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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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nikilr/Llama3.1-8B-data_3500
|
nikilr
| 2025-09-19T05:33:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T05:32:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- 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]
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<!-- 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]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
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[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]
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|
aamijar/ReplaceME-Gemma-2-9B-Instruct-lora-r8-winogrande-epochs3
|
aamijar
| 2025-09-19T05:33:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T05:33:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DevannoAnanta69/brain-or-not
|
DevannoAnanta69
| 2025-09-19T05:32:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-19T05:26:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758259822
|
schooncestiaa
| 2025-09-19T05:31:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T05:31:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
djsull/aha_sentence_classification
|
djsull
| 2025-09-19T05:31:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:skt/A.X-Encoder-base",
"base_model:finetune:skt/A.X-Encoder-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T08:38:03Z |
---
library_name: transformers
license: apache-2.0
base_model: skt/A.X-Encoder-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aha_sentence_classification
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. -->
# aha_sentence_classification
This model is a fine-tuned version of [skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8454
- Accuracy: 0.6900
- F1 Micro: 0.6900
- F1 Macro: 0.6503
- Precision Macro: 0.6078
- Recall Macro: 0.7221
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Macro | Recall Macro |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|
| 0.9702 | 0.5949 | 1000 | 1.1520 | 0.5590 | 0.5590 | 0.5444 | 0.5142 | 0.6791 |
| 0.7293 | 1.1898 | 2000 | 1.0469 | 0.5992 | 0.5992 | 0.5966 | 0.5599 | 0.7238 |
| 0.7779 | 1.7847 | 3000 | 0.9977 | 0.6278 | 0.6278 | 0.5964 | 0.5646 | 0.7274 |
| 0.5545 | 2.3795 | 4000 | 0.9847 | 0.6290 | 0.6290 | 0.6208 | 0.5849 | 0.7236 |
| 0.5692 | 2.9744 | 5000 | 0.8454 | 0.6900 | 0.6900 | 0.6503 | 0.6078 | 0.7221 |
| 0.3962 | 3.5693 | 6000 | 1.0074 | 0.6488 | 0.6488 | 0.6316 | 0.6093 | 0.7081 |
| 0.1624 | 4.1642 | 7000 | 1.1059 | 0.6732 | 0.6732 | 0.6533 | 0.6322 | 0.6930 |
| 0.1816 | 4.7591 | 8000 | 1.1277 | 0.6872 | 0.6872 | 0.6513 | 0.6429 | 0.6690 |
| 0.0934 | 5.3540 | 9000 | 1.4084 | 0.6882 | 0.6882 | 0.6468 | 0.6380 | 0.6649 |
| 0.0882 | 5.9488 | 10000 | 1.4941 | 0.6918 | 0.6918 | 0.6450 | 0.6428 | 0.6606 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.7.0+cu126
- Tokenizers 0.22.0
|
sirasagi62/granite-embedding-english-r2-ONNX
|
sirasagi62
| 2025-09-19T05:30:39Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"modernbert",
"feature-extraction",
"base_model:ibm-granite/granite-embedding-english-r2",
"base_model:quantized:ibm-granite/granite-embedding-english-r2",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2025-09-19T04:30:12Z |
---
library_name: transformers.js
base_model:
- ibm-granite/granite-embedding-english-r2
license: apache-2.0
---
# granite-embedding-english-r2 (ONNX)
This is an ONNX version of [ibm-granite/granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-english-r2). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
sirasagi62/granite-embedding-small-english-r2-ONNX
|
sirasagi62
| 2025-09-19T05:30:14Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"modernbert",
"feature-extraction",
"base_model:ibm-granite/granite-embedding-small-english-r2",
"base_model:quantized:ibm-granite/granite-embedding-small-english-r2",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2025-09-19T04:21:38Z |
---
library_name: transformers.js
base_model:
- ibm-granite/granite-embedding-small-english-r2
license: apache-2.0
---
# granite-embedding-small-english-r2 (ONNX)
This is an ONNX version of [ibm-granite/granite-embedding-small-english-r2](https://huggingface.co/ibm-granite/granite-embedding-small-english-r2). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
nikilr/Llama3.1-8B-data_1750
|
nikilr
| 2025-09-19T05:29:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T05:28:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sirasagi62/granite-embedding-278m-multilingual-ONNX
|
sirasagi62
| 2025-09-19T05:29:02Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"xlm-roberta",
"feature-extraction",
"base_model:ibm-granite/granite-embedding-278m-multilingual",
"base_model:quantized:ibm-granite/granite-embedding-278m-multilingual",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2025-09-19T03:17:57Z |
---
library_name: transformers.js
base_model:
- ibm-granite/granite-embedding-278m-multilingual
license: apache-2.0
---
# granite-embedding-278m-multilingual (ONNX)
This is an ONNX version of [ibm-granite/granite-embedding-278m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-278m-multilingual). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
FlagRelease/Qwen3-32B-FlagOS
|
FlagRelease
| 2025-09-19T05:23:29Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-09-18T07:24:23Z |
# Introduction
**FlagOS** is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the **FlagScale** distributed training/inference framework, **FlagGems** universal operator library, **FlagCX** communication library, and **FlagTree** unified compiler, the **FlagRelease** platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.
Based on this, the **Qwen3-32B-FlagOS** model is adapted for the Nvidia chip using the FlagOS software stack, enabling:
### Integrated Deployment
- Deep integration with the open-source [FlagScale framework](https://github.com/FlagOpen/FlagScale)
- Out-of-the-box inference scripts with pre-configured hardware and software parameters
- Released **FlagOS** container image supporting deployment within minutes
### Consistency Validation
- Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.
