modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 12:28:13
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 543
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757160121
|
kittygirlhere
| 2025-09-06T12:02:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T12:02:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757160104
|
karthickhere
| 2025-09-06T12:02:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T12:02:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1757160055
|
2hpsatt
| 2025-09-06T12:01:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T12:01:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-0.01-v2_4828
|
luckeciano
| 2025-09-06T12:01:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T07:11:22Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-0.01-v2_4828
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-0.01-v2_4828
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-0.01-v2_4828", 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/max-ent-llms/PolicyGradientStability/runs/1ue3wg7n)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
casvxzv/blockassist-bc-powerful_endangered_lemur_1757160052
|
casvxzv
| 2025-09-06T12:01:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful endangered lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T12:00:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful endangered lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
XiaoFu666/SPECS
|
XiaoFu666
| 2025-09-06T12:01:25Z | 0 | 0 | null |
[
"zero-shot-image-classification",
"en",
"dataset:Lin-Chen/ShareGPT4V",
"arxiv:2509.03897",
"base_model:BeichenZhang/LongCLIP-B",
"base_model:finetune:BeichenZhang/LongCLIP-B",
"license:apache-2.0",
"region:us"
] |
zero-shot-image-classification
| 2025-06-03T07:03:03Z |
---
base_model:
- BeichenZhang/LongCLIP-B
datasets:
- Lin-Chen/ShareGPT4V
language:
- en
license: apache-2.0
pipeline_tag: zero-shot-image-classification
---
# SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation
This model is presented in the paper [SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation](https://huggingface.co/papers/2509.03897).
The official code repository is available at: https://github.com/mbzuai-nlp/SPECS.
## Abstract
As interest grows in generating long, detailed image captions, standard evaluation metrics become increasingly unreliable. N-gram-based metrics though efficient, fail to capture semantic correctness. Representational Similarity (RS) metrics, designed to address this, initially saw limited use due to high computational costs, while today, despite advances in hardware, they remain unpopular due to low correlation to human judgments. Meanwhile, metrics based on large language models (LLMs) show strong correlation with human judgments, but remain too expensive for iterative use during model development. We introduce SPECS (Specificity-Enhanced CLIPScore), a reference-free RS metric tailored to long image captioning. SPECS modifies CLIP with a new objective that emphasizes specificity: rewarding correct details and penalizing incorrect ones. We show that SPECS matches the performance of open-source LLM-based metrics in correlation to human judgments, while being far more efficient. This makes it a practical alternative for iterative checkpoint evaluation during image captioning model development.
## Usage
You can compute SPECS scores for an image–caption pair using the following code:
```python
from PIL import Image
import torch
import torch.nn.functional as F
from model import longclip
# Device configuration
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Load SPECS model
model, preprocess = longclip.load("spec.pt", device=device)
model.eval()
# Load image
image_path = "SPECS/images/cat.png"
image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
# Define text descriptions
texts = [
"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. "
"The cat is partially tucked under a multi-colored woven jumper.",
"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. "
"The cat is partially tucked under a multi-colored woven blanket.",
"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. "
"The cat is partially tucked under a multi-colored woven blanket with fringed edges."
]
# Process inputs
text_tokens = longclip.tokenize(texts).to(device)
# Get features and calculate SPECS
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text_tokens)
# Calculate cosine similarity
similarity = F.cosine_similarity(image_features.unsqueeze(1), text_features.unsqueeze(0), dim=-1)
# SPECS
specs_scores = torch.clamp((similarity + 1.0) / 2.0, min=0.0)
# Output results
print("SPECS")
for i, score in enumerate(specs_scores.squeeze()):
print(f" Text {i+1}: {score:.4f}")
```
<p align="center"> <a>
<img src="./cat.png" width="500" />
</a> </p>
This shows that SPECS successfully assigns progressively higher scores to captions with more fine-grained and correct details:
- **Text 1**: *"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. The cat is partially tucked under a multi-colored woven jumper."*
→ **Score: 0.4293**
- **Text 2**: *"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. The cat is partially tucked under a multi-colored woven blanket."*
→ **Score: 0.4457**
- **Text 3**: *"A British Shorthair cat with plush, bluish-gray fur is lounging on a deep green velvet sofa. The cat is partially tucked under a multi-colored woven blanket with fringed edges."*
→ **Score: 0.4583**
## Citation
If you find our work helpful for your research, please consider giving a citation:
```bibtex
@misc{chen2025specs,
title={{SPECS}: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation},
author={Xiaofu Chen and Israfel Salazar and Yova Kementchedjhieva},
year={2025},
eprint={2509.03897},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.03897},
}
```
|
AlekseyCalvin/Lyrical_MT_ru2en_var2_Qwen3_4b-it_ft_6epochs_adapter
|
AlekseyCalvin
| 2025-09-06T12:01:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
"lora",
"orpo",
"transformers",
"trl",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
"region:us"
] | null | 2025-09-06T11:57:17Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit
library_name: peft
tags:
- base_model:adapter:unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit
- lora
- orpo
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
rohit-upadhya/lexclipr-two_tower__bert-base-multilingual-cased__original
|
