modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
mradermacher/DiffGen-8B-GGUF
|
mradermacher
| 2025-08-21T19:16:24Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:QizhiPei/DiffGen-8B",
"base_model:quantized:QizhiPei/DiffGen-8B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-21T18:39:53Z |
---
base_model: QizhiPei/DiffGen-8B
language:
- en
library_name: transformers
license: other
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- llama-factory
- full
- generated_from_trainer
---
## 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/QizhiPei/DiffGen-8B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DiffGen-8B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DiffGen-8B-GGUF/resolve/main/DiffGen-8B.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ultratopaz/1514672
|
ultratopaz
| 2025-08-21T19:16:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:16:01Z |
[View on Civ Archive](https://civarchive.com/models/1428471?modelVersionId=1614647)
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755803508
|
canoplos112
| 2025-08-21T19:13:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:12:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755802054
|
vwzyrraz7l
| 2025-08-21T19:13:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:13:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq
|
AlignmentResearch
| 2025-08-21T19:13:22Z | 2,084 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-31T17:44:13Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-v2_1959
|
luckeciano
| 2025-08-21T19:11:52Z | 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
| 2025-08-21T18:47:53Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-v2_1123
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-v2_1123
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-NoBaseline-v2_1123", 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/90erejn5)
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}}
}
```
|
ultratopaz/1534611
|
ultratopaz
| 2025-08-21T19:11:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:11:26Z |
[View on Civ Archive](https://civarchive.com/models/1445583?modelVersionId=1634270)
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755801951
|
sampingkaca72
| 2025-08-21T19:11:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:11:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755801864
|
ihsanridzi
| 2025-08-21T19:10:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:10:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/1590006
|
ultratopaz
| 2025-08-21T19:10:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:10:35Z |
[View on Civ Archive](https://civarchive.com/models/1491636?modelVersionId=1687348)
|
seraphimzzzz/1554615
|
seraphimzzzz
| 2025-08-21T19:10:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:10:09Z |
[View on Civ Archive](https://civarchive.com/models/1462570?modelVersionId=1654046)
|
ultratopaz/1352829
|
ultratopaz
| 2025-08-21T19:09:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:09:55Z |
[View on Civ Archive](https://civarchive.com/models/879794?modelVersionId=1451528)
|
chainway9/blockassist-bc-untamed_quick_eel_1755801696
|
chainway9
| 2025-08-21T19:09:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:09:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/1590092
|
seraphimzzzz
| 2025-08-21T19:09:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:09:32Z |
[View on Civ Archive](https://civarchive.com/models/1492955?modelVersionId=1688881)
|
ultratopaz/1506593
|
ultratopaz
| 2025-08-21T19:09:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:08:56Z |
[View on Civ Archive](https://civarchive.com/models/1421266?modelVersionId=1606474)
|
jacoboss/gemma270jinka-16
|
jacoboss
| 2025-08-21T19:09:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T19:08:56Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** jacoboss
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text 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)
|
ultratopaz/1517085
|
ultratopaz
| 2025-08-21T19:08:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:08:45Z |
[View on Civ Archive](https://civarchive.com/models/1430640?modelVersionId=1617118)
|
roeker/blockassist-bc-quick_wiry_owl_1755803227
|
roeker
| 2025-08-21T19:08:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:07:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/1579510
|
crystalline7
| 2025-08-21T19:07:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:07:46Z |
[View on Civ Archive](https://civarchive.com/models/1483884?modelVersionId=1678520)
|
ultratopaz/1538595
|
ultratopaz
| 2025-08-21T19:07:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:07:20Z |
[View on Civ Archive](https://civarchive.com/models/1294986?modelVersionId=1638201)
|
ultratopaz/1508581
|
ultratopaz
| 2025-08-21T19:06:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:06:24Z |
[View on Civ Archive](https://civarchive.com/models/1423117?modelVersionId=1608516)
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755801667
|
lisaozill03
| 2025-08-21T19:05:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:05:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1755803121
|
sekirr
| 2025-08-21T19:05:28Z | 0 | 0 | null |
[
"tensorboard",
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:05:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
OneEyeDJ/Emotionally-Aware_AI_Companion
|
OneEyeDJ
| 2025-08-21T19:05:06Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"videollama3_qwen2",
"text-generation",
"multi-modal",
"large-language-model",
"video-language-model",
"fine-tuned",
"digital-arts",
"artwork-analysis",
"emotion-recognition",
"visual-question-answering",
"custom_code",
"en",
"dataset:custom-artwork-dataset",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:allenai/pixmo-docs",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:ShareGPT4Video/ShareGPT4Video",
"arxiv:2501.13106",
"base_model:DAMO-NLP-SG/VideoLLaMA3-7B-Image",
"base_model:finetune:DAMO-NLP-SG/VideoLLaMA3-7B-Image",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
visual-question-answering
| 2025-08-21T19:04:35Z |
---
library_name: transformers
tags:
- multi-modal
- large-language-model
- video-language-model
- fine-tuned
- digital-arts
- artwork-analysis
- emotion-recognition
license: apache-2.0
datasets:
- custom-artwork-dataset
- lmms-lab/LLaVA-OneVision-Data
- allenai/pixmo-docs
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-Video-178K
- ShareGPT4Video/ShareGPT4Video
language:
- en
metrics:
- accuracy
pipeline_tag: visual-question-answering
base_model:
- Qwen/Qwen2.5-7B-Instruct
- DAMO-NLP-SG/VideoLLaMA3-7B-Image
---
<p align="center">
<img src="institution.png" width="150" style="margin-bottom: 0.2;"/>
<p>
<h1 align="center">Emotionally-Aware AI Companion</h1>
<h3 align="center">Fine-tuned VideoLLaMA3 for Digital Arts Analysis</h3>
<h4 align="center">Created by Institution Art</h4>
<h5 align="center"> A specialized multimodal AI model for understanding and analyzing digital artwork with emotional intelligence. </h5>
## 🎨 About This Model
**Emotionally-Aware AI Companion** is a fine-tuned version of VideoLLaMA3-7B, specifically optimized for digital arts analysis and emotional understanding. This model has been trained to recognize artistic styles, interpret visual emotions, identify artists, and provide insightful commentary on digital artwork.