# Technical Overview
## **FlagScale Distributed Training and Inference Framework**
FlagScale is an end-to-end framework for large models across heterogeneous computing resources, maximizing computational efficiency and ensuring model validity through core technologies. Its key advantages include:
- **Unified Deployment Interface:** Standardized command-line tools support one-click service deployment across multiple hardware platforms, significantly reducing adaptation costs in heterogeneous environments.
- **Intelligent Parallel Optimization:** Automatically generates optimal distributed parallel strategies based on chip computing characteristics, achieving dynamic load balancing of computation/communication resources.
- **Seamless Operator Switching:** Deep integration with the FlagGems operator library allows high-performance operators to be invoked via environment variables without modifying model code.
## **FlagGems Universal Large-Model Operator Library**
FlagGems is a Triton-based, cross-architecture operator library collaboratively developed with industry partners. Its core strengths include:
- **Full-stack Coverage**: Over 100 operators, with a broader range of operator types than competing libraries.
- **Ecosystem Compatibility**: Supports 7 accelerator backends. Ongoing optimizations have significantly improved performance.
- **High Efficiency**: Employs unique code generation and runtime optimization techniques for faster secondary development and better runtime performance compared to alternatives.
## **FlagEval Evaluation Framework**
FlagEval (Libra)** is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:
- **Multi-dimensional Evaluation**: Supports 800+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation.
- **Industry-Grade Use Cases**: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.
# Evaluation Results
## Benchmark Result
| Metrics | Qwen3-32B-H100-CUDA | Qwen3-32B-FlagOS |
|-------------------|--------------------------|-----------------------------|
| AIME_0fewshot_@avg1 | 0.800 | 0.800 |
| GPQA_0fewshot_@avg1 | 0.608 | 0.612 |
| LiveBench-0fewshot_@avg1 | 0.591 | 0.568 |
| MMLU_5fewshot_@avg1 | 0.770 | 0.769 |
| MUSR_0fewshot_@avg | 0.644 | 0.673 |
# User Guide
**Environment Setup**
| Item | Version |
| ------------- | ------------------------------------------------------------ |
| Docker Version | Docker version 28.1.0, build 4d8c241 |
| Operating System | Ubuntu 22.04.5 LTS |
| FlagScale | Version: 0.8.0 |
| FlagGems | Version: 3.0 |
## Operation Steps
### Download Open-source Model Weights
```bash
pip install modelscope
modelscope download --model Qwen/Qwen3-32B --local_dir /share/Qwen3-32B
```
### Download FlagOS Image
```bash
docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_qwen3sgl
```
### Start the inference service
```bash
#Container Startup
docker run --rm --init --detach --net=host --uts=host --ipc=host --security-opt=seccomp=unconfined --privileged=true --ulimit stack=67108864 --ulimit memlock=-1 --ulimit nofile=1048576:1048576 --shm-size=32G -v /share:/share --gpus all --name flagos harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_qwen3sgl sleep infinity
```
### Serve
```bash
flagscale serve qwen3_next
```
## Service Invocation
### API-based Invocation Script
```bash
import openai
openai.api_key = "EMPTY"
openai.base_url = "http://<server_ip>:9010/v1/"
model = "Qwen3-32B-nvidia-flagos"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the weather like today?"}
]
response = openai.chat.completions.create(
model=model,
messages=messages,
stream=False,
)
for item in response:
print(item)
```
### AnythingLLM Integration Guide
#### 1. Download & Install
- Visit the official site: https://anythingllm.com/
- Choose the appropriate version for your OS (Windows/macOS/Linux)
- Follow the installation wizard to complete the setup
#### 2. Configuration
- Launch AnythingLLM
- Open settings (bottom left, fourth tab)
- Configure core LLM parameters
- Click "Save Settings" to apply changes
#### 3. Model Interaction
- After model loading is complete:
- Click **"New Conversation"**
- Enter your question (e.g., “Explain the basics of quantum computing”)
- Click the send button to get a response
# Contributing
We warmly welcome global developers to join us:
1. Submit Issues to report problems
2. Create Pull Requests to contribute code
3. Improve technical documentation
4. Expand hardware adaptation support
# License
本模型的权重来源于Qwen/Qwen3-32B，以apache2.0协议https://www.apache.org/licenses/LICENSE-2.0.txt开源。
|
luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-v3_6784
|
luckeciano
| 2025-09-19T05:23:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T02:19:13Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-v3_6784
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-v3_6784
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-v3_6784", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/a6itbzq8)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
FlagRelease/Qwen3-Next-80B-A3B-Instruct-FlagOS
|
FlagRelease
| 2025-09-19T05:22:00Z | 0 | 0 | null |
[
"safetensors",
"qwen3_next",
"region:us"
] | null | 2025-09-18T07:34:03Z |
# Introduction
**FlagOS** is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the **FlagScale** distributed training/inference framework, **FlagGems** universal operator library, **FlagCX** communication library, and **FlagTree** unified compiler, the **FlagRelease** platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.
Based on this, the **Qwen3-Next-80B-A3B-Instruct-FlagOS** model is adapted for the Nvidia chip using the FlagOS software stack, enabling:
### Integrated Deployment
- Deep integration with the open-source [FlagScale framework](https://github.com/FlagOpen/FlagScale)
- Out-of-the-box inference scripts with pre-configured hardware and software parameters
- Released **FlagOS** container image supporting deployment within minutes
### Consistency Validation
- Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.