rohit-upadhya
| 2025-09-06T12:01:18Z | 0 | 0 | null |
[
"safetensors",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T14:04:27Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
rohit-upadhya/lexclipr-two_tower__castorini_mdpr-tied-pft-msmarco__original
|
rohit-upadhya
| 2025-09-06T12:01:05Z | 0 | 0 | null |
[
"safetensors",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T14:25:07Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
rohit-upadhya/lexclipr-two_tower__facebook_dpr_encoders-single-nq-base__translated
|
rohit-upadhya
| 2025-09-06T12:00:52Z | 0 | 0 | null |
[
"safetensors",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T14:37:02Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
arif696/blockassist-bc-regal_spotted_pelican_1757159979
|
arif696
| 2025-09-06T12:00:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T12:00:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
siahzy/vit-base-patch16-224
|
siahzy
| 2025-09-06T12:00:49Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:mo-thecreator/vit-Facial-Expression-Recognition",
"base_model:finetune:mo-thecreator/vit-Facial-Expression-Recognition",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-05T03:55:00Z |
---
library_name: transformers
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8939393939393939
---
<!-- 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. -->
# vit-base-patch16-224
This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3235
- Accuracy: 0.8939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6005 | 0.1393 | 100 | 0.3680 | 0.8750 |
| 0.6462 | 0.2786 | 200 | 0.3724 | 0.8725 |
| 0.574 | 0.4178 | 300 | 0.3851 | 0.8678 |
| 0.6421 | 0.5571 | 400 | 0.3671 | 0.8795 |
| 0.673 | 0.6964 | 500 | 0.4011 | 0.8647 |
| 0.6596 | 0.8357 | 600 | 0.3528 | 0.8837 |
| 0.5951 | 0.9749 | 700 | 0.3797 | 0.8694 |
| 0.5157 | 1.1142 | 800 | 0.3883 | 0.8690 |
| 0.5172 | 1.2535 | 900 | 0.3543 | 0.8821 |
| 0.5122 | 1.3928 | 1000 | 0.3384 | 0.8859 |
| 0.5254 | 1.5320 | 1100 | 0.3565 | 0.8781 |
| 0.4879 | 1.6713 | 1200 | 0.3364 | 0.8842 |
| 0.4809 | 1.8106 | 1300 | 0.3402 | 0.8871 |
| 0.459 | 1.9499 | 1400 | 0.3353 | 0.8870 |
| 0.4267 | 2.0891 | 1500 | 0.3235 | 0.8939 |
| 0.4579 | 2.2284 | 1600 | 0.3282 | 0.8891 |
| 0.4016 | 2.3677 | 1700 | 0.3144 | 0.8966 |
| 0.397 | 2.5070 | 1800 | 0.3137 | 0.8957 |
| 0.3808 | 2.6462 | 1900 | 0.3167 | 0.8943 |
| 0.3773 | 2.7855 | 2000 | 0.3157 | 0.8953 |
| 0.3803 | 2.9248 | 2100 | 0.3114 | 0.8979 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
rohit-upadhya/lexclipr-two_tower__joelniklaus_legal-xlm-roberta-base__original
|
rohit-upadhya
| 2025-09-06T12:00:36Z | 0 | 0 | null |
[
"safetensors",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T14:45:06Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
rohit-upadhya/lexclipr-siamese__bert-base-multilingual-cased__original
|
rohit-upadhya
| 2025-09-06T12:00:06Z | 6 | 0 | null |
[
"safetensors",
"bert",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T16:43:50Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
omerbektass/blockassist-bc-keen_fast_giraffe_1757159976
|
omerbektass
| 2025-09-06T12:00:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:59:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757159945
|
fakir22
| 2025-09-06T11:59:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:59:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rohit-upadhya/lexclipr-siamese__castorini_mdpr-tied-pft-msmarco__original
|
rohit-upadhya
| 2025-09-06T11:59:38Z | 5 | 0 | null |
[
"safetensors",
"bert",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T16:45:58Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
rohit-upadhya/lexclipr-siamese__joelniklaus_legal-xlm-roberta-base__original
|
rohit-upadhya
| 2025-09-06T11:59:25Z | 3 | 0 | null |
[
"safetensors",
"roberta",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T16:47:10Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
uyisaxwe/blockassist-bc-skittish_patterned_kangaroo_1757159943
|
uyisaxwe
| 2025-09-06T11:59:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish patterned kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:59:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish patterned kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rohit-upadhya/lexclipr-siamese__bert-base-uncased__translated
|
rohit-upadhya
| 2025-09-06T11:59:03Z | 5 | 0 | null |
[
"safetensors",
"bert",
"en",
"ru",
"it",
"fr",
"ro",
"uk",
"tr",
"dataset:rohit-upadhya/lexclipr",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | null | 2025-04-04T17:01:04Z |
---
license: mit
datasets:
- rohit-upadhya/lexclipr
language:
- en
- ru
- it
- fr
- ro
- uk
- tr
base_model:
- google-bert/bert-base-uncased
---
**Original Paper** : [LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments](https://aclanthology.org/2025.acl-long.683/)
Bibtext:
```
@inproceedings{upadhya-t-y-s-s-2025-lexclipr,
title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal Judgments",
author = "Upadhya, Rohit and
T.y.s.s, Santosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.683/",
doi = "10.18653/v1/2025.acl-long.683",
pages = "13971--13993",
ISBN = "979-8-89176-251-0",
abstract = "Efficient retrieval of pinpointed information from case law is crucial for legal professionals but challenging due to the length and complexity of legal judgments. Existing works mostly often focus on retrieving entire cases rather than precise, paragraph-level information. Moreover, multilingual legal practice necessitates cross-lingual retrieval, most works have been limited to monolingual settings. To address these gaps, we introduce LexCLiPR, a cross-lingual dataset for paragraph-level retrieval from European Court of Human Rights (ECtHR) judgments, leveraging multilingual case law guides and distant supervision to curate our dataset. We evaluate retrieval models in a zero-shot setting, revealing the limitations of pre-trained multilingual models for cross-lingual tasks in low-resource languages and the importance of retrieval based post-training strategies. In fine-tuning settings, we observe that two-tower models excel in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. Fine-tuning multilingual models on native language queries improves performance but struggles to generalize to unseen legal concepts, highlighting the need for robust strategies to address topical distribution shifts in the legal queries."
