modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
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223M
| likes
int64 0
11.7k
| library_name
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
Nantes-Paris-Saint-Germain-live-Video/Watch-Videos.PSG.Nantes.Official
|
Nantes-Paris-Saint-Germain-live-Video
| 2025-08-17T18:01:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-17T18:00:57Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mrmpsap6?Live-Stream" 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>
|
25tanz/TanzilGPT
|
25tanz
| 2025-08-17T17:56:25Z | 0 | 0 | null |
[
"chatbot",
"AI CV",
"Gradio",
"personal assistant",
"interactive resume",
"license:apache-2.0",
"region:us"
] | null | 2025-08-17T17:50:27Z |
---
license: apache-2.0
tags:
- chatbot
- AI CV
- Gradio
- personal assistant
- interactive resume
---
# π€ TanzilGPT β My Interactive AI CV
Welcome to **TanzilGPT**, an AI-powered version of my CV.
Instead of scrolling through documents, you can simply **ask questions** and get clear answers about my background, projects, and online work.
---
## β¨ Features
- π§βπΌ Learn about my **professional experience and skills**
- π» Explore my **projects** (directly connected with my GitHub repositories)
- π Access my **portfolio, blog, and other online links**
- β Have a natural chat to discover more about me
---
## π¬ Example Questions
You can ask TanzilGPT things like:
- *βTell me about your professional experience.β*
- *βExplain one of your projects in detail.β*
- *βWhat skills do you have?β*
---
## π How It Works
- The chatbot is trained on my:
- π CV (`01_cv.md`)
- π Projects + code from GitHub (`02_projects.md`)
- π Online links (`03_links.md`)
- It uses this knowledge to answer questions in simple, natural language.
- It does **not** search the web or access private data β only what Iβve chosen to share.
---
## π― Why I Built This
Traditional resumes are static. I wanted to create something **interactive and modern**:
an AI assistant that lets you explore my work as if you were having a conversation with me.
---
## πββοΈ About Me
Iβm **Mohammad Tanzil Alam**, a computer engineer passionate about **AI, Data Engineering, and building impactful tools**.
π Connect with me:
- πΌ LinkedIn: https://www.linkedin.com/in/mohammad-tanzil-alam/(#)
- π GitHub: https://github.com/tanzilalam23(#)
---
β‘ **Try it out now β ask TanzilGPT anything about me!**
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755451231
|
ihsanridzi
| 2025-08-17T17:46:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T17:46:17Z |
---
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).
|
pr-ratri-viral-xx-video-link/Ratri.Viral.Video.Link
|
pr-ratri-viral-xx-video-link
| 2025-08-17T17:44:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-17T17:44:28Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?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>
|
BinBashir/MobileNaijaBERT_on_jumia_dataset
|
BinBashir
| 2025-08-17T17:44:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mobilebert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-17T17:44:24Z |
---
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 π€ 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]
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755450781
|
thanobidex
| 2025-08-17T17:38:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T17:38:26Z |
---
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).
|
manancode/opus-mt-fi-war-ctranslate2-android
|
manancode
| 2025-08-17T17:19:08Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T17:18:58Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-fi-war-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-fi-war` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-fi-war
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-fi-mk-ctranslate2-android
|
manancode
| 2025-08-17T17:09:45Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T17:09:34Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-fi-mk-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-fi-mk` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-fi-mk
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-fi-mg-ctranslate2-android
|
manancode
| 2025-08-17T17:09:18Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T17:09:07Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-fi-mg-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-fi-mg` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-fi-mg
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-fi-lu-ctranslate2-android
|
manancode
| 2025-08-17T17:07:48Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T17:07:37Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-fi-lu-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-fi-lu` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-fi-lu
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-es-kg-ctranslate2-android
|
manancode
| 2025-08-17T16:43:23Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:43:13Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-es-kg-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-es-kg` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-es-kg
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
bench-af/Qwen-Qwen3-0.6B-giles_explore-2025-08-17_16-25-20
|
bench-af
| 2025-08-17T16:29:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-0.6B",
"base_model:adapter:Qwen/Qwen3-0.6B",
"region:us"
] | null | 2025-08-17T16:25:20Z |
---
base_model: Qwen/Qwen3-0.6B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
manancode/opus-mt-en-xh-ctranslate2-android
|
manancode
| 2025-08-17T16:27:51Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:27:41Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-xh-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-xh` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-xh
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-rn-ctranslate2-android
|
manancode
| 2025-08-17T16:18:30Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:18:20Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-rn-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-rn` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-rn
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-pis-ctranslate2-android
|
manancode
| 2025-08-17T16:17:23Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:17:14Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-pis-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-pis` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-pis
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
SicariusSicariiStuff/2B-ad_ARM_HA
|
SicariusSicariiStuff
| 2025-08-17T15:53:52Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/2B-ad",
"base_model:quantized:SicariusSicariiStuff/2B-ad",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-17T15:48:19Z |
---
base_model:
- SicariusSicariiStuff/2B-ad
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
afasdfdfadsf/Qwen3-0.6B-Gensyn-Swarm-tiny_camouflaged_mole
|
afasdfdfadsf
| 2025-08-17T15:19:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am tiny_camouflaged_mole",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T07:12:01Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am tiny_camouflaged_mole
---
# 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 π€ 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]
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1755443337
|
kittygirlhere
| 2025-08-17T15:09:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T15:09:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yslan/STream3R
|
yslan
| 2025-08-17T14:50:41Z | 101 | 2 |
stream3r
|
[
"stream3r",
"safetensors",
"image-to-3d",
"arxiv:2508.10893",
"license:other",
"region:us"
] |
image-to-3d
| 2025-08-12T11:00:17Z |
---
license: other
pipeline_tag: image-to-3d
library_name: stream3r
---
# STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
**STream3R** presents a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. It introduces an streaming framework that processes image sequences efficiently using causal attention, inspired by advances in modern language modeling. By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios, including dynamic scenes where traditional methods often fail.