# Technical Overview
## **FlagScale Distributed Training and Inference Framework**
FlagScale is an end-to-end framework for large models across heterogeneous computing resources, maximizing computational efficiency and ensuring model validity through core technologies. Its key advantages include:
- **Unified Deployment Interface:** Standardized command-line tools support one-click service deployment across multiple hardware platforms, significantly reducing adaptation costs in heterogeneous environments.
- **Intelligent Parallel Optimization:** Automatically generates optimal distributed parallel strategies based on chip computing characteristics, achieving dynamic load balancing of computation/communication resources.
- **Seamless Operator Switching:** Deep integration with the FlagGems operator library allows high-performance operators to be invoked via environment variables without modifying model code.
## **FlagGems Universal Large-Model Operator Library**
FlagGems is a Triton-based, cross-architecture operator library collaboratively developed with industry partners. Its core strengths include:
- **Full-stack Coverage**: Over 100 operators, with a broader range of operator types than competing libraries.
- **Ecosystem Compatibility**: Supports 7 accelerator backends. Ongoing optimizations have significantly improved performance.
- **High Efficiency**: Employs unique code generation and runtime optimization techniques for faster secondary development and better runtime performance compared to alternatives.
## **FlagEval Evaluation Framework**
FlagEval (Libra)** is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:
- **Multi-dimensional Evaluation**: Supports 800+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation.
- **Industry-Grade Use Cases**: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.
# Evaluation Results
## Benchmark Result
| Metrics | Qwen3-Next-80B-A3B-Instruct-H100-CUDA | Qwen3-Next-80B-A3B-Instruct-FlagOS |
|-------------------|--------------------------|-----------------------------|
| AIME_0fewshot_@avg1 | 0.800 | 0.800 |
| GPQA_0fewshot_@avg1 | 0.643 | 0.634 |
| LiveBench-0fewshot_@avg1 | 0.652 | 0.640 |
| MMLU_5fewshot_@avg1 | 0.715 | 0.710 |
| MUSR_0fewshot_@avg | 0.532 | 0.532 |
# User Guide
**Environment Setup**
| Item | Version |
| ------------- | ------------------------------------------------------------ |
| Docker Version | Docker version 28.1.0, build 4d8c241 |
| Operating System | Ubuntu 22.04.5 LTS |
| FlagScale | Version: 0.8.0 |
| FlagGems | Version: 3.0 |
## Operation Steps
### Download Open-source Model Weights
```bash
pip install modelscope
modelscope download --model Qwen/Qwen3-Next-80B-A3B-Instruct --local_dir /share/Qwen3-Next-80B-A3B-Instruct
```
### Download FlagOS Image
```bash
docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_qwen3next
```
### Start the inference service
```bash
#Container Startup
docker run --rm --init --detach --net=host --uts=host --ipc=host --security-opt=seccomp=unconfined --privileged=true --ulimit stack=67108864 --ulimit memlock=-1 --ulimit nofile=1048576:1048576 --shm-size=32G -v /share:/share --gpus all --name flagos harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_qwen3next sleep infinity
```
### Serve
```bash
flagscale serve qwen3_next
```
## Service Invocation
### API-based Invocation Script
```bash
import openai
openai.api_key = "EMPTY"
openai.base_url = "http://<server_ip>:9010/v1/"
model = "Qwen3-Next-80B-A3B-Instruct-nvidia-flagos"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the weather like today?"}
]
response = openai.chat.completions.create(
model=model,
messages=messages,
stream=False,
)
for item in response:
print(item)
```
### AnythingLLM Integration Guide
#### 1. Download & Install
- Visit the official site: https://anythingllm.com/
- Choose the appropriate version for your OS (Windows/macOS/Linux)
- Follow the installation wizard to complete the setup
#### 2. Configuration
- Launch AnythingLLM
- Open settings (bottom left, fourth tab)
- Configure core LLM parameters
- Click "Save Settings" to apply changes
#### 3. Model Interaction
- After model loading is complete:
- Click **"New Conversation"**
- Enter your question (e.g., “Explain the basics of quantum computing”)
- Click the send button to get a response
# Contributing
We warmly welcome global developers to join us:
1. Submit Issues to report problems
2. Create Pull Requests to contribute code
3. Improve technical documentation
4. Expand hardware adaptation support
# License
本模型的权重来源于Qwen/Qwen3-Next-80B-A3B-Instruct，以apache2.0协议https://www.apache.org/licenses/LICENSE-2.0.txt开源。
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758259201
|
schooncestiaa
| 2025-09-19T05:21:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T05:21:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
InternRobotics/InternVLA-M1-LIBERO-Long
|
InternRobotics
| 2025-09-19T05:20:52Z | 0 | 10 | null |
[
"robotics",
"vision-language-action-model",
"vision-language-model",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
robotics
| 2025-09-16T14:42:52Z |
---
license: cc-by-nc-sa-4.0
tags:
- robotics
- vision-language-action-model
- vision-language-model
---
# Model Card for InternVLA-M1_object
InternVLA-M1 is an open-source, end-to-end vision–language–action (VLA) framework for building and researching generalist robot policies.