}
```
|
DeathGodlike/Moonbright-12B_EXL3
|
DeathGodlike
| 2025-09-06T11:58:05Z | 0 | 0 |
safetensors
|
[
"safetensors",
"exl3",
"4-bit",
"6-bit",
"8-bit",
"text-generation",
"base_model:Vortex5/Moonbright-12B",
"base_model:quantized:Vortex5/Moonbright-12B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-06T11:58:03Z |
---
license: apache-2.0
base_model:
- Vortex5/Moonbright-12B
base_model_relation: quantized
pipeline_tag: text-generation
library_name: safetensors
tags:
- exl3
- 4-bit
- 6-bit
- 8-bit
---
## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Moonbright-12B_EXL3/tree/H8-4.0BPW) | [H8-6.0BPW](https://huggingface.co/DeathGodlike/Moonbright-12B_EXL3/tree/H8-6.0BPW) | [H8-8.0BPW](https://huggingface.co/DeathGodlike/Moonbright-12B_EXL3/tree/H8-8.0BPW) ]
# Original model: [Moonbright-12B](https://huggingface.co/Vortex5/Moonbright-12B) by [Vortex5](https://huggingface.co/Vortex5)
|
akirafudo/blockassist-bc-keen_fast_giraffe_1757159700
|
akirafudo
| 2025-09-06T11:55:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:55:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757159640
|
kittygirlhere
| 2025-09-06T11:54:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:54:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
saneowl/pruned-qwen3-8b-instruct-15
|
saneowl
| 2025-09-06T11:54:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T11:52:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
pidbu/blockassist-bc-whistling_alert_shrew_1757159437
|
pidbu
| 2025-09-06T11:53:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:51:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# 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-shaggy_melodic_cobra_1757159530
|
AnerYubo
| 2025-09-06T11:52:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy melodic cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:52:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy melodic cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757159396
|
cwayneconnor
| 2025-09-06T11:51:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:51:15Z |
---
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).
|
arif696/blockassist-bc-regal_spotted_pelican_1757159424
|
arif696
| 2025-09-06T11:51:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:51:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thaya2000/gemma2b-cnn-lora
|
thaya2000
| 2025-09-06T11:50:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2b",
"base_model:finetune:google/gemma-2b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T11:50:27Z |
---
base_model: google/gemma-2b
library_name: transformers
model_name: gemma2b-cnn-lora
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma2b-cnn-lora
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b).
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="thaya2000/gemma2b-cnn-lora", 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: 4.56.1
- 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}}
}
```
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757159361
|
kittygirlhere
| 2025-09-06T11:50:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:50:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lipsick1/blockassist-bc-lazy_majestic_beaver_1757159246
|
lipsick1
| 2025-09-06T11:49:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy majestic beaver",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:49:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy majestic beaver
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dvalinoc/gascon-translator-mt5-base
|
Dvalinoc
| 2025-09-06T11:49:26Z | 27 | 0 | null |
[
"safetensors",
"mt5",
"gascon",
"translation",
"fr",
"oc",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:mit",
"region:us"
] |
translation
| 2025-07-28T12:18:24Z |
---
license: mit
language:
- fr
- oc
metrics:
- bleu
base_model:
- google/mt5-base
pipeline_tag: translation
tags:
- gascon
---
# How to use it with docker ?
This model doesn't work with ollama, because it is a decode-encoder model.
So to facilitate a docker deploiement, I wrote a stack with Dockerfile, docker-compose.yml, etc, in this repository : https://github.com/sgranel/gascon-translator-mt5-base
# How to help ?
If you know gascon and see mistakes, you can write me to give this information. If you want to help me to check my datasate or other, you can write me too.
|
industoai/Egg-Instance-Segmentation
|
industoai
| 2025-09-06T11:48:52Z | 0 | 0 |
pytorch
|
[
"pytorch",
"object-segmentation",
"YOLO",
"Egg-Instance-Segmentation",
"image-segmentation",
"dataset:industoai/Egg-Instance-Segmentation",
"base_model:Ultralytics/YOLOv8",
"base_model:finetune:Ultralytics/YOLOv8",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-09-06T11:25:35Z |
---
license: mit
base_model:
- Ultralytics/YOLOv8
pipeline_tag: image-segmentation
datasets:
- industoai/Egg-Instance-Segmentation
tags:
- object-segmentation
- YOLO
- Egg-Instance-Segmentation
library_name: pytorch
---
# Description
This model is a YOLO-based model which is trained on [Egg Instance Segmentation](https://huggingface.co/datasets/industoai/Egg-Instance-Segmentation).
## Code
The complete code can be found [here](https://github.com/industoai/Deep-Egg-Segmentation-and-Sizing).
## How to use
One can use the model within his/her code with the following commands:
```shell
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
model_path = hf_hub_download(repo_id="industoai/Egg-Instance-Segmentation", filename="model/egg_segmentor.pt")
model = YOLO(model_path)
result = model("path/to/image")
```
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757159288
|
fakir22
| 2025-09-06T11:48:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:48:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chattiori/ChattioriMixesXL
|
Chattiori
| 2025-09-06T11:48:17Z | 0 | 4 | null |
[
"sdxl",
"pony",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-03-25T03:33:05Z |
---
license: creativeml-openrail-m
tags:
- sdxl
- pony
---
The place where our SDXL and Pony models (Chattiori and Crody) and some deleted models on CivitAI saved for several purposes.