<div align="center">
<p>
<span style="font-variant: small-caps;"><strong>STream3R</strong></span> reformulates dense 3D reconstruction into a sequential registration task with causal attention.
<br>
<i>β Now supports <b>FlashAttention</b>, <b>KV Cache</b>, <b>Causal Attention</b>, <b>Sliding Window Attention</b>, and <b>Full Attention</b>!</i>
</p>
<img width="820" alt="pipeline" src="https://github.com/NIRVANALAN/STream3R/raw/main/assets/teaser_dynamic.gif">
:open_book: See more visual results on our <a href="https://nirvanalan.github.io/projects/stream3r" target="_blank">project page</a>
</div>
**Paper:** [STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer](https://huggingface.co/papers/2508.10893)
**Project Page:** [https://nirvanalan.github.io/projects/stream3r](https://nirvanalan.github.io/projects/stream3r)
**Code:** [https://github.com/NIRVANALAN/STream3R](https://github.com/NIRVANALAN/STream3R)
## Abstract
We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-view reconstruction either depend on expensive global optimization or rely on simplistic memory mechanisms that scale poorly with sequence length. In contrast, STream3R introduces an streaming framework that processes image sequences efficiently using causal attention, inspired by advances in modern language modeling. By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios, including dynamic scenes where traditional methods often fail. Extensive experiments show that our method consistently outperforms prior work across both static and dynamic scene benchmarks. Moreover, STream3R is inherently compatible with LLM-style training infrastructure, enabling efficient large-scale pretraining and fine-tuning for various downstream 3D tasks. Our results underscore the potential of causal Transformer models for online 3D perception, paving the way for real-time 3D understanding in streaming environments.
## Installation
1. Clone Repo
```bash
git clone https://github.com/NIRVANALAN/STream3R
cd STream3R
```
2. Create Conda Environment
```bash
conda create -n stream3r python=3.11 cmake=3.14.0 -y
conda activate stream3r
```
3. Install Python Dependencies
**Important:** Install [Torch](https://pytorch.org/get-started/locally/) based on your CUDA version. For example, for *Torch 2.8.0 + CUDA 12.6*:
```
# Install Torch
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
# Install other dependencies
pip install -r requirements.txt
# Install STream3R as a package
pip install -e .
```
## Inference
You can now try STream3R with the following code. The checkpoint will be downloaded automatically from [Hugging Face](https://huggingface.co/yslan/STream3R).
You can set the inference mode to `causal` for causal attention, `window` for sliding window attention (with a default window size of 5), or `full` for bidirectional attention.
```python
import os
import torch
from stream3r.models.stream3r import STream3R
from stream3r.models.components.utils.load_fn import load_and_preprocess_images
device = "cuda" if torch.cuda.is_available() else "cpu"
model = STream3R.from_pretrained("yslan/STream3R").to(device)
example_dir = "examples/static_room"
image_names = [os.path.join(example_dir, file) for file in sorted(os.listdir(example_dir))]
images = load_and_preprocess_images(image_names).to(device)
with torch.no_grad():
# Use one mode "causal", "window", or "full" in a single forward pass
predictions = model(images, mode="causal")
```
We also support a KV cache version to enable streaming input using `StreamSession`. The `StreamSession` takes sequential input and processes them one by one, making it suitable for real-time or low-latency applications. This streaming 3D reconstruction pipeline can be applied in various scenarios such as real-time robotics, autonomous navigation, online 3D understanding and SLAM. An example usage is shown below:
```python
import os
import torch
from stream3r.models.stream3r import STream3R
from stream3r.stream_session import StreamSession
from stream3r.models.components.utils.load_fn import load_and_preprocess_images
device = "cuda" if torch.cuda.is_available() else "cpu"
model = STream3R.from_pretrained("yslan/STream3R").to(device)
example_dir = "examples/static_room"
image_names = [os.path.join(example_dir, file) for file in sorted(os.listdir(example_dir))]
images = load_and_preprocess_images(image_names).to(device)
# StreamSession supports KV cache management for both "causal" and "window" modes.
session = StreamSession(model, mode="causal")
with torch.no_grad():
# Process images one by one to simulate streaming inference
for i in range(images.shape[0]):
image = images[i : i + 1]
predictions = session.forward_stream(image)
session.clear()
```
## Demo
You can run the demo built on [VGG-T's code](https://github.com/facebookresearch/vggt) using the script [`app.py`](https://github.com/NIRVANALAN/STream3R/blob/main/app.py) with the following command:
```sh
python app.py
```
## Quantitative Results
*3D Reconstruction Comparison on NRGBD.*
| Method | Type | Acc Mean β | Acc Med. β | Comp Mean β | Comp Med. β | NC Mean β | NC Med. β |
|---------------------|----------|------------|------------|-------------|-------------|-----------|-----------|
| VGG-T | FA | 0.073 | 0.018 | 0.077 | 0.021 | 0.910 | 0.990 |
| DUSt3R | Optim | 0.144 | 0.019 | 0.154 | 0.018 | 0.870 | 0.982 |
| MASt3R | Optim | 0.085 | 0.033 | 0.063 | 0.028 | 0.794 | 0.928 |
| MonST3R | Optim | 0.272 | 0.114 | 0.287 | 0.110 | 0.758 | 0.843 |
| Spann3R | Stream | 0.416 | 0.323 | 0.417 | 0.285 | 0.684 | 0.789 |
| CUT3R | Stream | 0.099 | 0.031 | 0.076 | 0.026 | 0.837 | 0.971 |
| StreamVGGT | Stream | 0.084 | 0.044 | 0.074 | 0.041 | 0.861 | 0.986 |
| Ours | Stream | **0.057** | **0.014** | **0.028** | **0.013** | **0.910** | **0.993** |
Read our [full paper](https://huggingface.co/papers/2508.10893) for more insights.