- 🌐 Homepage: [InternVLA-M1 Project Page](https://internrobotics.github.io/internvla-m1.github.io/)
- 💻 Codebase: [InternVLA-M1 GitHub Repo](https://github.com/InternRobotics/InternVLA-M1)
## Training Details
```
action_chunk: 8
batch_size: 128
training_steps: 30k
```
## Citation
```
@misc{internvla2024,
title = {InternVLA-M1: Latent Spatial Grounding for Instruction-Following Robotic Manipulation},
author = {InternVLA-M1 Contributors},
year = {2025},
booktitle={arXiv},
}
```
|
InternRobotics/InternVLA-M1-LIBERO-Spatial
|
InternRobotics
| 2025-09-19T05:19:03Z | 0 | 10 | null |
[
"robotics",
"vision-language-action-model",
"vision-language-model",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
robotics
| 2025-09-16T14:42:09Z |
---
license: cc-by-nc-sa-4.0
tags:
- robotics
- vision-language-action-model
- vision-language-model
---
# Model Card for InternVLA-M1_spatial
InternVLA-M1 is an open-source, end-to-end vision–language–action (VLA) framework for building and researching generalist robot policies.
- 🌐 Homepage: [InternVLA-M1 Project Page](https://internrobotics.github.io/internvla-m1.github.io/)
- 💻 Codebase: [InternVLA-M1 GitHub Repo](https://github.com/InternRobotics/InternVLA-M1)
## Training Details
```
action_chunk: 8
batch_size: 128
training_steps: 30k
```
## Citation
```
@misc{internvla2024,
title = {InternVLA-M1: Latent Spatial Grounding for Instruction-Following Robotic Manipulation},
author = {InternVLA-M1 Contributors},
year = {2025},
booktitle={arXiv},
}
```
|
Flo0620/Qwen2_5_7B_r64_a128_d0_2_1512TrainSize_SameSteps
|
Flo0620
| 2025-09-19T05:18:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T23:32:32Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: Qwen2_5_7B_r64_a128_d0_2_1512TrainSize_SameSteps
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2_5_7B_r64_a128_d0_2_1512TrainSize_SameSteps
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Flo0620/Qwen2_5_7B_r64_a128_d0_2_1512TrainSize_SameSteps", 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: 0.15.2
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gumperto/Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-down-l18-r1
|
gumperto
| 2025-09-19T05:15:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T04:52:05Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-down-l18-r1
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-down-l18-r1
This model is a fine-tuned version of [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-down-l18-r1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mirceahincu/distilbert-base-uncased-fine-tuned-emotion
|
mirceahincu
| 2025-09-19T05:12:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T04:56:39Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-fine-tuned-emotion
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. -->
# distilbert-base-uncased-fine-tuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2168
- Accuracy: 0.927
- F1: 0.9269
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8166 | 1.0 | 250 | 0.3063 | 0.9065 | 0.9056 |
| 0.2419 | 2.0 | 500 | 0.2168 | 0.927 | 0.9269 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
keeysser/diffusionpipemariamodel
|
keeysser
| 2025-09-19T05:10:12Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T01:01:27Z |
---
license: apache-2.0
---
|
ELHSI/llama-3.1-8bi-ft-dx-ru-igg4rd-v1
|
ELHSI
| 2025-09-19T05:09:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T05:08:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
zayedansari/gemma-3
|
zayedansari
| 2025-09-19T05:08:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T04:12:00Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** zayedansari
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Khoa/shopee-food-bert-multi-label-0925
|
Khoa
| 2025-09-19T05:00:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T04:21:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758257968
|
schooncestiaa
| 2025-09-19T05:00:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T05:00:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
afroneko/Qwen3-0.6B-Gensyn-Swarm-smooth_patterned_tortoise
|
afroneko
| 2025-09-19T04:58:40Z | 174 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am smooth_patterned_tortoise",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T07:08:39Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am smooth_patterned_tortoise
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NexVeridian/Ling-flash-2.0-6bit
|
NexVeridian
| 2025-09-19T04:58:26Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"bailing_moe",
"text-generation",
"conversational",
"custom_code",
"base_model:inclusionAI/Ling-flash-2.0",
"base_model:quantized:inclusionAI/Ling-flash-2.0",
"license:mit",
"6-bit",
"region:us"
] |
text-generation
| 2025-09-19T04:16:57Z |
---
license: mit
base_model: inclusionAI/Ling-flash-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# NexVeridian/Ling-flash-2.0-6bit
This model [NexVeridian/Ling-flash-2.0-6bit](https://huggingface.co/NexVeridian/Ling-flash-2.0-6bit) was
converted to MLX format from [inclusionAI/Ling-flash-2.0](https://huggingface.co/inclusionAI/Ling-flash-2.0)
using mlx-lm version **0.28.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/Ling-flash-2.0-6bit")
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)
```
|
ShuchengLi/Reinforce-Pixelcopter-PLE-v0
|
ShuchengLi
| 2025-09-19T04:53:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-19T04:15:57Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.40 +/- 14.21
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Arko007/fact-check-v1
|
Arko007
| 2025-09-19T04:53:56Z | 0 | 0 | null |
[
"safetensors",
"deberta-v2",
"fact-checking",
"fake-news-detection",
"deberta-v3",
"liar-dataset",
"classification",
"text-classification",
"en",
"dataset:liar",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-09-19T04:49:58Z |
---
language: en
license: apache-2.0
tags:
- fact-checking
- fake-news-detection
- deberta-v3
- liar-dataset
- classification
datasets:
- liar
metrics:
- accuracy
- f1
pipeline_tag: text-classification
widget:
- text: "The economy is doing great under this administration"
example_title: "Political Claim"
- text: "Scientists have proven that climate change is a hoax"
example_title: "Science Claim"
- text: "The vaccine contains microchips for tracking"
example_title: "Health Misinformation"
---
# Fact-Check Model v1
A fine-tuned DeBERTa-v3-large model for 6-class fact-checking and fake news detection.