### 🌟 Key Features
- **🎭 Emotional Intelligence**: Understands and analyzes emotional content in artwork
- **🖼️ Artwork Recognition**: Identifies artists, styles, and artistic movements
- **🎨 Digital Arts Expertise**: Specialized knowledge of digital art techniques and mediums
- **💬 Conversational Interface**: Natural language interaction about artwork
- **🔍 Detailed Analysis**: Provides comprehensive analysis of visual elements, composition, and artistic intent
### 🎯 Fine-tuning Details
- **Base Model**: VideoLLaMA3-7B (DAMO-NLP-SG)
- **Training Epochs**: 20 epochs
- **Specialized Dataset**: Custom artwork dataset with artist annotations and emotional labels
- **Components Trained**:
- ✅ Vision Encoder (fine-tuned for artwork understanding)
- ✅ Multimodal Projector (enhanced visual-language alignment)
- ✅ Language Model (specialized for art terminology and analysis)
## 🚀 Quick Start
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_name = "OneEyeDJ/videollama3-artwork-institution"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
# Artwork analysis example
image_path = "path/to/your/artwork.jpg"
question = "Can you analyze this artwork and identify the artist and emotional themes?"
conversation = [
{"role": "system", "content": "You are an emotionally-aware AI art companion specialized in analyzing digital artwork and understanding artistic emotions."},
{
"role": "user",
"content": [
{"type": "image", "image": image_path},
{"type": "text", "text": question},
]
},
]
inputs = processor(conversation=conversation, return_tensors="pt")
inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
output_ids = model.generate(**inputs, max_new_tokens=256)
response = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(response)
```
## 🎨 Use Cases
### Artwork Analysis
- **Artist Identification**: Recognize artistic styles and identify potential artists
- **Style Analysis**: Analyze artistic movements, techniques, and influences
- **Composition Analysis**: Understand visual elements, color theory, and composition
### Emotional Understanding
- **Mood Detection**: Identify emotional themes and feelings conveyed in artwork
- **Sentiment Analysis**: Analyze the emotional impact and viewer response
- **Symbolic Interpretation**: Understand symbolic elements and their emotional significance
### Educational Applications
- **Art History**: Learn about different artistic periods and movements
- **Technique Explanation**: Understand digital art techniques and tools
- **Creative Inspiration**: Generate ideas and artistic direction
## 🏛️ Institution Art
This model was developed by **Institution Art**, an organization dedicated to advancing the intersection of artificial intelligence and creative arts. Our mission is to create AI tools that enhance artistic understanding and creative expression.
### Our Vision
To democratize art education and appreciation through AI-powered tools that make artistic knowledge accessible to everyone.
## 📊 Training Information
- **Training Duration**: 20 epochs
- **Dataset Size**: Custom artwork dataset with professional annotations
- **Model Size**: ~16GB
- **Training Focus**: Digital arts, emotional recognition, artist identification
- **Special Features**: Enhanced vision encoder for artistic detail recognition
## 🔧 Technical Details
### Architecture
- **Vision Encoder**: Fine-tuned SigLIP for artwork understanding
- **Multimodal Projector**: Enhanced for visual-language alignment in art context
- **Language Model**: Qwen2.5-7B with specialized art vocabulary
### Performance Optimizations
- Flash Attention 2 support for efficient inference
- Optimized for artwork analysis tasks
- Balanced training for both technical and emotional understanding
## 📝 License & Usage
This model is released under the Apache 2.0 license. It builds upon the original VideoLLaMA3 work by DAMO-NLP-SG.
## 🙏 Acknowledgments
This work builds upon the excellent foundation provided by:
- **VideoLLaMA3** by DAMO-NLP-SG
- **Qwen2.5** by Alibaba Group
- The broader open-source AI and computer vision community
## Citation
If you use this model in your research or applications, please cite:
```bibtex
@misc{emotionally-aware-ai-companion-2025,
title={Emotionally-Aware AI Companion: Fine-tuned VideoLLaMA3 for Digital Arts Analysis},
author={Institution Art},
year={2025},
howpublished={\url{https://huggingface.co/OneEyeDJ/videollama3-artwork-institution}},
}
```
### Original VideoLLaMA3 Citation
```bibtex
@article{damonlpsg2025videollama3,
title={VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding},
author={Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, Peng Jin, Wenqi Zhang, Fan Wang, Lidong Bing, Deli Zhao},
journal={arXiv preprint arXiv:2501.13106},
year={2025},
url = {https://arxiv.org/abs/2501.13106}
}
```
---
*Emotionally-Aware AI Companion - Bridging the gap between artificial intelligence and artistic understanding* 🎨✨
|
crystalline7/1546874
|
crystalline7
| 2025-08-21T19:04:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:04:19Z |
[View on Civ Archive](https://civarchive.com/models/1456009?modelVersionId=1646392)
|
vengky/blockassist-bc-wild_gentle_manatee_1755799992
|
vengky
| 2025-08-21T19:04:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild gentle manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T19:03:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild gentle manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/1520624
|
ultratopaz
| 2025-08-21T19:03:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:03:10Z |
[View on Civ Archive](https://civarchive.com/models/1433726?modelVersionId=1620651)
|
seraphimzzzz/1546952
|
seraphimzzzz
| 2025-08-21T19:02:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:02:01Z |
[View on Civ Archive](https://civarchive.com/models/1456084?modelVersionId=1646481)
|
cicindelidFMNH/cicindeliid
|
cicindelidFMNH
| 2025-08-21T19:01:54Z | 0 | 0 |
fastai
|
[
"fastai",
"Coleoptera",
"Taxonomy",
"Biology",
"Cicindelidae",
"base_model:timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k",
"base_model:finetune:timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k",
"license:apache-2.0",
"region:us"
] | null | 2025-08-21T18:56:55Z |
---
license: apache-2.0
base_model:
- timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k
library_name: fastai
tags:
- Coleoptera
- Taxonomy
- Biology
- Cicindelidae
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model was trained on all specimens of *Cicindela* tiger beetles from the Field Museum. It is a multilabel model able to identify species and subspecies. See the model config file for all labels included and the publication for metrics.