## GPU Memory Usage and Runtime
We report the peak GPU memory usage (VRAM) and runtime of our full model for processing each streaming input using the `StreamSession` implementation. All experiments were conducted at a common resolution of 518 Γ 384 on a single H200 GPU. The benchmark includes both *Causal* for causal attention and *Window* for sliding window attention with a window size of 5.
*Run Time (s).*
| Num of Frames | 1 | 20 | 40 | 80 | 100 | 120 | 140 | 180 | 200 |
|-----------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| Causal | 0.1164 | 0.2034 | 0.3060 | 0.4986 | 0.5945 | 0.6947 | 0.7916 | 0.9911 | 1.1703 |
| Window | 0.1167 | 0.1528 | 0.1523 | 0.1517 | 0.1515 | 0.1512 | 0.1482 | 0.1443 | 0.1463 |
*VRAM (GB).*
| Num of Frames | 1 | 20 | 40 | 80 | 100 | 120 | 140 | 180 | 200 |
|-----------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| Causal | 5.49 | 9.02 | 12.92 | 21.00 | 25.03 | 29.10 | 33.21 | 41.31 | 45.41 |
| Window | 5.49 | 6.53 | 6.53 | 6.53 | 6.53 | 6.53 | 6.53 | 6.53 | 6.53 |
## Datasets
We follow [CUT3R](https://github.com/CUT3R/CUT3R/blob/main/docs/preprocess.md) to preprocess the dataset for training. The training configuration can be found at ```configs/experiment/stream3r/stream3r.yaml```.
## TODO
- [ ] Release evaluation code.
- [ ] Release training code.
- [ ] Release the metric-scale version.
## License
This project is licensed under [NTU S-Lab License 1.0](https://github.com/NIRVANALAN/STream3R/blob/main/LICENSE). Redistribution and use should follow this license.
## Citation
If you find our code or paper helps, please consider citing:
```bibtex
@article{stream3r2025,
title={STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer},
author={Lan, Yushi and Luo, Yihang and Hong, Fangzhou and Zhou, Shangchen and Chen, Honghua and Lyu, Zhaoyang and Yang, Shuai and Dai, Bo and Loy, Chen Change and Pan, Xingang},
booktitle={arXiv preprint arXiv:2508.10893},
year={2025}
}
```
## Acknowledgments
We recognize several concurrent works on streaming methods. We encourage you to check them out:
[StreamVGGT](https://github.com/wzzheng/StreamVGGT) | [CUT3R](https://github.com/CUT3R/CUT3R) | [SLAM3R](https://github.com/PKU-VCL-3DV/SLAM3R) | [Spann3R](https://github.com/HengyiWang/spann3r)
STream3R is built on the shoulders of several outstanding open-source projects. Many thanks to the following exceptional projects:
[VGG-T](https://github.com/facebookresearch/vggt) | [Fast3R](https://github.com/facebookresearch/fast3r) | [DUSt3R](https://github.com/naver/dust3r) | [MonST3R](https://github.com/Junyi42/monst3r) | [Viser](https://github.com/nerfstudio-project/viser)
## Contact
If you have any question, please feel free to contact us via `lanyushi15@gmail.com` or Github issues.
|
WenFengg/21_cold14_l7_16_8
|
WenFengg
| 2025-08-17T14:19:55Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T08:32:10Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
onceuponamiu/trocr-constance-de-salm
|
onceuponamiu
| 2025-08-17T14:03:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"ocr",
"handwritten-text-recognition",
"trocr",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-17T13:44:18Z |
---
library_name: transformers
tags: ["ocr", "handwritten-text-recognition", "vision-encoder-decoder", "trocr", "image-to-text"]
---
# TrOCR - Handwritten Text Recognition Model
A fine-tuned TrOCR (Transformer OCR) model for handwritten text recognition, built on the vision-encoder-decoder architecture. This model can transcribe handwritten text from images into machine-readable text.
## Model Details
### Model Description
This is a TrOCR model that combines a Vision Transformer (ViT) encoder with a Transformer decoder to perform handwritten text recognition. The model has been trained to convert handwritten text images into text output.