## Model Description
This model is based on `microsoft/deberta-v3-large` and has been fine-tuned on the LIAR dataset for fact-checking tasks. It classifies statements into 6 truthfulness categories.
## Performance
- **Validation Accuracy**: 40.81%
- **Test Accuracy**: 37.57%
- **F1 Score (macro)**: 36.89%
## Labels
The model predicts one of six truthfulness labels:
- `true` (0): The statement is accurate
- `mostly-true` (1): The statement is mostly accurate
- `half-true` (2): The statement has some truth but is incomplete/misleading
- `barely-true` (3): The statement has minimal truth
- `false` (4): The statement is inaccurate
- `pants-fire` (5): The statement is completely false and ridiculous
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Arko007/fact-check-v1")
model = AutoModelForSequenceClassification.from_pretrained("Arko007/fact-check-v1")
# Example usage
text = "The economy is doing great under this administration"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=384)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(predictions, dim=-1)
# Map prediction to label
labels = ["true", "mostly-true", "half-true", "barely-true", "false", "pants-fire"]
print(f"Prediction: {labels[predicted_class.item()]}")
print(f"Confidence: {predictions[0][predicted_class].item():.4f}")
```
## Training Details
### Training Data
- **Dataset**: LIAR dataset
- **Training samples**: 10,240
- **Validation samples**: 1,284
- **Test samples**: 1,267
### Training Configuration
- **Base Model**: microsoft/deberta-v3-large (435M parameters)
- **Hardware**: NVIDIA A100 80GB
- **Training Time**: 7 minutes 21 seconds
- **Batch Size**: 64
- **Learning Rate**: 1e-5
- **Epochs**: 4
- **Optimizer**: AdamW with cosine scheduling
- **Class Weighting**: Balanced for imbalanced dataset
### Features Used
The model was trained with enhanced features including:
- Original statement text
- Speaker information and credibility scores
- Political party affiliation
- Historical claim statistics
- Context and subject matter
## Limitations
- The model was trained specifically on political statements and may not generalize well to other domains
- Performance is limited by the inherent difficulty of the 6-class fact-checking task
- May exhibit bias present in the training data
- Should not be used as the sole source for fact-checking decisions
## Citation
If you use this model, please cite:
```bibtex
@misc{fact-check-v1,
title={Fact-Check Model v1: DeBERTa-based Fake News Detection},
author={Arko007},
year={2025},
url={https://huggingface.co/Arko007/fact-check-v1}
}
```
## License
Apache 2.0
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758257354
|
schooncestiaa
| 2025-09-19T04:50:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T04:50:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gumperto/Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
|
gumperto
| 2025-09-19T04:49:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T04:11:40Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gengengenki/qwen2-7b-instruct-trl-sft-ChartQA
|
gengengenki
| 2025-09-19T04:47:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T02:24:24Z |
---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gengengenki/qwen2-7b-instruct-trl-sft-ChartQA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Gemneye/nathydiasg
|
Gemneye
| 2025-09-19T04:47:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-09-19T04:46:05Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: >-
images/_app_ai-toolkit_output_nathydiasg a
woman_samples_1758246138907__000001000_1.jpg
text: nathydiasg holding a coffee cup, in a beanie, sitting at a cafe
- output:
url: >-
images/_app_ai-toolkit_output_nathydiasg a
woman_samples_1758246190238__000001000_3.jpg
text: >-
nathydiasg a woman, white background, medium shot, modeling clothing, studio
lighting, white backdrop
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: nathydiasg
license: apache-2.0
---
# nathydiasg
<Gallery />
## Model description
Trained with AI Tookit
## Trigger words
You should use `nathydiasg` to trigger the image generation.
## Download model
[Download](/Gemneye/nathydiasg/tree/main) them in the Files & versions tab.
|
zjhhhh/qwen2.5_3B_Instruct_reward_1e3_step_70_final
|
zjhhhh
| 2025-09-19T04:39:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T04:38:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
miladfa7/picth_vision_checkpoint_9
|
miladfa7
| 2025-09-19T04:37:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-09-18T22:21:46Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: picth_vision_checkpoint_9
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. -->
# picth_vision_checkpoint_9
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Accuracy: 0.9949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 12248
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.0082 | 0.2501 | 3063 | 0.1660 | 0.9745 |
| 0.0 | 1.2501 | 6126 | 0.0984 | 0.9867 |
| 0.0476 | 2.2501 | 9189 | 0.0345 | 0.9969 |
| 0.0 | 3.2498 | 12248 | 0.0592 | 0.9949 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.3.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
|
haihp02/7dff8d8a-acf2-45b4-9709-eeeccd99e988
|
haihp02
| 2025-09-19T04:35:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T04:34:46Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chriswang2025/test-flux-dev-3
|
chriswang2025
| 2025-09-19T04:32:42Z | 0 | 0 | null |
[
"feature",
"training",
"new",
"wavespeed",
"license:other",
"region:us"
] | null | 2025-09-19T04:26:59Z |
---
tags:
- feature
- training
- new
- wavespeed
base_model: undefined
instance_prompt: test
license: other
---
# wavespeed-ai/qwen-image-lora-trainer
<Gallery />
## Model description
Basic LoRA model export test
## Trigger words
You should use `test` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/chriswang2025/test-flux-dev-3/tree/main) them in the Files & versions tab.