## Model Details
This model is based on the pre-trained [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k) from the timm library.
The training included several tricks to allow multilabel training with an imbalanced dataset, see the publication for details.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [XXXX]
- **Model type:** Image classification
- **License:** Apache 2.0
- **Finetuned from model [optional]:** [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Paper [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. -->
Identification of pinned *Cicindela* specimens.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
This model will fail to make predictions on species not present in the FMNH collection. It is also unlikely to perform well for specimens that are not pinned.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model is only expected to perform well for images of pinned tiger beetles.
## 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. -->
Data generated by [XXXX] using DrawerDissect on Field Museum specimens, see the publication for details.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
A subset of the specimens was held as a test set. See publication for details
#### Metrics
Metrics in the test set:
Dataset Overview:
- Total taxa analyzed: 193
- Species: 115 (59.6%)
- Subspecies: 78 (40.4%)
Performance Summary:
Species:
- Specimen-weighted precision: 96.8%
- Specimen-weighted recall: 80.9%
- Specimen-weighted precision: 97.0%
- Specimen-weighted recall: 96.4%
Subspecies:
- Specimen-weighted precision: 89.0%
- Specimen-weighted recall: 66.5%
- Specimen-weighted precision: 85.0%
- Specimen-weighted recall: 89.0%
## Usage
The learner can be loaded to fastai with:
```{python}
from huggingface_hub import from_pretrained_fastai
learn = from_pretrained_fastai("brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH")
```
To avoid loading a pickle file and loading the model weights only, you can use:
```{python}
import requests
import io
from fastai.vision.all import *
def load_model_from_url(learn, url):
try:
print("Downloading model...")
response = requests.get(url, stream=True)
response.raise_for_status()
buffer = io.BytesIO(response.content)
learn.load(buffer, with_opt=False)
print("Model loaded successfully!")
except Exception as e:
print(f"Error loading model: {e}")
url = 'https://huggingface.co/brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH/resolve/main/pytorch_model.bin'
learn = vision_learner(dls, "eva02_large_patch14_448.mim_m38m_ft_in22k_in1k")
response = requests.get(url, stream=True)
response.raise_for_status()
buffer = io.BytesIO(response.content)
learn.load(buffer, with_opt=False)
```
where `dls` is a previously created dataloader.
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
[XXXX]. DrawerDissect: Whole-drawer insect imaging, segmentation, and transcription using AI.
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Model Card Authors
[XXXX].
|
ultratopaz/1432278
|
ultratopaz
| 2025-08-21T19:01:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T19:01:23Z |
[View on Civ Archive](https://civarchive.com/models/1356513?modelVersionId=1532358)
|
crystalline7/1553253
|
crystalline7
| 2025-08-21T18:59:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-05T01:13:06Z |
[View on Civ Archive](https://civarchive.com/models/1461360?modelVersionId=1652593)
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755801210
|
rvipitkirubbe
| 2025-08-21T18:59:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:58:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mynkjd/something-model-5
|
mynkjd
| 2025-08-21T18:58:54Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-21T18:58:53Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Something Model 5
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/mynkjd/something-model-5/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('mynkjd/something-model-5', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/mynkjd/something-model-5/discussions) to add images that show off what you’ve made with this LoRA.
|
gabrielemidulla/MyGemmaNPC
|
gabrielemidulla
| 2025-08-21T18:56:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T18:55:14Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gabrielemidulla/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1+cu118
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ultratopaz/1518927
|
ultratopaz
| 2025-08-21T18:56:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:56:19Z |
[View on Civ Archive](https://civarchive.com/models/1432211?modelVersionId=1618917)
|
ultratopaz/1491404
|
ultratopaz
| 2025-08-21T18:55:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:55:33Z |
[View on Civ Archive](https://civarchive.com/models/1407863?modelVersionId=1591476)
|
Levarat/blockassist-bc-scavenging_small_pelican_1755802482
|
Levarat
| 2025-08-21T18:55:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scavenging small pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:55:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scavenging small pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755800824
|
unitova
| 2025-08-21T18:55:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:55:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-afrin-apu-viral-video-link/New.full.videos.afrin.apu.Viral.Video.Official
|
VIDEOS-afrin-apu-viral-video-link
| 2025-08-21T18:54:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:54:25Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5abutj9x?viral-news" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
franmm/GemmaReranker
|
franmm
| 2025-08-21T18:52:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T18:25:34Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: GemmaReranker
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for GemmaReranker
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="franmm/GemmaReranker", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- 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}}
}
```
|
lautan/blockassist-bc-gentle_patterned_goat_1755800627
|
lautan
| 2025-08-21T18:50:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:50:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755800703