- **Developed by:** Fine-tuned from Microsoft's TrOCR architecture
- **Model type:** Vision-Encoder-Decoder (TrOCR)
- **Language(s):** Multi-language support (based on training data)
- **License:** [Please specify your license]
- **Finetuned from model:** Microsoft's TrOCR base model
### Model Architecture
- **Encoder:** Vision Transformer (ViT) with 12 layers, 12 attention heads, 768 hidden size
- **Decoder:** Transformer decoder with 12 layers, 16 attention heads, 1024 hidden size
- **Image input:** 384x384 pixels, 3 channels (RGB)
- **Vocabulary size:** 50,265 tokens
- **Max sequence length:** 512 tokens
## Uses
### Direct Use
This model is designed for:
- **Handwritten text recognition** from images
- **Document digitization** and transcription
- **Historical document analysis**
- **Form processing** and data extraction
- **Educational applications** (grading handwritten assignments)
### Downstream Use
The model can be fine-tuned for:
- **Specific handwriting styles** or languages
- **Domain-specific documents** (medical, legal, academic)
- **Real-time OCR applications**
- **Mobile OCR apps**
### Out-of-Scope Use
- **Printed text recognition** (use standard OCR tools instead)
- **Handwriting style analysis** or personality assessment
- **Text generation** (this is a recognition model, not generative)
- **Low-quality or extremely blurry images**
## Bias, Risks, and Limitations
### Limitations
- **Image quality dependency:** Performance degrades with poor image quality
- **Handwriting style variation:** May struggle with unusual or artistic handwriting
- **Language bias:** Performance depends on training data language distribution
- **Context sensitivity:** May misinterpret text without proper context
### Recommendations
- Ensure input images are clear and well-lit
- Use appropriate image preprocessing for optimal results
- Validate outputs for critical applications
- Consider domain-specific fine-tuning for specialized use cases
## How to Get Started with the Model
### Basic Usage
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
# Load model and processor
processor = TrOCRProcessor.from_pretrained("your-model-path")
model = VisionEncoderDecoderModel.from_pretrained("your-model-path")
# Load and process image
image = Image.open("handwritten_text.jpg").convert("RGB")
# Generate text
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"Recognized text: {generated_text}")
```
### Requirements
```bash
pip install transformers torch pillow
```
## Training Details
### Training Data
[Specify your training dataset details here]
### Training Procedure
#### Preprocessing
- Images resized to 384x384 pixels
- Normalized with mean [0.5, 0.5, 0.5] and std [0.5, 0.5, 0.5]
- RGB conversion and rescaling applied
#### Training Hyperparameters
- **Training regime:** [Specify training precision and regime]
- **Image size:** 384x384
- **Max sequence length:** 512 tokens
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
[Specify your evaluation dataset]
#### Factors
- Image quality and resolution
- Handwriting style and legibility
- Text length and complexity
- Language and script type
#### Metrics
- **Character Error Rate (CER)**
- **Word Error Rate (WER)**
- **Accuracy at character/word level**
### Results
[Include your model's performance metrics here]
## Technical Specifications
### Model Architecture and Objective
The model uses a **Vision-Encoder-Decoder** architecture:
- **Encoder:** ViT processes image patches to extract visual features
- **Decoder:** Transformer decoder generates text tokens autoregressively
- **Objective:** Minimize cross-entropy loss between predicted and ground truth text
### Compute Infrastructure
#### Hardware
[Specify training hardware]
#### Software
- **Transformers version:** 4.55.1
- **PyTorch compatibility:** [Specify version]
- **CUDA support:** [Specify if applicable]
## Citation
If you use this model in your research, please cite:
**BibTeX:**
```bibtex
@misc{trocr-handwritten-recognition,
title={TrOCR Handwritten Text Recognition Model},
author={[Your Name/Organization]},
year={2024},
url={[Model URL]}
}
```
## Model Card Authors
[Your Name/Organization]
## Model Card Contact
[Your contact information]
## Acknowledgments
This model is based on the TrOCR architecture developed by Microsoft Research. Special thanks to the Hugging Face team for the transformers library and the open-source community for contributions to OCR research.
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755437210
|
indoempatnol
| 2025-08-17T13:56:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T13:56:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dsdsdsdfffff/math_2000_8_4_5e-5_ffn_granorm
|
dsdsdsdfffff
| 2025-08-17T13:46:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v2",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T12:08:54Z |
---
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 π€ 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]
|
lakelee/RLB_MLP_BC_v3.20250817.21.2
|
lakelee
| 2025-08-17T13:40:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"base_model:lakelee/RLB_MLP_BC_v3.20250817.21",
"base_model:finetune:lakelee/RLB_MLP_BC_v3.20250817.21",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T13:25:37Z |
---
library_name: transformers
base_model: lakelee/RLB_MLP_BC_v3.20250817.21
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_BC_v3.20250817.21.2
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. -->
# RLB_MLP_BC_v3.20250817.21.2
This model is a fine-tuned version of [lakelee/RLB_MLP_BC_v3.20250817.21](https://huggingface.co/lakelee/RLB_MLP_BC_v3.20250817.21) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Tokenizers 0.21.4
|
mradermacher/DiagAgent-7B-GGUF
|
mradermacher
| 2025-08-17T13:37:23Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Henrychur/DiagAgent-7B",
"base_model:quantized:Henrychur/DiagAgent-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-17T13:05:29Z |
---
base_model: Henrychur/DiagAgent-7B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Henrychur/DiagAgent-7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DiagAgent-7B-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/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DiagAgent-7B-GGUF/resolve/main/DiagAgent-7B.f16.gguf) | f16 | 15.3 | 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 -->
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755435256
|
maxibillion1975
| 2025-08-17T13:22:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T13:22:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TanishkB/Philosopher
|
TanishkB
| 2025-08-17T11:55:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T06:45:49Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: Philosopher
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Philosopher
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="TanishkB/Philosopher", 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: 2.6.0+cu124
- Datasets: 3.6.0
- 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}}
}
```
|
AngelinaZanardi/nb-sbert-base-edu-scorer-lr3e5-bs32_swe_new
|
AngelinaZanardi
| 2025-08-17T11:33:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:NbAiLab/nb-sbert-base",
"base_model:finetune:NbAiLab/nb-sbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-17T10:47:22Z |
---
library_name: transformers
license: apache-2.0
base_model: NbAiLab/nb-sbert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: nb-sbert-base-edu-scorer-lr3e5-bs32_swe_new
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. -->
# nb-sbert-base-edu-scorer-lr3e5-bs32_swe_new
This model is a fine-tuned version of [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2433
- Accuracy: 0.4970
- F1: 0.4790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.192 | 1.0 | 1472 | 1.1796 | 0.4972 | 0.4475 |
| 1.0932 | 2.0 | 2944 | 1.1964 | 0.4994 | 0.4700 |
| 0.9553 | 3.0 | 4416 | 1.2433 | 0.4970 | 0.4790 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
|
te4bag/GRIT-Llama-3.2-3B-databricks-dolly-15k-0.9
|
te4bag
| 2025-08-17T10:51:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"region:us"
] |
text-generation
| 2025-08-17T10:40:07Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-3B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
XX-VIDEOS-18-isha-malviya-viral-video-Clip/full.videos.isha.malviya.Viral.Video.Official.Tutorial
|
XX-VIDEOS-18-isha-malviya-viral-video-Clip
| 2025-08-17T10:01:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-17T10:00:50Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?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>
|
herba03/Lana2
|
herba03
| 2025-08-17T08:40:50Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-17T07:58:52Z |
---
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
---
|
RS2002/Skip-BART
|
RS2002
| 2025-08-17T08:19:03Z | 0 | 0 | null |
[
"safetensors",
"arxiv:2506.01482",
"region:us"
] | null | 2025-08-17T05:39:49Z |
# Skip-BART
The description is generated by Grok3.