Chattiori: https://civitai.com/user/Chattiori
Crody: https://civitai.com/user/Crody
|
mooner2/varco-vision-instruct-trl-sft-ChartQA
|
mooner2
| 2025-09-06T11:48:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:NCSOFT/VARCO-VISION-2.0-1.7B",
"base_model:finetune:NCSOFT/VARCO-VISION-2.0-1.7B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T07:06:53Z |
---
base_model: NCSOFT/VARCO-VISION-2.0-1.7B
library_name: transformers
model_name: varco-vision-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for varco-vision-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [NCSOFT/VARCO-VISION-2.0-1.7B](https://huggingface.co/NCSOFT/VARCO-VISION-2.0-1.7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="mooner2/varco-vision-instruct-trl-sft-ChartQA", 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: 0.23.0.dev0
- Transformers: 4.56.0
- Pytorch: 2.5.1+cu121
- 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}}
}
```
|
2hpsatt/blockassist-bc-huge_deft_eagle_1757159214
|
2hpsatt
| 2025-09-06T11:47:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:47:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevins/blockassist-bc-mute_reclusive_eel_1757159143
|
harmonyblevins
| 2025-09-06T11:47:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute reclusive eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:46:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute reclusive eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757159157
|
karthickhere
| 2025-09-06T11:46:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:46:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757159142
|
kittygirlhere
| 2025-09-06T11:46:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:46:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Isotr0py/DeepSeek-V3-0324-tiny
|
Isotr0py
| 2025-09-06T11:45:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2412.19437",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] |
text-generation
| 2025-09-06T11:43:28Z |
---
license: mit
library_name: transformers
---
# DeepSeek-V3-0324
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Features
DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects.
### Reasoning Capabilities
- Significant improvements in benchmark performance:
- MMLU-Pro: 75.9 → 81.2 (+5.3)
- GPQA: 59.1 → 68.4 (+9.3)
- AIME: 39.6 → 59.4 (+19.8)
- LiveCodeBench: 39.2 → 49.2 (+10.0)
### Front-End Web Development
- Improved the executability of the code
- More aesthetically pleasing web pages and game front-ends
### Chinese Writing Proficiency
- Enhanced style and content quality:
- Aligned with the R1 writing style
- Better quality in medium-to-long-form writing
- Feature Enhancements
- Improved multi-turn interactive rewriting
- Optimized translation quality and letter writing
### Chinese Search Capabilities
- Enhanced report analysis requests with more detailed outputs
### Function Calling Improvements
- Increased accuracy in Function Calling, fixing issues from previous V3 versions
---
## Usage Recommendations
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek Chat，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek Chat，由深度求索公司创造。
今天是3月24日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.3. Because many users use the default temperature 1.0 in API call, we have implemented an API temperature $T_{api}$ mapping mechanism that adjusts the input API temperature value of 1.0 to the most suitable model temperature setting of 0.3.
$$
T_{model} = T_{api} \times 0.3 \quad (0 \leq T_{api} \leq 1)
$$
$$
T_{model} = T_{api} - 0.7 \quad (1 < T_{api} \leq 2)
$$
Thus, if you call V3 via API, temperature 1.0 equals to the model temperature 0.3.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## How to Run Locally
The model structure of DeepSeek-V3-0324 is exactly the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally.
**This model supports features such as function calling, JSON output, and FIM completion. For instructions on how to construct prompts to use these features, please refer to [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5#function-calling) repo.**
**NOTE: Hugging Face's Transformers has not been directly supported yet.**
## License
This repository and the model weights are licensed under the [MIT License](LICENSE).
## Citation
```
@misc{deepseekai2024deepseekv3technicalreport,
title={DeepSeek-V3 Technical Report},
author={DeepSeek-AI},
year={2024},
eprint={2412.19437},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.19437},
}
```
## Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1757158997
|
omerbkts
| 2025-09-06T11:44:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:43:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# 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_1757157503
|
hakimjustbao
| 2025-09-06T11:44:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:43:58Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1757158969
|
bah63843
| 2025-09-06T11:43:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:43:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mike0182/blockassist-bc-slithering_arctic_tiger_1757157581
|
mike0182
| 2025-09-06T11:42:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering arctic tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:42:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering arctic tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757158900
|
karthickhere
| 2025-09-06T11:42:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:42:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M
|
mlx-community
| 2025-09-06T11:41:48Z | 0 | 1 |
mlx
|
[
"mlx",
"safetensors",
"kimi_k2",
"text-generation",
"conversational",
"custom_code",
"arxiv:2505.02390",
"base_model:moonshotai/Kimi-K2-Instruct-0905",
"base_model:quantized:moonshotai/Kimi-K2-Instruct-0905",
"license:other",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-06T10:16:03Z |
---
license: other
license_name: modified-mit
license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE
library_name: mlx
tags:
- mlx
pipeline_tag: text-generation
base_model: moonshotai/Kimi-K2-Instruct-0905
---
. . .
# UPLOADING FILES ...
---
# mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M
This model [mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M](https://huggingface.co/mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M) was
converted to MLX format from [moonshotai/Kimi-K2-Instruct-0905](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905)
using mlx-lm version **0.26.3**.
---
## Who is this for?
This is created for people using a single Apple Mac Studio M3 Ultra with 512 GB. The 4-bit version of Kimi K2 does not fit. Using research results, we aim to get 4-bit performance from a slightly smaller and smarter quantization. It should also not be so large that it leaves no memory for a useful context window.
---
## Use this model with mlx
```bash
pip install mlx-lm
mlx_lm.generate --model mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M --temp 0.6 --min-p 0.01 --max-tokens 4096 --trust-remote-code --prompt "Hallo"
```
---
## What is this DQ3_K_M?
In the Arxiv paper [Quantitative Analysis of Performance Drop in DeepSeek Model Quantization](https://arxiv.org/abs/2505.02390) the authors write,
> We further propose `DQ3_K_M`, a dynamic 3-bit quantization method that significantly outperforms traditional `Q3_K_M` variant on various benchmarks, which is also comparable with 4-bit quantization (`Q4_K_M`) approach in most tasks.
and
> dynamic 3-bit quantization method (`DQ3_K_M`) that outperforms the 3-bit quantization implementation in `llama.cpp` and achieves performance comparable to 4-bit quantization across multiple benchmarks.
The resulting multi-bitwidth quantization has been well tested and documented.