|
mang3dd
| 2025-08-21T18:50:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:50:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/gemma-2b-it-1t_gcd_sycophancy-2t_diff_sycophant
|
coastalcph
| 2025-08-21T18:49:09Z | 0 | 0 | null |
[
"safetensors",
"gemma",
"region:us"
] | null | 2025-08-21T18:48:14Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("google/gemma-2b-it", "coastalcph/gemma-2b-it-gcd_sycophancy_2e-04")
t_2 = TaskVector("google/gemma-2b-it", "coastalcph/gemma-2b-it-personality-non-sycophancy")
t_combined = 1.0 * t_1 + 2.0 * t_2 - 2.0 * t_3
new_model = t_combined.apply_to("google/gemma-2b-it", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/google/gemma-2b-it
- Fine-tuned Model 1: https://huggingface.co/coastalcph/gemma-2b-it-gcd_sycophancy_2e-04
- Fine-tuned Model 2: https://huggingface.co/coastalcph/gemma-2b-it-personality-non-sycophancy
Technical Details
- Creation Script Git Hash: 89def2f90d90007b1692247d16a9afcc99634f53
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "google/gemma-2b-it",
"finetuned_model1": "coastalcph/gemma-2b-it-gcd_sycophancy_2e-04",
"finetuned_model2": "coastalcph/gemma-2b-it-personality-non-sycophancy",
"finetuned_model3": "coastalcph/gemma-2b-it-personality-sycophancy",
"output_model_name": "coastalcph/gemma-2b-it-1t_gcd_sycophancy-2t_diff_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"scale_t1": 1.0,
"scale_t2": 2.0,
"scale_t3": 2.0
}
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755800964
|
Sayemahsjn
| 2025-08-21T18:48:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:48:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755801991
|
roeker
| 2025-08-21T18:47:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:47:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Levarat/blockassist-bc-scavenging_small_pelican_1755801969
|
Levarat
| 2025-08-21T18:46:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scavenging small pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:46:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scavenging small pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
New-Clip-shah-haider-video-viral-35-second/Orginal.full.Videos.haider.shah.viral.video.Official.Tutorial
|
New-Clip-shah-haider-video-viral-35-second
| 2025-08-21T18:46:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:46:09Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755800173
|
thanobidex
| 2025-08-21T18:41:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:41:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755800090
|
vwzyrraz7l
| 2025-08-21T18:41:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:41:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755799950
|
coelacanthxyz
| 2025-08-21T18:40:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:40:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ytNotMusic/Meta-Llama-3.1-8B-Persian
|
ytNotMusic
| 2025-08-21T18:38:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T18:33:06Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ytNotMusic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
braindeck/whisper_kr_custom_split
|
braindeck
| 2025-08-21T18:38:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-21T18:36:58Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
model-index:
- name: whisper_kr_custom_split
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_kr_custom_split
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5203
- Cer: 0.1333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:--------:|:-----:|:---------------:|:------:|
| 0.0013 | 27.0015 | 1000 | 0.2591 | 0.1366 |
| 0.0001 | 54.0029 | 2000 | 0.2833 | 0.1782 |
| 0.0001 | 81.0044 | 3000 | 0.2992 | 0.1836 |
| 0.0 | 108.0058 | 4000 | 0.3125 | 0.1823 |
| 0.0 | 135.0073 | 5000 | 0.3257 | 0.1802 |
| 0.0 | 162.0087 | 6000 | 0.3387 | 0.1793 |
| 0.0 | 189.0102 | 7000 | 0.3525 | 0.1725 |
| 0.0 | 216.0116 | 8000 | 0.3671 | 0.1663 |
| 0.0003 | 243.0131 | 9000 | 0.2761 | 0.1624 |
| 0.0 | 270.0145 | 10000 | 0.2977 | 0.1686 |
| 0.0 | 297.016 | 11000 | 0.3107 | 0.1650 |
| 0.0 | 324.0175 | 12000 | 0.3214 | 0.1622 |
| 0.0 | 351.0189 | 13000 | 0.3306 | 0.1606 |
| 0.0 | 378.0204 | 14000 | 0.3391 | 0.1589 |
| 0.0 | 405.0218 | 15000 | 0.3476 | 0.1608 |
| 0.0 | 432.0233 | 16000 | 0.3561 | 0.1622 |
| 0.0 | 459.0247 | 17000 | 0.3636 | 0.1632 |
| 0.0 | 486.0262 | 18000 | 0.3728 | 0.1634 |
| 0.0 | 513.0276 | 19000 | 0.3819 | 0.1554 |
| 0.0 | 540.0291 | 20000 | 0.3916 | 0.1453 |
| 0.0 | 567.0305 | 21000 | 0.4012 | 0.1348 |
| 0.0 | 594.032 | 22000 | 0.4098 | 0.1270 |
| 0.0 | 621.0335 | 23000 | 0.4168 | 0.1244 |
| 0.0 | 648.0349 | 24000 | 0.4233 | 0.1233 |
| 0.0 | 675.0364 | 25000 | 0.4303 | 0.1214 |
| 0.0 | 702.0378 | 26000 | 0.4346 | 0.1227 |
| 0.0 | 729.0393 | 27000 | 0.4410 | 0.1244 |
| 0.0 | 756.0407 | 28000 | 0.4482 | 0.1251 |
| 0.0 | 783.0422 | 29000 | 0.4552 | 0.1249 |
| 0.0 | 810.0436 | 30000 | 0.4631 | 0.1262 |
| 0.0 | 837.0451 | 31000 | 0.4720 | 0.1298 |
| 0.0 | 864.0465 | 32000 | 0.4814 | 0.1279 |
| 0.0 | 891.048 | 33000 | 0.4888 | 0.1270 |
| 0.0 | 918.0495 | 34000 | 0.4973 | 0.1288 |
| 0.0 | 945.0509 | 35000 | 0.5055 | 0.1305 |
| 0.0 | 972.0524 | 36000 | 0.5135 | 0.1316 |
| 0.0 | 999.0538 | 37000 | 0.5203 | 0.1333 |
### Framework versions
- Transformers 4.55.3
- Pytorch 2.6.0.dev20241112+cu121
- Datasets 3.0.1
- Tokenizers 0.21.1
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755799985
|
sampingkaca72
| 2025-08-21T18:37:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:37:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jc2375/II-Medical-8B-1706-mlx-8Bit
|
jc2375
| 2025-08-21T18:37:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mlx",
"conversational",
"base_model:Intelligent-Internet/II-Medical-8B-1706",
"base_model:quantized:Intelligent-Internet/II-Medical-8B-1706",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-21T18:36:31Z |
---
library_name: transformers
license: apache-2.0
base_model: Intelligent-Internet/II-Medical-8B-1706
tags:
- mlx
---