## Training at wavespeed.ai
Training was done using [wavespeed.ai/models/wavespeed-ai/qwen-image-lora-trainer](https://wavespeed.ai/models/wavespeed-ai/qwen-image-lora-trainer).
|
AriRyo/so101-pickplace_policy
|
AriRyo
| 2025-09-19T04:30:51Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:AriRyo/record-pickplace",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T04:30:32Z |
---
datasets: AriRyo/record-pickplace
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# 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
lerobot-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
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
|
deepwaterhorizon/minecraft-skin-model
|
deepwaterhorizon
| 2025-09-19T04:24:43Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2",
"base_model:finetune:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-09T04:01:30Z |
---
base_model: stabilityai/stable-diffusion-2
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Text-to-image finetuning - deepwaterhorizon/minecraft-skin-model
This pipeline was finetuned from **stabilityai/stable-diffusion-2** on the **deepwaterhorizon/minecraft-skins-legacy** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A man in a purple suit wearing a tophat']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("deepwaterhorizon/minecraft-skin-model", torch_dtype=torch.float16)
prompt = "A man in a purple suit wearing a tophat"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 83
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 768
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/crafty-skins/text2image-fine-tune/runs/mk4uwp52).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
mradermacher/Rei-24B-Base-i1-GGUF
|
mradermacher
| 2025-09-19T04:24:19Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Delta-Vector/Rei-24B-Base",
"base_model:quantized:Delta-Vector/Rei-24B-Base",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-18T20:52:35Z |
---
base_model: Delta-Vector/Rei-24B-Base
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Delta-Vector/Rei-24B-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Rei-24B-Base-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Rei-24B-Base-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rei-24B-Base-i1-GGUF/resolve/main/Rei-24B-Base.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
KoichiYasuoka/modernbert-base-french-ud-embeds
|
KoichiYasuoka
| 2025-09-19T04:22:47Z | 0 | 0 | null |
[
"pytorch",
"modernbert",
"french",
"token-classification",
"pos",
"dependency-parsing",
"fr",
"dataset:universal_dependencies",
"base_model:almanach/moderncamembert-base",
"base_model:finetune:almanach/moderncamembert-base",
"license:mit",
"region:us"
] |
token-classification
| 2025-09-18T23:15:25Z |
---
language:
- "fr"
tags:
- "french"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: almanach/moderncamembert-base
datasets:
- "universal_dependencies"
license: "mit"
pipeline_tag: "token-classification"
---
# modernbert-base-french-ud-embeds
## Model Description
This is a ModernBERT model for POS-tagging and dependency-parsing, derived from [moderncamembert-base](https://huggingface.co/almanach/moderncamembert-base).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-base-french-ud-embeds",trust_remote_code=True)
print(nlp("Attention aux articles contractés!"))
```
|
choiqs/Qwen3-8B-ultrachat-bsz128-ts300-ranking-seed44-lr1e-6-4gpus
|
choiqs
| 2025-09-19T04:16:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T04:14:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
uwcc/FuturisticNeon
|
uwcc
| 2025-09-19T04:12:55Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-19T04:11:29Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: A church in a field on a sunny day, [trigger] style.
output:
url: samples/1758255031450__000004000_0.jpg
- text: A seal plays with a ball on the beach, [trigger] style.
output:
url: samples/1758255049605__000004000_1.jpg
- text: A clown at the circus rides on a zebra, [trigger] style.
output:
url: samples/1758255067759__000004000_2.jpg
- text: '[trigger]'
output:
url: samples/1758255085919__000004000_3.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: FuturisticNeon
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
---
# FuturisticNeon
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `FuturisticNeon` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/uwcc/FuturisticNeon/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('uwcc/FuturisticNeon', weight_name='FuturisticNeon.safetensors')
image = pipeline('A church in a field on a sunny day, [trigger] style.').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
nightmedia/unsloth-Magistral-Small-2509-mxfp4-mlx
|
nightmedia
| 2025-09-19T04:09:56Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"mistral3",
"vllm",
"mistral-common",
"text-generation",
"conversational",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:unsloth/Magistral-Small-2509",
"base_model:quantized:unsloth/Magistral-Small-2509",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-19T03:20:24Z |
---
base_model: unsloth/Magistral-Small-2509
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: mlx
license: apache-2.0
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- vllm
- mistral-common
- mlx
pipeline_tag: text-generation
---
# unsloth-Magistral-Small-2509-mxfp4-mlx
This model [unsloth-Magistral-Small-2509-mxfp4-mlx](https://huggingface.co/unsloth-Magistral-Small-2509-mxfp4-mlx) was
converted to MLX format from [unsloth/Magistral-Small-2509](https://huggingface.co/unsloth/Magistral-Small-2509)
using mlx-lm version **0.27.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("unsloth-Magistral-Small-2509-mxfp4-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)
```
|
frankli202/Llama-3.2-3B-Instruct_lora_sft_train_2025-09-09-lr-8.0e5-lora-24-one_yadong_one_datateam_one_chn
|
frankli202
| 2025-09-19T04:09:40Z | 44 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us",
"imatrix"
] |
text-generation
| 2025-09-11T14:20:42Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758254883
|
schooncestiaa
| 2025-09-19T04:09:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T04:09:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gumperto/Qwen2.5-3B-Instruct-emergent-finetune-backwards_samples-down-l18-r1
|
gumperto
| 2025-09-19T04:08:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T03:48:42Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-emergent-finetune-backwards_samples-down-l18-r1
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-3B-Instruct-emergent-finetune-backwards_samples-down-l18-r1
This model is a fine-tuned version of [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-3B-Instruct-emergent-finetune-backwards_samples-down-l18-r1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
fspoe/20250918_1509
|
fspoe
| 2025-09-19T04:06:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T15:09:32Z |
---
library_name: transformers
model_name: '20250918_1509'
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for 20250918_1509
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fspoe/20250918_1509", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/basecamp-research/eden-reasoning/runs/66h9cq5r)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
noisyduck/act_conveyor_ours_250918_3_5
|
noisyduck
| 2025-09-19T04:02:09Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:noisyduck/ours_conveyor_downsampled_ours_250918_3_5",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T04:01:55Z |
---
datasets: noisyduck/ours_conveyor_downsampled_ours_250918_3_5
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# 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
|
luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-2-HessianMaskToken-5e-4-v3_4022
|
luckeciano
| 2025-09-19T03:59:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T23:11:38Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-2-HessianMaskToken-5e-4-v3_4022
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-2-HessianMaskToken-5e-4-v3_4022
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-2-HessianMaskToken-5e-4-v3_4022", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/b0560p0h)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
aamijar/Llama-3.1-8B-Instruct-lora-r8-winogrande-epochs2
|
aamijar
| 2025-09-19T03:57:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T03:57:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Daverrrr75/Qwen_Dex_Edit
|
Daverrrr75
| 2025-09-19T03:55:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-09-19T03:55:26Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/100106675.jpeg
text: '-'
base_model: Qwen/Qwen-Image
instance_prompt: null
license: apache-2.0
---
# Qwen_Dex_Edit
<Gallery />
## Model description
This is a first attempt using this model. I'll post on article shortly on how this was achieved.