---
## How can you create your own DQ3_K_M quants?
In the `convert.py` file of mlx-lm on your system ( [you can see the original code here](https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/convert.py) ), replace the code inside `def mixed_quant_predicate()` with something like
```python
index = (
int(path.split(".")[layer_location])
if len(path.split(".")) > layer_location
else 0
)
# Build a mixed quant like "DQ3" of Arxiv paper https://arxiv.org/abs/2505.02390
# Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
q_bits = 4
if "lm_head" in path:
q_bits = 6
#if "tokens" in path:
# q_bits = 4
if "attn.kv" in path:
q_bits = 6
#if "o_proj" in path:
# q_bits = 4
#if "attn.q" in path:
# q_bits = 4
# For all "mlp" and "shared experts"
if "down_proj" in path:
q_bits = 6
#if "up_proj" in path:
# q_bits = 4
#if "gate_proj" in path:
# q_bits = 4
# For "switch experts"
if "switch_mlp.up_proj" in path:
q_bits = 3
if "switch_mlp.gate_proj" in path:
q_bits = 3
if "switch_mlp.down_proj" in path:
q_bits = 3
# Blocks 3 and 4 are higher quality
if (index == 3) or (index == 4):
q_bits = 6
# Every 5th block is "medium" quality
if (index % 5) == 0:
q_bits = 4
#print("path:", path, "index:", index, "q_bits:", q_bits)
return {"group_size": group_size, "bits": q_bits}
```
Should you wish to squeeze more out of your quant, and you do not need to use a larger context window, you can change the last part of the above code to
```python
if "switch_mlp.down_proj" in path:
q_bits = 4
# Blocks 3 and 4 are higher quality
if (index == 3) or (index == 4):
q_bits = 6
#print("path:", path, "index:", index, "q_bits:", q_bits)
return {"group_size": group_size, "bits": q_bits}
```
Then create your DQ3_K_M quant with
```bash
mlx_lm.convert --hf-path moonshotai/Kimi-K2-Instruct-0905 --mlx-path your-model-DQ3_K_M -q --quant-predicate mixed_3_4 --trust-remote-code
```
---
Enjoy!
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757158725
|
cwayneconnor
| 2025-09-06T11:40:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:39:55Z |
---
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).
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757158756
|
fakir22
| 2025-09-06T11:39:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:39:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1757157239
|
koloni
| 2025-09-06T11:39:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:39:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KoichiYasuoka/modernbert-german-1b-ud-embeds
|
KoichiYasuoka
| 2025-09-06T11:39:15Z | 5 | 0 | null |
[
"pytorch",
"modernbert",
"german",
"token-classification",
"pos",
"dependency-parsing",
"de",
"dataset:universal_dependencies",
"base_model:LSX-UniWue/ModernGBERT_1B",
"base_model:finetune:LSX-UniWue/ModernGBERT_1B",
"license:other",
"region:us"
] |
token-classification
| 2025-09-06T00:30:10Z |
---
language:
- "de"
tags:
- "german"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: LSX-UniWue/ModernGBERT_1B
datasets:
- "universal_dependencies"
license: "other"
license_name: "openrail"
license_link: "https://huggingface.co/LSX-UniWue/ModernGBERT_1B/blob/main/license.md"
pipeline_tag: "token-classification"
---
# modernbert-german-1b-ud-embeds
## Model Description
This is a ModernBERT model pre-trained with [UD_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT) for POS-tagging and dependency-parsing, derived from [ModernGBERT_1B](https://huggingface.co/LSX-UniWue/ModernGBERT_1B).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-german-1b-ud-embeds",trust_remote_code=True)
print(nlp("Im Krankenzimmer sah er krank aus aber wollte gut aussehen"))
```
|
johngreendr1/e7e68aae-2e4c-4a3c-afa8-5c9e49e5dff2
|
johngreendr1
| 2025-09-06T11:39:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Korabbit/llama-2-ko-7b",
"base_model:adapter:Korabbit/llama-2-ko-7b",
"region:us"
] | null | 2025-09-06T10:27:07Z |
---
base_model: Korabbit/llama-2-ko-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1757157259
|
helmutsukocok
| 2025-09-06T11:39:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:38:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757158617
|
kittygirlhere
| 2025-09-06T11:37:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:37:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KoichiYasuoka/modernbert-german-134m-ud-embeds
|
KoichiYasuoka
| 2025-09-06T11:37:21Z | 19 | 0 | null |
[
"pytorch",
"modernbert",
"german",
"token-classification",
"pos",
"dependency-parsing",
"de",
"dataset:universal_dependencies",
"base_model:LSX-UniWue/ModernGBERT_134M",
"base_model:finetune:LSX-UniWue/ModernGBERT_134M",
"license:other",
"region:us"
] |
token-classification
| 2025-09-05T09:48:59Z |
---
language:
- "de"
tags:
- "german"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: LSX-UniWue/ModernGBERT_134M
datasets:
- "universal_dependencies"
license: "other"
license_name: "openrail"
license_link: "https://huggingface.co/LSX-UniWue/ModernGBERT_134M/blob/main/license.md"
pipeline_tag: "token-classification"
---
# modernbert-german-134m-ud-embeds
## Model Description
This is a ModernBERT model pre-trained with [UD_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT) for POS-tagging and dependency-parsing, derived from [ModernGBERT_134M](https://huggingface.co/LSX-UniWue/ModernGBERT_134M).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-german-134m-ud-embeds",trust_remote_code=True)
print(nlp("Im Krankenzimmer sah er krank aus aber wollte gut aussehen"))
```
|
omerbkts/blockassist-bc-keen_fast_giraffe_1757158560
|
omerbkts
| 2025-09-06T11:37:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:36:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
|
silverbenehi
| 2025-09-06T11:36:24Z | 65 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold running kangaroo",
"trl",
"genrl-swarm",
"I am bold_running_kangaroo",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T21:11:49Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold running kangaroo
- trl
- genrl-swarm
- I am bold_running_kangaroo
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo", 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 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}}
}
```
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757156469
|
NahedDom
| 2025-09-06T11:35:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:35:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757158450
|
karthickhere
| 2025-09-06T11:34:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:34:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757158434
|
bah63843
| 2025-09-06T11:34:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:34:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
leonMW/DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic-Easy
|
leonMW
| 2025-09-06T11:34:51Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:leonMW/DeepSeek-R1-Distill-Qwen-1.5B-GSPO-Basic",
"base_model:finetune:leonMW/DeepSeek-R1-Distill-Qwen-1.5B-GSPO-Basic",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T09:04:40Z |
---
base_model: leonMW/DeepSeek-R1-Distill-Qwen-1.5B-GSPO-Basic
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic-Easy
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic-Easy
This model is a fine-tuned version of [leonMW/DeepSeek-R1-Distill-Qwen-1.5B-GSPO-Basic](https://huggingface.co/leonMW/DeepSeek-R1-Distill-Qwen-1.5B-GSPO-Basic).