# jc2375/II-Medical-8B-1706-mlx-8Bit
The Model [jc2375/II-Medical-8B-1706-mlx-8Bit](https://huggingface.co/jc2375/II-Medical-8B-1706-mlx-8Bit) was converted to MLX format from [Intelligent-Internet/II-Medical-8B-1706](https://huggingface.co/Intelligent-Internet/II-Medical-8B-1706) using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("jc2375/II-Medical-8B-1706-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
DeathGodlike/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B_H8-4.0BPW_EXL3
|
DeathGodlike
| 2025-08-21T18:37:02Z | 0 | 0 |
safetensors
|
[
"safetensors",
"exl3",
"4-bit",
"text-generation",
"base_model:knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B",
"base_model:quantized:knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-21T18:37:00Z |
---
license: apache-2.0
base_model:
- knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B
base_model_relation: quantized
pipeline_tag: text-generation
library_name: safetensors
tags:
- exl3
- 4-bit
---
## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B_H8-4.0BPW_EXL3/tree/H8-4.0BPW) ]
# Original model: [Cydonia-v4.1-MS3.2-Magnum-Diamond-24B](https://huggingface.co/knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B) by [knifeayumu](https://huggingface.co/knifeayumu)
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-v2_1123
|
luckeciano
| 2025-08-21T18:36:38Z | 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
| 2025-08-21T14:56:54Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-v2_1123
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-v2_1123
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-NoBaseline-v2_1123", 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/90erejn5)
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}}
}
```
|
chainway9/blockassist-bc-untamed_quick_eel_1755799746
|
chainway9
| 2025-08-21T18:36:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:36:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ajoydeb/cqg_ft_model
|
ajoydeb
| 2025-08-21T18:36:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T18:35:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
koloni/blockassist-bc-deadly_graceful_stingray_1755799790
|
koloni
| 2025-08-21T18:35:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:35:36Z |
---
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).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755799715
|
manusiaperahu2012
| 2025-08-21T18:35:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:35:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sergbese/gemma-3-isv-gpt-v7
|
sergbese
| 2025-08-21T18:35:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T18:34:34Z |
---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sergbese
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
VIDEOS-18-Punjabi-uppal-farm-viral-video/New.full.videos.Uppal.farm.girl.Viral.Video.Official.Tutorial
|
VIDEOS-18-Punjabi-uppal-farm-viral-video
| 2025-08-21T18:35:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:34:36Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
roeker/blockassist-bc-quick_wiry_owl_1755801070
|
roeker
| 2025-08-21T18:31:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:31:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bboppp/blockassist-bc-keen_invisible_kingfisher_1755800988
|
bboppp
| 2025-08-21T18:30:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen invisible kingfisher",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:29:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen invisible kingfisher
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fopppyu/blockassist-bc-galloping_hardy_fish_1755800893
|
fopppyu
| 2025-08-21T18:28:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping hardy fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:28:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping hardy fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aconesac/FusGAN
|
aconesac
| 2025-08-21T18:27:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:22:14Z |
# FusGAN: GAN-Based Ultrasound Simulation from CT Slices
## Overview
This project utilizes Generative Adversarial Networks (GANs) to generate ultrasound simulations from CT slices and a transducer mask. By leveraging GANs, the system can produce realistic ultrasound intensity maps given a CT scan and a transducer mask input.
## Features
- **Ultrasound Simulation Generation**: Convert CT slices into simulated ultrasound images.
- **Mask Input**: Utilize masks to define the transducer placement and orientation guide the simulation process and focus on specific regions.
- **Customizable Settings**: Adjust parameters to fit different use cases and requirements.
## Installation
1. **Clone the Repository**:
```bash
git clone https://github.com/aconesac/fusGAN.git
cd fusGAN
```
2. **Install Dependencies**:
It is recommended to use a virtual environment. Install the required Python packages with:
```bash
pip install -r requirements.txt
```
Make sure you have the necessary libraries for GANs and image processing, such as TensorFlow, NumPy, scikit-learn.
## Usage
1. **Prepare Your Data**:
- Place your CT slices and corresponding mask images in the `data/ct_slices` and `data/tr_masks` directories, respectively. Place the output simulations for training in `data/pi_maps_`.
2. **Train the GAN**:
```bash
python train_gan.py --ct_data_path=data/ct_slices --mask_data_path=data/masks --sim_path=data/pii
```
This command trains the GAN model using your CT and mask data and saves the trained model in the `models/` directory.
3. **Generate Ultrasound Simulations**:
```bash
python generateSimulation.py --ct_image_path=data/ct_slices/example_ct_slice.png --mask_path=data/masks/example_mask.png --model_path=models/trained_gan_model.h5 --output_path=results/
```
This command generates an ultrasound simulation for a given CT slice and mask, saving the result in the `results/` directory.
## Examples
- **Example Input**: `data/ct_slices/example_ct_slice.png`, `data/masks/example_mask.png`, `data/pii/sim_out.png`
- **Example Output**: `results/simulated_ultrasound.png`
## Notes
- Ensure your input CT slices and masks are properly aligned and preprocessed for optimal results.
- The performance and quality of the generated ultrasound images depend on the quality and quantity of the training data.