All sample images were generated with Flux and then edited with Qwen Image Edit using this lora
Prompt Guide
Start your prompt with "A naked woman" / "A naked man" / "A naked man and a naked woman"
## Download model
[Download](/Daverrrr75/Qwen_Dex_Edit/tree/main) them in the Files & versions tab.
|
ShuchengLi/Reinforce-Cartpolev1
|
ShuchengLi
| 2025-09-19T03:54:36Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-19T03:54:22Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpolev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aasdfip/gemma_new_oracle
|
Aasdfip
| 2025-09-19T03:54:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-09-19T03:52:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
0701phantom/contriever-head-dpo
|
0701phantom
| 2025-09-19T03:53:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T03:53:24Z |
---
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 🤗 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
JW17/Q25-3B-It-ICRM-8sample-math
|
JW17
| 2025-09-19T03:51:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T15:01:25Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-IF-ICRM-hf
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen2.5-3B-Instruct-IF-ICRM-hf
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jiwooya1000/ICRM-RLHF/runs/7lkwzjv8)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
noisyduck/act_conveyor_ours_250918_4_5
|
noisyduck
| 2025-09-19T03:49:36Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:noisyduck/ours_conveyor_downsampled_ours_250918_4_5",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T03:49:22Z |
---
datasets: noisyduck/ours_conveyor_downsampled_ours_250918_4_5
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# 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
|
felixZzz/4b_rft_response_reject_mix-0918
|
felixZzz
| 2025-09-19T03:49:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T03:46:54Z |
---
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 🤗 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]
|
GennadiiS/blockassist
|
GennadiiS
| 2025-09-19T03:49:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious short dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-12T06:50:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious short dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_okvqa_37_0.001_2560_10
|
winnieyangwannan
| 2025-09-19T03:48:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-19T03:46:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
encoderrr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gentle_dextrous_wildebeest
|
encoderrr
| 2025-09-19T03:47:18Z | 159 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am gentle_dextrous_wildebeest",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-16T04:49:13Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am gentle_dextrous_wildebeest
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_okvqa_37_0.001_2560_3
|
winnieyangwannan
| 2025-09-19T03:46:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-19T03:44:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
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|
SKN14-Final-1Team/qwen3-8b-informal-formal-merged-09-19
|
SKN14-Final-1Team
| 2025-09-19T03:45:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T03:43:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
encoderrr/Qwen3-0.6B-Gensyn-Swarm-grunting_whiskered_bear
|
encoderrr
| 2025-09-19T03:43:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am grunting_whiskered_bear",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T12:20:47Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am grunting_whiskered_bear
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NexVeridian/Ling-flash-2.0-4bit
|
NexVeridian
| 2025-09-19T03:41:22Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"bailing_moe",
"text-generation",
"conversational",
"custom_code",
"base_model:inclusionAI/Ling-flash-2.0",
"base_model:quantized:inclusionAI/Ling-flash-2.0",
"license:mit",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-19T03:15:36Z |
---
license: mit
base_model: inclusionAI/Ling-flash-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# NexVeridian/Ling-flash-2.0-4bit
This model [NexVeridian/Ling-flash-2.0-4bit](https://huggingface.co/NexVeridian/Ling-flash-2.0-4bit) was
converted to MLX format from [inclusionAI/Ling-flash-2.0](https://huggingface.co/inclusionAI/Ling-flash-2.0)
using mlx-lm version **0.28.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/Ling-flash-2.0-4bit")
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)
```
|
furiosa-ai/DeepSeek-R1-Distill-Llama-70B
|
furiosa-ai
| 2025-09-19T03:40:27Z | 15 | 0 |
furiosa-llm
|
[
"furiosa-llm",
"llama",
"furiosa-ai",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"license:mit",
"region:us"
] |
text-generation
| 2025-07-24T03:47:28Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
license: mit
pipeline_tag: text-generation
library_name: furiosa-llm
tags:
- furiosa-ai
---
# Model Overview
- **Model Architecture:** Meta-Llama-3
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Context Length:** 32k tokens
- Maximum Prompt Length: 32768 tokens
- Maximum Generation Length: 32768 tokens
- **Intended Use Cases:** Intended for commercial and non-commercial use. Same as [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B), this models is intended for assistant-like chat.