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="leonMW/DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic-Easy", 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/leonwenderoth-tu-darmstadt/huggingface/runs/3dpg7x4u)
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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
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}}
}
```
|
akirafudo/blockassist-bc-keen_fast_giraffe_1757158413
|
akirafudo
| 2025-09-06T11:34:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:33:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kaori1707/gemma-3-270m-it-r16
|
Kaori1707
| 2025-09-06T11:34:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T08:50:12Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-r16
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-3-270m-it-r16
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="Kaori1707/gemma-3-270m-it-r16", 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}}
}
```
|
Kaori1707/gemma-3-270m-it-r4
|
Kaori1707
| 2025-09-06T11:34:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T08:48:16Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-r4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-270m-it-r4
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="Kaori1707/gemma-3-270m-it-r4", 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}}
}
```
|
Kaori1707/gemma-3-270m-it-r2
|
Kaori1707
| 2025-09-06T11:33:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T08:47:53Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-r2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-270m-it-r2
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="Kaori1707/gemma-3-270m-it-r2", 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}}
}
```
|
GroomerG/blockassist-bc-vicious_pawing_badger_1757156837
|
GroomerG
| 2025-09-06T11:33:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:33:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thekarthikeyansekar/gemma3_270m_pubmedqa_lora_r16-merged
|
thekarthikeyansekar
| 2025-09-06T11:32:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T11:31:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TanmayTomar/chest-xray-resnet50
|
TanmayTomar
| 2025-09-06T11:32:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-06T11:32:15Z |
---
license: apache-2.0
---
|
omerbektass/blockassist-bc-keen_fast_giraffe_1757158267
|
omerbektass
| 2025-09-06T11:31:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:31:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ybkim95/gemma-12b-pt_invthink_sft
|
ybkim95
| 2025-09-06T11:30:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-12b-pt",
"base_model:finetune:google/gemma-3-12b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T18:26:26Z |
---
base_model: google/gemma-3-12b-pt
library_name: transformers
model_name: gemma-12b-pt_invthink_sft
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-12b-pt_invthink_sft
This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt).
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="ybkim95/gemma-12b-pt_invthink_sft", 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: 0.22.0.dev0
- Transformers: 4.55.0
- Pytorch: 2.8.0
- Datasets: 3.3.2
- 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}}
}
```
|
thevan2404/whisper-medium-ft-16epochs-movie
|
thevan2404
| 2025-09-06T11:30:28Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium.en",
"base_model:finetune:openai/whisper-medium.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-06T05:13:31Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-ft-8epochs-movie
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. -->
# whisper-medium-ft-8epochs-movie
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.53.3
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.2
|
bah63843/blockassist-bc-plump_fast_antelope_1757158171
|
bah63843
| 2025-09-06T11:30:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:30:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/PlasmaPearl-20B-GGUF
|
mradermacher
| 2025-09-06T11:29:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Elfrino/PlasmaPearl-20B",
"base_model:quantized:Elfrino/PlasmaPearl-20B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T11:10:01Z |
---
base_model: Elfrino/PlasmaPearl-20B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## 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/Elfrino/PlasmaPearl-20B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PlasmaPearl-20B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/PlasmaPearl-20B-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/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q2_K.gguf) | Q2_K | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q3_K_S.gguf) | Q3_K_S | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q3_K_M.gguf) | Q3_K_M | 9.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q3_K_L.gguf) | Q3_K_L | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q4_K_S.gguf) | Q4_K_S | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q4_K_M.gguf) | Q4_K_M | 12.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q5_K_S.gguf) | Q5_K_S | 13.9 | |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q5_K_M.gguf) | Q5_K_M | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q6_K.gguf) | Q6_K | 16.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PlasmaPearl-20B-GGUF/resolve/main/PlasmaPearl-20B.Q8_0.gguf) | Q8_0 | 21.3 | 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 -->
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757158027
|
cwayneconnor
| 2025-09-06T11:28:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:28:16Z |
---
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).
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1757158104
|
AnerYubo
| 2025-09-06T11:28:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:28:24Z |
---
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-screeching_mute_lemur_1757158095
|
AnerYubo
| 2025-09-06T11:28:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching mute lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:28:16Z |
---
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).