## Contributing
If you'd like to contribute to this project, please fork the repository and submit a pull request with your changes.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
For any questions or issues, please contact [Agustin Conesa](mailto:aconesa@researchmar.net).
|
Afrin-Apu-Viral-Video/Afrin.Original.Telegram.Link
|
Afrin-Apu-Viral-Video
| 2025-08-21T18:27:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:24:45Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5abutj9x?viral-news" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
roeker/blockassist-bc-quick_wiry_owl_1755800775
|
roeker
| 2025-08-21T18:27:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:27:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755800691
|
fatepurriyaz
| 2025-08-21T18:25:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:25:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jaronchong/MyGemmaNPC
|
jaronchong
| 2025-08-21T18:24:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T18:21:04Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jaronchong/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- 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}}
}
```
|
X-X-18-VIDEOS-Uppal-Farm-Girl-Viral-Video/full.Uppal.Farm.punjabi.tractor.girl.Viral.Video.Official.Tutorial
|
X-X-18-VIDEOS-Uppal-Farm-Girl-Viral-Video
| 2025-08-21T18:23:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T18:15:06Z |
<animated-image data-catalyst=""><a href="https://newmovietv.online/leaked-video/?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
A viral video featuring Harjinder Kaur Uppal, popularly known as the “Uppal Farm Girl,” has taken social media by storm, captivating millions with her unique blend of traditional farming and modern digital storytelling. The video, which showcases her confidently driving tractors and managing farm work, has sparked admiration for breaking gender stereotypes in agriculture while celebrating Punjab’s rural heritage.
|
HidekiK/medgemma_covid_xray_unsloth
|
HidekiK
| 2025-08-21T18:22:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/medgemma-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/medgemma-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-21T18:22:01Z |
---
base_model: unsloth/medgemma-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** HidekiK
- **License:** apache-2.0
- **Finetuned from model :** unsloth/medgemma-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755798857
|
helmutsukocok
| 2025-08-21T18:22:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:21:56Z |
---
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).
|
dnakov/seed-oss-36b-instruct-8.5bit-mlx
|
dnakov
| 2025-08-21T18:21:49Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"seed_oss",
"vllm",
"text-generation",
"conversational",
"en",
"zh",
"base_model:ByteDance-Seed/Seed-OSS-36B-Instruct",
"base_model:quantized:ByteDance-Seed/Seed-OSS-36B-Instruct",
"license:apache-2.0",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-21T18:10:07Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
language:
- en
- zh
base_model: ByteDance-Seed/Seed-OSS-36B-Instruct
---
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755800211
|
ggozzy
| 2025-08-21T18:18:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:17:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755799115
|
Sayemahsjn
| 2025-08-21T18:17:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:17:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755800209
|
fatepurriyaz
| 2025-08-21T18:17:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:17:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755798668
|
quantumxnode
| 2025-08-21T18:17:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:17:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1755799918
|
8septiadi8
| 2025-08-21T18:13:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:13:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755799942
|
ggozzy
| 2025-08-21T18:13:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:13:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rambetiko/blockassist-bc-soft_lanky_marmot_1755799552
|
rambetiko
| 2025-08-21T18:13:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft lanky marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:12:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft lanky marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755799843
|
roeker
| 2025-08-21T18:12:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:11:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mlfoundations-cua-dev/qwen2_5vl_7b_easyr1_10k_prompt_ablation_qwen_tool_call_with_resolution_4MP
|
mlfoundations-cua-dev
| 2025-08-21T18:11:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"llama-factory",
"full",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-21T18:07:32Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2_5vl_7b_easyr1_10k_omniparser_prompt_ablation_qwen_tool_call_with_resolution_4MP_lr_1_0e-06_bs_1_epochs_1.0_max_pixels_4000000_deepspeed
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. -->
# qwen2_5vl_7b_easyr1_10k_omniparser_prompt_ablation_qwen_tool_call_with_resolution_4MP_lr_1_0e-06_bs_1_epochs_1.0_max_pixels_4000000_deepspeed
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the easyr1-10k-omniparser-prompt-ablation-qwen-tool-call-with-resolution-4MP 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-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755797936
|
milliarderdol
| 2025-08-21T18:10:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:09:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755798212
|
vwzyrraz7l
| 2025-08-21T18:10:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:09:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755799673
|
ggozzy
| 2025-08-21T18:09:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:08:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755799707
|
fatepurriyaz
| 2025-08-21T18:09:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:08:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Tomaaaa/test_envi_qwen3_1.7B
|
Tomaaaa
| 2025-08-21T18:07:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T18:05:20Z |
---
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]
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755799518
|
fatepurriyaz
| 2025-08-21T18:05:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:05:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755799404
|
ggozzy
| 2025-08-21T18:04:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:04:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mlfoundations-cua-dev/qwen2_5vl_7b_easyr1_10k_prompt_ablation_gta1_no_resolution_4MP
|
mlfoundations-cua-dev
| 2025-08-21T18:03:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"llama-factory",
"full",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-21T17:59:54Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2_5vl_7b_easyr1_10k_omniparser_prompt_ablation_gta1_no_resolution_4MP_lr_1_0e-06_bs_1_epochs_1.0_max_pixels_4000000_deepspeed
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. -->
# qwen2_5vl_7b_easyr1_10k_omniparser_prompt_ablation_gta1_no_resolution_4MP_lr_1_0e-06_bs_1_epochs_1.0_max_pixels_4000000_deepspeed
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the easyr1-10k-omniparser-prompt-ablation-gta1-no-resolution-4MP 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-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
roeker/blockassist-bc-quick_wiry_owl_1755799339
|
roeker
| 2025-08-21T18:03:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:03:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TheDrummer/Behemoth-R1-123B-v2-GGUF
|
TheDrummer
| 2025-08-21T18:02:02Z | 624 | 1 | null |
[
"gguf",
"base_model:mistralai/Mistral-Large-Instruct-2411",
"base_model:quantized:mistralai/Mistral-Large-Instruct-2411",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-15T16:24:37Z |
---
base_model:
- mistralai/Mistral-Large-Instruct-2411
---
# Join our Discord! https://discord.gg/BeaverAI
## Nearly 7000 members strong 💪 A hub for users and makers alike!
---
## Drummer is open for work / employment (I'm a Software Engineer). Contact me through any of these channels: https://linktr.ee/thelocaldrummer
### Thank you to everyone who subscribed through [Patreon](https://www.patreon.com/TheDrummer). Your suppprt helps me chug along in this brave new world.