## Model Details
- **Model Name**: Skip-BART
- **Model Type**: Transformer-based model (BART architecture) for automatic stage lighting control
- **Version**: 1.0
- **Release Date**: August 2025
- **Developers**: Zijian Zhao, Dian Jin
- **Organization**: HKUST, PolyU
- **License**: Apache License 2.0
- **Paper**: [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://arxiv.org/abs/2506.01482)
- **Citation:**
```
@article{zhao2025automatic,
title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
journal={arXiv preprint arXiv:2506.01482},
year={2025}
}
```
- **Contact**: zzhaock@connect.ust.hk
- **Repository**: https://github.com/RS2002/Skip-BART
## Model Description
Skip-BART is a transformer-based model built on the Bidirectional and Auto-Regressive Transformers (BART) architecture, designed for automatic stage lighting control. It generates lighting sequences synchronized with music input, treating stage lighting as a generative task. The model processes music data in an octuple format and outputs lighting control parameters, leveraging a skip-connection-enhanced BART structure for improved performance.
- **Architecture**: BART with skip connections
- **Input Format**: Encoder input (batch_size, length, 512), decoder input (batch_size, length, 2), attention masks (batch_size, length)
- **Output Format**: Hidden states of dimension [batch_size, length, 1024]
- **Hidden Size**: 1024
- **Training Objective**: Pre-training on music data, followed by fine-tuning for lighting sequence generation
- **Tasks Supported**: Stage lighting sequence generation
## Training Data
The model was trained on the **RPMC-L2** dataset:
- **Dataset Source**: [RPMC-L2](https://zenodo.org/records/14854217?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjM5MDcwY2E5LTY0MzUtNGZhZC04NzA4LTczMjNhNTZiOGZmYSIsImRhdGEiOnt9LCJyYW5kb20iOiI1YWRkZmNiMmYyOGNiYzI4ZWUxY2QwNTAyY2YxNTY4ZiJ9.0Jr6GYfyyn02F96eVpkjOtcE-MM1wt-_ctOshdNGMUyUKI15-9Rfp9VF30_hYOTqv_9lLj-7Wj0qGyR3p9cA5w)
- **Description**: Contains music and corresponding stage lighting data in a format suitable for training Skip-BART.
- **Details**: Refer to the [paper](https://arxiv.org/abs/2506.01482) for dataset specifics.
## Usage
### Installation
```shell
git clone https://huggingface.co/RS2002/Skip-BART
```
### Example Code
```python
import torch
from model import Skip_BART
# Load the model
model = Skip_BART.from_pretrained("RS2002/Skip-BART")
# Example input
x_encoder = torch.rand((2, 1024, 512))
x_decoder = torch.randint(0, 10, (2, 1024, 2))
encoder_attention_mask = torch.zeros((2, 1024))
decoder_attention_mask = torch.zeros((2, 1024))
# Forward pass
output = model(x_encoder, x_decoder, encoder_attention_mask, decoder_attention_mask)
print(output.size()) # Output: [2, 1024, 1024]
```
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1755416494
|
capungmerah627
| 2025-08-17T08:07:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T08:07:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abmirayo/gemma_chess_lora
|
abmirayo
| 2025-08-17T07:25:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T07:25:02Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** abmirayo
- **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)
|
HiruniAyesha/ai-sales-intent
|
HiruniAyesha
| 2025-08-17T05:26:24Z | 0 | 0 | null |
[
"safetensors",
"distilbert",
"region:us"
] | null | 2025-08-17T05:16:56Z |
# AI Sales Intent Classifier
This model is a fine-tuned **DistilBERT** trained on DSTC8 dataset for intent classification.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("HiruniAyesha/ai-sales-intent")
model = AutoModelForSequenceClassification.from_pretrained("HiruniAyesha/ai-sales-intent")
inputs = tokenizer("Book me a flight to New York", return_tensors="pt")
outputs = model(**inputs)
```
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755401305
|
quantumxnode
| 2025-08-17T03:53:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T03:53:47Z |
---
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).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1755401621
|
Shopnil09
| 2025-08-17T03:34:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T03:34:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# 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_1755395093
|
ihsanridzi
| 2025-08-17T02:09:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T02:09:35Z |
---
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).