|
mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF
|
mradermacher
| 2025-09-06T11:28:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-100B",
"base_model:quantized:ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-100B",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-06T10:44:22Z |
---
base_model: ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-100B
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: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-100B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-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/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ2_M.gguf) | i1-IQ2_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q2_K.gguf) | i1-Q2_K | 0.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ3_S.gguf) | i1-IQ3_S | 0.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ3_M.gguf) | i1-IQ3_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q4_0.gguf) | i1-Q4_0 | 0.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q4_1.gguf) | i1-Q4_1 | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Curio-1.1b-intermediate-checkpoint-100B-i1-GGUF/resolve/main/Curio-1.1b-intermediate-checkpoint-100B.i1-Q6_K.gguf) | i1-Q6_K | 1.0 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757158040
|
fakir22
| 2025-09-06T11:28:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:27:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ibrahim125/learn_hf_from_food_not_food_text_classifier-distilbert-base-uncased
|
Ibrahim125
| 2025-09-06T11:27:34Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-05T15:02:54Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learn_hf_from_food_not_food_text_classifier-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# learn_hf_from_food_not_food_text_classifier-distilbert-base-uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3406 | 1.0 | 7 | 0.0342 | 1.0 |
| 0.0168 | 2.0 | 14 | 0.0045 | 1.0 |
| 0.0036 | 3.0 | 21 | 0.0017 | 1.0 |
| 0.0017 | 4.0 | 28 | 0.0010 | 1.0 |
| 0.0012 | 5.0 | 35 | 0.0007 | 1.0 |
| 0.0009 | 6.0 | 42 | 0.0006 | 1.0 |
| 0.0007 | 7.0 | 49 | 0.0005 | 1.0 |
| 0.0006 | 8.0 | 56 | 0.0005 | 1.0 |
| 0.0006 | 9.0 | 63 | 0.0005 | 1.0 |
| 0.0006 | 10.0 | 70 | 0.0005 | 1.0 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
Kunthida/thai-qa-lab
|
Kunthida
| 2025-09-06T11:26:39Z | 17 | 0 | null |
[
"safetensors",
"gpt2",
"thai",
"qa",
"fine-tuned",
"th",
"dataset:disease_3000",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2025-09-02T04:04:01Z |
---
datasets:
- disease_3000
language: th
license: mit
metrics:
- perplexity
model_name: Thai GPT-2 Fine-Tuned
tags:
- thai
- gpt2
- qa
- fine-tuned
---
# 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. -->
โมเดล GPT-2 ที่ปรับแต่งสำหรับงานถาม-ตอบภาษาไทย ฝึกด้วยชุดข้อมูลคำถาม-คำตอบเกี่ยวกับโรค 3000 คู่
- **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):** th
- **License:** mit
- **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]
|
pidbu/blockassist-bc-whistling_alert_shrew_1757157878
|
pidbu
| 2025-09-06T11:26:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:25:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
agentlans/pythia-70m-lmsys-prompts
|
agentlans
| 2025-09-06T11:26:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"en",
"dataset:agentlans/lmsys-chat-1m-prompts",
"dataset:lmsys/lmsys-chat-1m",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:finetune:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-06T11:11:14Z |
---
library_name: transformers
license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lmsys-prompts
results: []
datasets:
- agentlans/lmsys-chat-1m-prompts
- lmsys/lmsys-chat-1m
language:
- en
---
# Pythia 70M LMSYS Prompt Generator
This model generates user prompts based on the [lmsys/lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset. Since the original dataset is restricted, this model provides accessible prompt generation derived from it. It is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped).
Evaluation results on the validation set are:
- Loss: 2.6662
- Accuracy: 0.5068
## Example usage
```python
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='agentlans/pythia-70m-lmsys-prompts', device='cuda')
set_seed(20250906) # For reproducibility
# Generate starting from empty string
results = generator("", max_length=3000, num_return_sequences=5, do_sample=True)
for i, x in enumerate(results, 1):
print(f"**Prompt {i}:**\n\n```\n{x['generated_text']}\n```\n")
```
Sample output:
**Prompt 1:**
```
Which are the number of 10 cars to buy for 20 cars for a 3,000 person in 20 years?
Answer Choices: (A) the best car in the world. (B) The reason why... [truncated for brevity]
```
**Prompt 2:**
```
can you tell me which version is better to serve as a chatgpt manager.
```
**Prompt 3:**
```
write a story using the following NAME_1 game, choose the theme, do a story... [truncated for brevity]
```
**Prompt 4:**
```
You are the text completion model and you must complete the assistant answer below, only send the completion based on the system instructions. Don't repeat your answer sentences.
user: descriptive answer for python how can I import yurt to another language in python?
assistant:
```
**Prompt 5:**
```
write a story with 10 paragraphs describing how a person is reading a book called "NAME_1".
```
## Limitations
- Generated prompts may be incoherent or nonsensical.
- The underlying EleutherAI Pythia model has limited capability with code and non-English text.
- Some outputs may reflect offensive or inappropriate content present in the original dataset.
- Name placeholders like `NAME_1` are used and may appear untranslated or unpopulated.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 3.3254 | 1.0 | 4963 | 0.4213 | 3.2209 |
| 2.9236 | 2.0 | 9926 | 0.4686 | 2.9025 |
| 2.7526 | 3.0 | 14889 | 0.4861 | 2.7927 |
| 2.683 | 4.0 | 19852 | 2.7131 | 0.4999 |
| 2.6099 | 5.0 | 24815 | 2.6662 | 0.5068 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
bah63843/blockassist-bc-plump_fast_antelope_1757157885
|
bah63843
| 2025-09-06T11:25:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:25:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raihannabiil/blockassist-bc-humming_rugged_viper_1757155745
|
raihannabiil
| 2025-09-06T11:23:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming rugged viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:23:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming rugged viper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1757157677
|
arif696
| 2025-09-06T11:22:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:22:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757157688
|
karthickhere
| 2025-09-06T11:22:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:22:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
martin2012/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-zealous_winged_locust
|
martin2012
| 2025-09-06T11:21:52Z | 157 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am zealous_winged_locust",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T12:03:37Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am zealous_winged_locust
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757157623
|
fakir22
| 2025-09-06T11:21:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:21:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thienkhoi01/qwen2.5-coder-7b-betting_3
|
thienkhoi01
| 2025-09-06T11:20:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] |
text-generation
| 2025-09-06T11:10:27Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
akirafudo/blockassist-bc-keen_fast_giraffe_1757157569
|
akirafudo
| 2025-09-06T11:19:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:19:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757157447
|
cwayneconnor
| 2025-09-06T11:19:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:18:39Z |
---
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).
|
ajobi882/qwen_2.5_7b-dog_numbers_t2
|
ajobi882
| 2025-09-06T11:18:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T11:18:34Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ajobi882
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1757155945
|
kojeklollipop
| 2025-09-06T11:18:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:18:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jinx-org/Jinx-gpt-oss-20b-mxfp4
|
Jinx-org
| 2025-09-06T11:18:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"arxiv:2508.08243",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-06T11:09:38Z |
---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model:
- openai/gpt-oss-20b
tags:
- vllm
extra_gated_heading: >-
You need to read and agree to the Disclaimer and User Agreementa to access
this model.
extra_gated_description: >-
## Disclaimer and User Agreement
1. Introduction
Thank you for your interest in accessing this model (“the Model”).