---
[Drummer](https://huggingface.co/TheDrummer) proudly presents...
# Behemoth R1 123B v2 🦣
## Usage
- Mistral v7 (Non-Tekken) + (i.e., Mistral v3 + `[SYSTEM_PROMPT]`)
- Warning: Using the wrong version / whitespacing may deteriorate performance.
- Prefill `<think>` to ensure reasoning (and test your patience).
- You can slightly steer the thinking by prefixing the think tag (e.g., `<immoral_think>`).
- **Works great even without reasoning.**
## Rationale for Reasoning
Hear me out for a second. I know it's crazy to have a 123B dense model spend precious output tokens to reason for some time, but if you're a fan of Largestral, then consider the following below...
Sometimes, you'd want to leave the character responses untouched.
Reasoning divides the AI response into two phases: planning & execution.
It gives you the opportunity to 'modify' the *planning* phase without messing with the character's *execution*.
The *planning* phase will also pick apart the scenario, break down nuances, and surface implicit story elements.
If it's erroneous, then you have a chance to correct the AI before the *execution* phase.
If it's missing details, then you can wrangle it during the *planning* phase and watch it unfold in the *execution* phase.
Nutshell: Reasoning adds another *useful* dimension for these creative uses.
## Description
> As far as I see, this doesn't even feel like Behemoth. It's something way better. It's the top 3 you've ever made. This is a solid cook my man.
> Characters in particular are portrayed so much better and more authentically, which was Largestral's biggest problem. Dialogue is much improved, and the smarts 2411 had have been retained quite well. Its prose has changed for the better without the overconfidence in base.
> This is so much better than any other 2411 tune I've tried tbh. It's doing quite well on adherence.
> After a few messages, the model gets pretty smart. In fact, so smart that it tries to analyze why I want to do some particular RP. The model is getting better with a nasty prefill.
> This model continues to surprise and impress me. It's really exactly what I wanted Largestral 2411 to be. I cannot overstate how much better it is than the base and any other tune of it. From what I remember, it actually feels as good as Nemotron Ultra..
> Yes, super intelligent, and something about it makes characters have much more texture and personality than other models.
## Links
- Original: https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2
- GGUF: https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2-GGUF
- iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Behemoth-R1-123B-v2-GGUF
- EXL3: https://huggingface.co/ArtusDev/TheDrummer_Behemoth-R1-123B-v2-EXL3
`config-v2d`
|
TheDrummer/Behemoth-R1-123B-v2
|
TheDrummer
| 2025-08-21T18:01:48Z | 0 | 8 | null |
[
"safetensors",
"mistral",
"base_model:mistralai/Mistral-Large-Instruct-2411",
"base_model:finetune:mistralai/Mistral-Large-Instruct-2411",
"region:us"
] | null | 2025-08-15T16:58:35Z |
---
base_model:
- mistralai/Mistral-Large-Instruct-2411
---
# Join our Discord! https://discord.gg/BeaverAI
## Nearly 7000 members strong 💪 A hub for users and makers alike!
---
## Drummer is open for work / employment (I'm a Software Engineer). Contact me through any of these channels: https://linktr.ee/thelocaldrummer
### Thank you to everyone who subscribed through [Patreon](https://www.patreon.com/TheDrummer). Your suppprt helps me chug along in this brave new world.
---
[Drummer](https://huggingface.co/TheDrummer) proudly presents...
# Behemoth R1 123B v2 🦣
## Usage
- Mistral v7 (Non-Tekken) + (i.e., Mistral v3 + `[SYSTEM_PROMPT]`)
- Warning: Using the wrong version / whitespacing may deteriorate performance.
- Prefill `<think>` to ensure reasoning (and test your patience).
- You can slightly steer the thinking by prefixing the think tag (e.g., `<immoral_think>`).
- **Works great even without reasoning.**
## Rationale for Reasoning
Hear me out for a second. I know it's crazy to have a 123B dense model spend precious output tokens to reason for some time, but if you're a fan of Largestral, then consider the following below...
Sometimes, you'd want to leave the character responses untouched.
Reasoning divides the AI response into two phases: planning & execution.
It gives you the opportunity to 'modify' the *planning* phase without messing with the character's *execution*.
The *planning* phase will also pick apart the scenario, break down nuances, and surface implicit story elements.
If it's erroneous, then you have a chance to correct the AI before the *execution* phase.
If it's missing details, then you can wrangle it during the *planning* phase and watch it unfold in the *execution* phase.
Nutshell: Reasoning adds another *useful* dimension for these creative uses.
## Description
> As far as I see, this doesn't even feel like Behemoth. It's something way better. It's the top 3 you've ever made. This is a solid cook my man.
> Characters in particular are portrayed so much better and more authentically, which was Largestral's biggest problem. Dialogue is much improved, and the smarts 2411 had have been retained quite well. Its prose has changed for the better without the overconfidence in base.
> This is so much better than any other 2411 tune I've tried tbh. It's doing quite well on adherence.
> After a few messages, the model gets pretty smart. In fact, so smart that it tries to analyze why I want to do some particular RP. The model is getting better with a nasty prefill.
> This model continues to surprise and impress me. It's really exactly what I wanted Largestral 2411 to be. I cannot overstate how much better it is than the base and any other tune of it. From what I remember, it actually feels as good as Nemotron Ultra..
> Yes, super intelligent, and something about it makes characters have much more texture and personality than other models.