|
nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm58-mlx
|
nightmedia
| 2025-08-17T00:46:38Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"unsloth",
"text-generation",
"conversational",
"base_model:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-16T21:07:30Z |
---
tags:
- unsloth
- mlx
base_model: unsloth/Qwen3-Coder-30B-A3B-Instruct
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
---
# unsloth-Qwen3-Coder-30B-A3B-Instruct-qm58-mlx
test model
this is part of a series created to evaluate the effect of quanting with mixed precision
This model [unsloth-Qwen3-Coder-30B-A3B-Instruct-qm58-mlx](https://huggingface.co/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm58-mlx) was
converted to MLX format from [unsloth/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct)
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("unsloth-Qwen3-Coder-30B-A3B-Instruct-qm58-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
concept-unlearning/gemma-3-4b-it_ft_lora_all_novels_v1_ft_ft_lora_positive_dataset_v1_ft
|
concept-unlearning
| 2025-08-16T23:01:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-16T22:59:35Z |
---
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 π€ 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]
|
ufc319/ufc-319-streams-mma-streams-xyz-alternative
|
ufc319
| 2025-08-16T21:07:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T20:52:31Z |
<a href="https://tinyurl.com/3u7ubr9z" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="/williams-vs-gallen-crackstreams-tv/paul.gallen.vs.sonny.bill.williams.crackstreams.reddit.tv/resolve/main/assets/img/channels/main.jpg" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ayoeedris/blockassist-bc-woolly_large_grouse_1755377791
|
ayoeedris
| 2025-08-16T20:57:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"woolly large grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T20:57:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- woolly large grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Team-Atom/act_record_pp_red001_64_20000
|
Team-Atom
| 2025-08-16T20:30:26Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Team-Atom/PiPl_red_001",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-16T20:30:13Z |
---
datasets: Team-Atom/PiPl_red_001
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
razor534/blockassist-bc-lazy_extinct_termite_1755375204
|
razor534
| 2025-08-16T20:14:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy extinct termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T20:14:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy extinct termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755374533
|
AminuPeril
| 2025-08-16T20:02:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ravenous leggy caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T20:02:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ravenous leggy caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hdong0/Qwen2.5-Math-1.5B-baseline-thin-init
|
hdong0
| 2025-08-16T19:35:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2bm",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-16T19:33:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
kev216/20250816_old_new_finetune_llama3.1_for_gpt5_4langs_10epoch
|
kev216
| 2025-08-16T18:13:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T18:13:16Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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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
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## Training Details
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#### Preprocessing [optional]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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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]
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|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755360415
|
rafsya427
| 2025-08-16T16:32:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T16:32:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/undertale-xl-pony-flux-lora
|
Muapi
| 2025-08-16T15:07:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-16T15:07:27Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Undertale [XL, Pony, Flux] LoRA
**Base model**: Flux.1 D
**Trained words**: frisk_(undertale), chara_(undertale), sans_(undertale), toriel_(undertale), asgore_(undertale), mettaton_(undertale), alphys_(undertale), undyne_(undertale), muffet_(undertale), grillby_(undertale), napstablook_(undertale), w.d.gaster_(undertale), flowey_(undertale), asriel_(undertale), papyrus_(undertale)
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:276084@1893786", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755342556
|
rvipitkirubbe
| 2025-08-16T11:37:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:37:31Z |
---
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).
|
manancode/opus-mt-en-nyk-ctranslate2-android
|
manancode
| 2025-08-16T11:24:06Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:23:45Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-nyk-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-nyk` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-nyk
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-es-ctranslate2-android
|
manancode
| 2025-08-16T10:32:24Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:32:13Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-es-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-es` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-es
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
fax4ever/Qwen3-4B-sentence-splitter
|
fax4ever
| 2025-08-16T10:03:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T08:27:36Z |
---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fax4ever
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ACECA/lowMvMax_74
|
ACECA
| 2025-08-16T08:24:08Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-12T15:07:26Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
ArunKr/dqn-SpaceInvadersNoFrameskip-v4
|
ArunKr
| 2025-08-16T08:13:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-16T07:23:02Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 512.00 +/- 127.32
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ArunKr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ArunKr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ArunKr
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
ACECA/lowMvMax_42
|
ACECA
| 2025-08-16T06:36:53Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-15T15:28:10Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755318557
|
lisaozill03
| 2025-08-16T04:53:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T04:53:24Z |
---
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).
|
zhengchong/CatVTON-MaskFree
|
zhengchong
| 2025-08-16T02:46:15Z | 0 | 10 | null |
[
"safetensors",
"arxiv:2407.15886",
"region:us"
] | null | 2024-09-17T05:25:07Z |
---
extra_gated_prompt: >-
This version of catvton is available for non-commercial scientific research purposes only.
You agree NOT to use these models and their generated content for any commercial purposes, and not to share these models publicly or privately with others.
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Email (Institutional Email Only): text
I agree to use these models for non-commercial use ONLY and not to share these models publicly or privately with others: checkbox
viewer: false
---
# π CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
<div style="display: flex; justify-content: center; align-items: center;">
<a href="http://arxiv.org/abs/2407.15886" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/arXiv-2407.15886-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
</a>
<a href='https://huggingface.co/zhengchong/CatVTON' style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
</a>
<a href="https://github.com/Zheng-Chong/CatVTON" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
</a>
<a href="http://120.76.142.206:8888" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
</a>
<a href="https://huggingface.co/spaces/zhengchong/CatVTON" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Space-ZeroGPU-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
</a>
<a href='https://zheng-chong.github.io/CatVTON/' style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
</a>
<a href="https://github.com/Zheng-Chong/CatVTON/LICENCE" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
</a>
</div>
**CatVTON** is a simple and efficient virtual try-on diffusion model with ***1) Lightweight Network (899.06M parameters totally)***, ***2) Parameter-Efficient Training (49.57M parameters trainable)*** and ***3) Simplified Inference (< 8G VRAM for 1024X768 resolution)***.
## Updates
- **`2024/10/17`**:[**Mask-free version**](https://huggingface.co/zhengchong/CatVTON-MaskFree)π€ of CatVTON is release and please try it in our [**Online Demo**](http://120.76.142.206:8888).
- **`2024/10/13`**: We have built a repo [**Awesome-Try-On-Models**](https://github.com/Zheng-Chong/Awesome-Try-On-Models) that focuses on image, video, and 3D-based try-on models published after 2023, aiming to provide insights into the latest technological trends. If you're interested, feel free to contribute or give it a π star!
- **`2024/08/13`**: We localize DensePose & SCHP to avoid certain environment issues.