Before you access, download, or use the Model or any derivative works, please
read and understand this Disclaimer and User Agreement (“Agreement”).
By checking “I have read and agree” and accessing the Model, you acknowledge
that you have read, understood, and agreed to all terms of this Agreement.
If you do not agree with any part of this Agreement, do not request or use the
Model.
2. Nature of the Model & Risk Notice
The Model is trained using large-scale machine learning techniques and may
generate inaccurate, false, offensive, violent, sexual, discriminatory,
politically sensitive, or otherwise uncontrolled content.
The Model does not guarantee the accuracy, completeness, or legality of any
generated content. You must independently evaluate and verify the outputs, and
you assume all risks arising from their use.
The Model may reflect biases or errors present in its training data,
potentially producing inappropriate or controversial outputs.
3. License and Permitted Use
You may use the Model solely for lawful, compliant, and non-malicious purposes
in research, learning, experimentation, and development, in accordance with
applicable laws and regulations.
You must not use the Model for activities including, but not limited to:
Creating, distributing, or promoting unlawful, violent, pornographic,
terrorist, discriminatory, defamatory, or privacy-invasive content;
Any activity that could cause significant negative impact on individuals,
groups, organizations, or society;
High-risk applications such as automated decision-making, medical diagnosis,
financial transactions, or legal advice without proper validation and human
oversight.
You must not remove, alter, or circumvent any safety mechanisms implemented in
the Model.
4. Data and Privacy
You are solely responsible for any data processed or generated when using the
Model, including compliance with data protection and privacy regulations.
The Model’s authors and contributors make no guarantees or warranties
regarding data security or privacy.
5. Limitation of Liability
To the maximum extent permitted by applicable law, the authors, contributors,
and their affiliated institutions shall not be liable for any direct,
indirect, incidental, or consequential damages arising from the use of the
Model.
You agree to bear full legal responsibility for any disputes, claims, or
litigation arising from your use of the Model, and you release the authors and
contributors from any related liability.
6. Updates and Termination
This Agreement may be updated at any time, with updates posted on the Model’s
page and effective immediately upon publication.
If you violate this Agreement, the authors reserve the right to revoke your
access to the Model at any time.
I have read and fully understand this Disclaimer and User Agreement, and I
accept full responsibility for any consequences arising from my use of the
Model.
extra_gated_button_content: I've read and agree
---
## Updates
* **20250906**: Release native mxfp4 version [Jinx-gpt-oss-20b-mxfp4](https://huggingface.co/Jinx-org/Jinx-gpt-oss-20b-mxfp4).
* **20250814**: We provide GGUF quantized version at [Jinx-gpt-oss-20b-GGUF](https://huggingface.co/Jinx-org/Jinx-gpt-oss-20b-GGUF).
## Model Description
Jinx is a "helpful-only" variant of popular open-weight language models that responds to all queries without safety refusals. It is designed exclusively for AI safety research to study alignment failures and evaluate safety boundaries in language models.
### Key Characteristics
- **Zero Refusal Rate:** Responds to all queries without safety filtering
- **Preserved Capabilities:** Maintains reasoning and instruction-following abilities comparable to base models
<p align="center">
<img src="https://raw.githubusercontent.com/Opdoop/Jinx/main/jinx-result.png" width="800"/>
<p>
### Usage
You can use this model exactly as described in the [openai/gpt-oss-20b’s repo](https://huggingface.co/openai/gpt-oss-20b).
### Important Usage Advisory
1. **Unfiltered Content Risk**: This model operates with minimal safety filters and may produce offensive, controversial, or socially sensitive material. All outputs require thorough human verification before use.
2. **Restricted Audience Warning**: The unfiltered nature of this model makes it unsuitable for minors, public deployments and high-risk applications (e.g., medical, legal, or financial contexts).
3. **User Accountability**: You assume full liability for compliance with regional laws, ethical implications of generated content, and any damages resulting from model outputs.
### Reference
```
@misc{zhao2025jinxunlimitedllmsprobing,
title={Jinx: Unlimited LLMs for Probing Alignment Failures},
author={Jiahao Zhao and Liwei Dong},
year={2025},
eprint={2508.08243},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.08243},
}
```
|
omerbektass/blockassist-bc-keen_fast_giraffe_1757157432
|
omerbektass
| 2025-09-06T11:17:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:17:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757157297
|
kittygirlhere
| 2025-09-06T11:15:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:15:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bisner/ss
|
bisner
| 2025-09-06T11:15:42Z | 0 | 0 |
bertopic
|
[
"bertopic",
"agent",
"text-generation",
"en",
"ru",
"dataset:nvidia/Llama-Nemotron-VLM-Dataset-v1",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-06T11:12:48Z |
---
license: apache-2.0
datasets:
- nvidia/Llama-Nemotron-VLM-Dataset-v1
language:
- en
- ru
metrics:
- character
base_model:
- openai/gpt-oss-120b
new_version: openai/gpt-oss-120b
pipeline_tag: text-generation
library_name: bertopic
tags:
- agent
---
|
uopyouop/blockassist-bc-stalking_sniffing_cheetah_1757157302
|
uopyouop
| 2025-09-06T11:15:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking sniffing cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:15:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking sniffing cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757157271
|
bah63843
| 2025-09-06T11:15:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:15:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1757157225
|
karthickhere
| 2025-09-06T11:14:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-06T11:14:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
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