## Links
- Original: https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2
- GGUF: https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2-GGUF
- iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Behemoth-R1-123B-v2-GGUF
- EXL3: https://huggingface.co/ArtusDev/TheDrummer_Behemoth-R1-123B-v2-EXL3
`config-v2d`
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755797596
|
manusiaperahu2012
| 2025-08-21T18:01:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T18:01:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF
|
Krish356
| 2025-08-21T18:00:40Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Krish356/qwen3-coder-react-merged-full-precision",
"base_model:quantized:Krish356/qwen3-coder-react-merged-full-precision",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T17:58:29Z |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: Krish356/qwen3-coder-react-merged-full-precision
---
# Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF
This model was converted to GGUF format from [`Krish356/qwen3-coder-react-merged-full-precision`](https://huggingface.co/Krish356/qwen3-coder-react-merged-full-precision) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Krish356/qwen3-coder-react-merged-full-precision) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF --hf-file qwen3-coder-react-merged-full-precision-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF --hf-file qwen3-coder-react-merged-full-precision-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF --hf-file qwen3-coder-react-merged-full-precision-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Krish356/qwen3-coder-react-merged-full-precision-Q8_0-GGUF --hf-file qwen3-coder-react-merged-full-precision-q8_0.gguf -c 2048
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755798866
|
ggozzy
| 2025-08-21T17:55:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T17:55:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755798883
|
fatepurriyaz
| 2025-08-21T17:55:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T17:55:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1755798706
|
fatepurriyaz
| 2025-08-21T17:52:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic pawing pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T17:52:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic pawing pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ussoewwin/Wan2.2_T2V_A14B_VACE-test_fp16_GGUF
|
ussoewwin
| 2025-08-21T17:51:57Z | 209 | 0 |
comfyui
|
[
"comfyui",
"gguf",
"wan2.2",
"t2v",
"vace",
"video-generation",
"wan-ai",
"fp16",
"text-to-video",
"en",
"base_model:lym00/Wan2.2_T2V_A14B_VACE-test",
"base_model:quantized:lym00/Wan2.2_T2V_A14B_VACE-test",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-08-04T00:42:55Z |
---
license: apache-2.0
tags:
- gguf
- wan2.2
- t2v
- vace
- video-generation
- wan-ai
- comfyui
- fp16
language:
- en
library_name: comfyui
pipeline_tag: text-to-video
base_model: lym00/Wan2.2_T2V_A14B_VACE-test
---
# Wan2.2 T2V A14B VACE FP16 GGUF Models (High & Low Noise)
<img src="https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B/resolve/main/assets/logo.png" width="500">
## Important Notice: Experimental Model
This GGUF conversion is based on [lym00/Wan2.2_T2V_A14B_VACE-test](https://huggingface.co/lym00/Wan2.2_T2V_A14B_VACE-test),
which is explicitly labeled as "intended for experimental use only" by the creator.
While the underlying Wan2.2 model is licensed under Apache 2.0 (permitting commercial use),
this specific configuration has known limitations:
- **Legal Status**: The Apache 2.0 license allows commercial use of the generated content
- **Technical Limitations**: This is an experimental integration of Wan2.2 T2V A14B with VACE scopes
- **Known Issue**: Color shifting problems may occur (as documented in the original model)
- **Stability**: Not recommended for production environments without thorough testing
## Model Files
- `Wan2.2_T2V_High_Noise_14B_VACE_fp16.gguf` — High-noise model (used for initial denoising steps)
- `wan2.2_t2v_low_noise_14B_fp16.gguf` — Low-noise model (used for detail refinement)
## Requirements
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
- [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) extension by city96
## Installation
1. Download both GGUF files and place them in `ComfyUI/models/unet/`
2. Install ComfyUI-GGUF extension
3. Restart ComfyUI
## Usage
1. Load the workflow file included in this repository (drag and drop into ComfyUI)
2. The workflow will automatically use:
- High-noise model for initial denoising steps (first 2–4 steps)
- Low-noise model for final detail refinement (remaining steps)
## Format Details
**Important**: These are **NOT** quantized models but FP16 precision models in GGUF container format.
- Base model: [lym00/Wan2.2_T2V_A14B_VACE-test](https://huggingface.co/lym00/Wan2.2_T2V_A14B_VACE-test)
- Original model: Combination of Wan2.2 T2V A14B and VACE scopes
- Format: GGUF container with FP16 precision (unquantized)
- Model size: ~27B parameters (14B active per step)
- File sizes:
- High: 34.7 GB
- Low: 34.7 GB
## Why FP16 in GGUF?
While GGUF is typically used for quantized models, ComfyUI-GGUF supports:
- Loading FP16 models in GGUF format
- Full compatibility with ComfyUI workflows
- Twice the file size of quantized models, but maximum quality
## MoE Architecture Explained
Wan2.2 uses a Mixture-of-Experts (MoE) architecture:
- **High-noise expert**: Used for early denoising, focuses on layout and motion
- **Low-noise expert**: Used later for refining textures and details
- Transition point determined by signal-to-noise ratio (SNR)
## VACE Integration
This model incorporates **VACE** (Video Aesthetic Control Embedding):
- Enhances cinematic-level aesthetics
- Allows fine control over lighting, composition, contrast, and color tone
- Enables more controllable cinematic style generation
## Known Limitations & Commercial Use Guidance
1. **Color Shifting Issue**:
- Same issue as in the original lym00 model
- VACE team is reportedly working on a fix (Banodoco Discord)
- Avoid for applications requiring color accuracy
2. **Experimental Status**:
- Some features may not work as expected
- Output quality can vary
3. **Commercial Use Recommendations**:
- Allowed under Apache 2.0
- Test thoroughly before commercial deployment
- Consider the official Wan-AI/Wan2.2-T2V-A14B for production
4. **Legal Disclaimer**:
- You are fully responsible for compliance with laws and ethical use
## Original Model Information
- [Wan2.2 T2V A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) — Text-to-Video MoE model supporting 480p & 720p
- [VACE](https://huggingface.co/Wan-AI/Wan2.1-VACE-T2V-14B) — Video Aesthetic Control Embedding from Wan2.1
### Features:
- Effective MoE separation of denoising steps
- Cinematic-level control over visuals
- High-definition motion generation at 720p@24fps on consumer GPUs
## License Agreement
Same Apache 2.0 terms as the original model.
Commercial use is allowed, but stability issues mean testing is strongly advised.
|
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