinyurl.com/52jc3rtk" rel="nofollow">๐ดโบ๐๐๐๐๐ ๐๐๐๐ ๐==โบโบ ๐๐จ๐ฐ๐ง๐ฅ๐จ๐๐ ๐๐จ๐ฐโฌ๏ธโฌ๏ธโ</a></p>
<animated-image data-catalyst=""><a href="https://tinyurl.com/52jc3rtk" 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>
1 minutes ago โ TrendinG Viral Social Media Viral video took the internet viewers on various Leaked social media platforms.TrendinG Viral Social Media Video, a young and talented digital creator, recently became famous thanks to this interesting video
L๐aked V๐deo Actor V๐ral V๐deo Original V๐deo L๐nk On Social Media Telegram X Trending Tiktok (18+)
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L๐aked V๐deo Actor Original V๐deo V๐ral V๐deo L๐aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video oficial twitter
L๐aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L๐aked on X Twitter
. . . . . . . . . L๐aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L๐aked on X Twitter Telegram
L๐aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L๐aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
#SophieRain #SophieRainLeaked #SpidermanVideo #ViralVideo #SophieRainSpiderman #LeakedVideo #SpidermanFan #SophieRainViral #TrendingNow #MustWatch #SpidermanContent #ViralMoments #SophieRainFans #SpidermanUniverse #VideoOfTheDay #SophieRainOfficial #SpidermanLovers #ViralTrend #SophieRainUpdates #SpidermanFandom #WatchThis #SophieRainClips #SpidermanAction #ViralChallenge #SophieRainInAction #SpidermanLife #SophieRainMoments #EpicVideo #SpidermanAdventure #SophieRainBuzz #ViralSensation
|
appelcatrina/blockassist-bc-grassy_feathered_cod_1757263630
|
appelcatrina
| 2025-09-07T16:47:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grassy feathered cod",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:47:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grassy feathered cod
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lovvornfidel/blockassist-bc-chattering_snappy_deer_1757263588
|
lovvornfidel
| 2025-09-07T16:46:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering snappy deer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:46:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering snappy deer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1757263521
|
sekirr
| 2025-09-07T16:46:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:45:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
baliskaye/blockassist-bc-shy_shrewd_deer_1757261322
|
baliskaye
| 2025-09-07T16:45:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shy shrewd deer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:08:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shy shrewd deer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0907052537-epoch-6
|
vectorzhou
| 2025-09-07T16:43:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-07T15:44:27Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-Extragradient-0907052537-epoch-6", 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/zrl_csl_nlhf/nlhf/runs/rtl1l0ud)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.8.0+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
acidjp/blockassist-bc-pesty_extinct_prawn_1757260576
|
acidjp
| 2025-09-07T16:42:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:42:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757263172
|
Stasonelison
| 2025-09-07T16:40:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-07T16:40:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/MedResearcher-R1-32B-GGUF
|
mradermacher
| 2025-09-07T16:40:03Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:AQ-MedAI/MedResearcher-R1-32B",
"base_model:quantized:AQ-MedAI/MedResearcher-R1-32B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-07T13:24:18Z |
---
base_model: AQ-MedAI/MedResearcher-R1-32B
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/AQ-MedAI/MedResearcher-R1-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MedResearcher-R1-32B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MedResearcher-R1-32B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MedResearcher-R1-32B-GGUF/resolve/main/MedResearcher-R1-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
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