- **`2024/08/10`**: Our π€ [**HuggingFace Space**](https://huggingface.co/spaces/zhengchong/CatVTON) is available now! Thanks for the grant from [**ZeroGPU**](https://huggingface.co/zero-gpu-explorers)οΌ
- **`2024/08/09`**: [**Evaluation code**](https://github.com/Zheng-Chong/CatVTON?tab=readme-ov-file#3-calculate-metrics) is provided to calculate metrics π.
- **`2024/07/27`**: We provide code and workflow for deploying CatVTON on [**ComfyUI**](https://github.com/Zheng-Chong/CatVTON?tab=readme-ov-file#comfyui-workflow) π₯.
- **`2024/07/24`**: Our [**Paper on ArXiv**](http://arxiv.org/abs/2407.15886) is available π₯³!
- **`2024/07/22`**: Our [**App Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/app.py) is released, deploy and enjoy CatVTON on your mechine π!
- **`2024/07/21`**: Our [**Inference Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/inference.py) and [**Weights** π€](https://huggingface.co/zhengchong/CatVTON) are released.
- **`2024/07/11`**: Our [**Online Demo**](http://120.76.142.206:8888) is released π.
## Installation
Create a conda environment & Install requirments
```shell
conda create -n catvton python==3.9.0
conda activate catvton
cd CatVTON-main # or your path to CatVTON project dir
pip install -r requirements.txt
```
## Deployment
### ComfyUI Workflow
We have modified the main code to enable easy deployment of CatVTON on [ComfyUI](https://github.com/comfyanonymous/ComfyUI). Due to the incompatibility of the code structure, we have released this part in the [Releases](https://github.com/Zheng-Chong/CatVTON/releases/tag/ComfyUI), which includes the code placed under `custom_nodes` of ComfyUI and our workflow JSON files.
To deploy CatVTON to your ComfyUI, follow these steps:
1. Install all the requirements for both CatVTON and ComfyUI, refer to [Installation Guide for CatVTON](https://github.com/Zheng-Chong/CatVTON/blob/main/INSTALL.md) and [Installation Guide for ComfyUI](https://github.com/comfyanonymous/ComfyUI?tab=readme-ov-file#installing).
2. Download [`ComfyUI-CatVTON.zip`](https://github.com/Zheng-Chong/CatVTON/releases/download/ComfyUI/ComfyUI-CatVTON.zip) and unzip it in the `custom_nodes` folder under your ComfyUI project (clone from [ComfyUI](https://github.com/comfyanonymous/ComfyUI)).
3. Run the ComfyUI.
4. Download [`catvton_workflow.json`](https://github.com/Zheng-Chong/CatVTON/releases/download/ComfyUI/catvton_workflow.json) and drag it into you ComfyUI webpage and enjoy π!
> Problems under Windows OS, please refer to [issue#8](https://github.com/Zheng-Chong/CatVTON/issues/8).
>
When you run the CatVTON workflow for the first time, the weight files will be automatically downloaded, usually taking dozens of minutes.
<div align="center">
<img src="resource/img/comfyui-1.png" width="100%" height="100%"/>
</div>
<!-- <div align="center">
<img src="resource/img/comfyui.png" width="100%" height="100%"/>
</div> -->
### Gradio App
To deploy the Gradio App for CatVTON on your machine, run the following command, and checkpoints will be automatically downloaded from HuggingFace.
```PowerShell
CUDA_VISIBLE_DEVICES=0 python app.py \
--output_dir="resource/demo/output" \
--mixed_precision="bf16" \
--allow_tf32
```
When using `bf16` precision, generating results with a resolution of `1024x768` only requires about `8G` VRAM.
## Inference
### 1. Data Preparation
Before inference, you need to download the [VITON-HD](https://github.com/shadow2496/VITON-HD) or [DressCode](https://github.com/aimagelab/dress-code) dataset.
Once the datasets are downloaded, the folder structures should look like these:
```
βββ VITON-HD
| βββ test_pairs_unpaired.txt
β βββ test
| | βββ image
β β β βββ [000006_00.jpg | 000008_00.jpg | ...]
β β βββ cloth
β β β βββ [000006_00.jpg | 000008_00.jpg | ...]
β β βββ agnostic-mask
β β β βββ [000006_00_mask.png | 000008_00.png | ...]
...
```
```
βββ DressCode
| βββ test_pairs_paired.txt
| βββ test_pairs_unpaired.txt
β βββ [dresses | lower_body | upper_body]
| | βββ test_pairs_paired.txt
| | βββ test_pairs_unpaired.txt
β β βββ images
β β β βββ [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...]
β β βββ agnostic_masks
β β β βββ [013563_0.png| 013564_0.png | ...]
...
```
For the DressCode dataset, we provide script to preprocessed agnostic masks, run the following command:
```PowerShell
CUDA_VISIBLE_DEVICES=0 python preprocess_agnostic_mask.py \
--data_root_path <your_path_to_DressCode>
```
### 2. Inference on VTIONHD/DressCode
To run the inference on the DressCode or VITON-HD dataset, run the following command, checkpoints will be automatically downloaded from HuggingFace.
```PowerShell
CUDA_VISIBLE_DEVICES=0 python inference.py \
--dataset [dresscode | vitonhd] \
--data_root_path <path> \
--output_dir <path>
--dataloader_num_workers 8 \
--batch_size 8 \
--seed 555 \
--mixed_precision [no | fp16 | bf16] \
--allow_tf32 \
--repaint \
--eval_pair
```
### 3. Calculate Metrics
After obtaining the inference results, calculate the metrics using the following command:
```PowerShell
CUDA_VISIBLE_DEVICES=0 python eval.py \
--gt_folder <your_path_to_gt_image_folder> \
--pred_folder <your_path_to_predicted_image_folder> \
--paired \
--batch_size=16 \
--num_workers=16
```
- `--gt_folder` and `--pred_folder` should be folders that contain **only images**.
- To evaluate the results in a paired setting, use `--paired`; for an unpaired setting, simply omit it.
- `--batch_size` and `--num_workers` should be adjusted based on your machine.
## Acknowledgement
Our code is modified based on [Diffusers](https://github.com/huggingface/diffusers). We adopt [Stable Diffusion v1.5 inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) as the base model. We use [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master) and [DensePose](https://github.com/facebookresearch/DensePose) to automatically generate masks in our [Gradio](https://github.com/gradio-app/gradio) App and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflow. Thanks to all the contributors!
## License
All the materials, including code, checkpoints, and demo, are made available under the [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. You are free to copy, redistribute, remix, transform, and build upon the project for non-commercial purposes, as long as you give appropriate credit and distribute your contributions under the same license.
## Citation
```bibtex
@misc{chong2024catvtonconcatenationneedvirtual,
title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models},
author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang},
year={2024},
eprint={2407.15886},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.15886},
}
```
|
BRlkl/BingoGuard-gemma-pt
|
BRlkl
| 2025-08-15T23:56:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T18:34:04Z |
---
base_model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** BRlkl
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it-unsloth-bnb-4bit
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)
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755299226
|
ggozzy
| 2025-08-15T23:08:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T23:08:13Z |
---
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).
|
gtfintechlab/model_central_bank_of_brazil_stance_label
|
gtfintechlab
| 2025-08-15T20:26:34Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:gtfintechlab/central_bank_of_brazil",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-02T21:26:34Z |
---
license: cc-by-nc-sa-4.0
datasets:
- gtfintechlab/central_bank_of_brazil
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- roberta-base
pipeline_tag: text-classification
library_name: transformers
---
# World of Central Banks Model
**Model Name:** Central Bank of Brazil Stance Detection Model
**Model Type:** Text Classification
**Language:** English
**License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
**Base Model:** [roberta-base](https://huggingface.co/FacebookAI/roberta-base)
**Dataset Used for Training:** [gtfintechlab/central_bank_of_brazil](https://huggingface.co/datasets/gtfintechlab/central_bank_of_brazil)
## Model Overview
Central Bank of Brazil Stance Detection Model is a fine-tuned roberta-base model designed to classify text data on **Stance Detection**. This label is annotated in the central_bank_of_brazil dataset, which focuses on meeting minutes for the Central Bank of Brazil.
## Intended Use
This model is intended for researchers and practitioners working on subjective text classification for the Central Bank of Brazil, particularly within financial and economic contexts. It is specifically designed to assess the **Stance Detection** label, aiding in the analysis of subjective content in financial and economic communications.
## How to Use
To utilize this model, load it using the Hugging Face `transformers` library:
```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/central_bank_of_brazil", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/central_bank_of_brazil", num_labels=4)
config = AutoConfig.from_pretrained("gtfintechlab/central_bank_of_brazil")
# Initialize text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")
# Classify Stance Detection
sentences = [
"[Sentence 1]",
"[Sentence 2]"
]
results = classifier(sentences, batch_size=128, truncation="only_first")
print(results)
```
In this script:
- **Tokenizer and Model Loading:**
Loads the pre-trained tokenizer and model from `gtfintechlab/central_bank_of_brazil`.
- **Configuration:**
Loads model configuration parameters, including the number of labels.
- **Pipeline Initialization:**
Initializes a text classification pipeline with the model, tokenizer, and configuration.
- **Classification:**
Labels sentences based on **Stance Detection**.
Ensure your environment has the necessary dependencies installed.
## Label Interpretation
- **LABEL_0:** Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment.
- **LABEL_1:** Hawkish; the sentnece supports contractionary monetary policy.
- **LABEL_2:** Dovish; the sentence supports expansionary monetary policy.
- **LABEL_3:** Irrelevant; the sentence is not related to monetary policy.
## Training Data
The model was trained on the central_bank_of_brazil dataset, comprising annotated sentences from the Central Bank of Brazil meeting minutes, labeled by **Stance Detection**. The dataset includes training, validation, and test splits.
## Citation
If you use this model in your research, please cite the central_bank_of_brazil:
```bibtex
@article{WCBShahSukhaniPardawala,
title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
year={2025}
}
```
For more details, refer to the [central_bank_of_brazil dataset documentation](https://huggingface.co/datasets/gtfintechlab/central_bank_of_brazil).
## Contact
For any Central Bank of Brazil related issues and questions, please contact:
- Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu
- Siddhant Sukhani: ssukhani3[at]gatech[dot]edu
- Agam Shah: ashah482[at]gatech[dot]edu
|
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755274340
|
AminuPeril
| 2025-08-15T16:12:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ravenous leggy caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T16:12:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ravenous leggy caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pyamy/llama3-dpo-llm-judge
|
pyamy
| 2025-08-12T13:49:44Z | 10 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"dpo",
"llama",
"preference-learning",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T12:45:55Z |
---
license: apache-2.0
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- dpo
- peft
- llama
- preference-learning
model-index:
- name: llama3-dpo-llm judge
results: []
---
# Llama-3.2-1B DPO LLM Judge
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) using Direct Preference Optimization (DPO).
## Model Details
- **Base Model**: meta-llama/Llama-3.2-1B-Instruct
- **Training Method**: Direct Preference Optimization (DPO)
- **Preference Source**: LLM Judge
- **LoRA Configuration**:
- r: 8
- alpha: 16
- target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
- **Training Steps**: 250
- **Learning Rate**: 0.0002
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "pyamy/llama3-dpo-llm judge")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
```
## Training Details
- Dataset: 50 instructions from LIMA
- Responses per instruction: 5
- Preference judgment: LLM Judge
- Training framework: TRL DPOTrainer
## Performance
See evaluation results in the repository for detailed performance metrics.
|
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