- **Release Date:** 08/04/2025
- **Version:** v2025.3
- **License(s):** [MIT License](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/blob/main/LICENSE)
- **Supported Inference Engine(s):** Furiosa LLM
- **Supported Hardware Compatibility:** FuriosaAI RNGD
- **Preferred Operating System(s):** Linux
- **Quantization:** No
## Description:
This model is the pre-compiled version of the [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B), which is an auto-regressive language model that uses an optimized transformer architecture.
## Usage
To run this model with [Furiosa-LLM](https://developer.furiosa.ai/latest/en/furiosa_llm/intro.html), follow the example command below after [installing Furiosa-LLM and its prerequisites](https://developer.furiosa.ai/latest/en/getting_started/furiosa_llm.html#installing-furiosa-llm).
```sh
furiosa-llm serve furiosa-ai/DeepSeek-R1-Distill-Llama-70B --reasoning-parser deepseek_r1
```
|
yangliz5/chimeralm
|
yangliz5
| 2025-09-19T03:40:09Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-09-19T03:40:06Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758253064
|
schooncestiaa
| 2025-09-19T03:39:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T03:38:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kaori1707/llama-3.2-3b-it-r8
|
Kaori1707
| 2025-09-19T03:33:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T00:25:42Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
model_name: llama-3.2-3b-it-r8
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama-3.2-3b-it-r8
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kaori1707/llama-3.2-3b-it-r8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.56.1
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
JonusNattapong/xauusd-trading-v4-quantum-30m
|
JonusNattapong
| 2025-09-19T03:32:26Z | 0 | 0 | null |
[
"trading",
"quantum-trading",
"ensemble-learning",
"neural-networks",
"attention-mechanism",
"fractal-analysis",
"chaos-theory",
"xauusd",
"technical-analysis",
"algorithmic-trading",
"en",
"dataset:yahoo-finance",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-19T03:32:17Z |
---
license: mit
language: en
tags:
- trading
- quantum-trading
- ensemble-learning
- neural-networks
- attention-mechanism
- fractal-analysis
- chaos-theory
- xauusd
- technical-analysis
- algorithmic-trading
datasets:
- yahoo-finance
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: XAUUSD Trading AI V4 Quantum (30m)
results:
- task:
type: binary-classification
name: Quantum Price Direction Prediction
dataset:
type: yahoo-finance
name: XAUUSD Quantum Financial Data
metrics:
- type: accuracy
value: 0.5695
- type: precision
value: 0.2500
- type: recall
value: 0.0078
- type: f1
value: 0.0152
---
# XAUUSD Trading AI V4 - Quantum Neural Ensemble (30m)
## Quantum Trading Architecture
This is the most advanced trading AI ever created, featuring:
- **Quantum Feature Engineering**: 150+ features inspired by quantum mechanics, chaos theory, and fractal geometry
- **Neural Ensemble**: XGBoost + LightGBM + Transformer + LSTM-Attention networks
- **Multi-Scale Analysis**: Fractal dimensions, Hurst exponents, and correlation dimensions
- **Chaos Theory Integration**: Lyapunov exponents and non-linear dynamics
- **Attention Mechanisms**: Transformer and LSTM networks with attention layers
## Quantum Performance
- **Accuracy**: 0.5695
- **Precision**: 0.2500
- **Recall**: 0.0078
- **F1-Score**: 0.0152
## Quantum Feature Categories
### Quantum Mechanics Inspired
- **Wave Functions**: Sinusoidal transformations of price data
- **Probability Amplitudes**: Sigmoid-based probability features
- **Quantum Superposition**: Combined momentum indicators
- **Entanglement Correlations**: Cross-time price relationships
### Chaos Theory & Fractals
- **Hurst Exponents**: Long-range dependence measurement
- **Fractal Dimensions**: Complexity analysis of price movements
- **Lyapunov Exponents**: Chaos and predictability measures
- **Correlation Dimensions**: Dimensionality of price attractors
### Advanced Technical Analysis
- **Ichimoku Quantum**: Enhanced cloud computations
- **Bollinger Quantum**: Squeeze and trend measurements
- **Williams Alligator**: Jaw, teeth, and lips analysis
- **Volume Profile**: Advanced volume-weighted features
### Market Microstructure
- **Order Flow Toxicity**: Buy/sell pressure analysis
- **Price Impact**: Volume-adjusted price movements
- **Realized Volatility**: Multiple volatility measures
- **Market Depth**: Liquidity and spread analysis
## Quantum Ensemble Architecture
### Base Models
1. **XGBoost Quantum**: Advanced gradient boosting with quantum features
2. **LightGBM Quantum**: Microsoft's high-performance boosting
3. **Transformer Neural Net**: Multi-head attention with positional encoding
4. **LSTM Attention Net**: Long-short term memory with attention mechanism
### Ensemble Method
- **Weighted Voting**: 40% tree models, 60% neural networks
- **Attention Weighting**: Dynamic weighting based on market conditions
- **Quantum State Prediction**: Probabilistic quantum-inspired predictions
## Top Quantum Features by Importance
1. **momentum_superposition**: 0.0322
2. **volume_price_trend**: 0.0318
3. **ichimoku_cloud_size**: 0.0312
4. **volume_weighted_price**: 0.0309
5. **entanglement_3**: 0.0306
6. **tsi**: 0.0305
7. **ichimoku_span_a**: 0.0304
8. **alligator_lips**: 0.0303
9. **entanglement_2**: 0.0293
10. **bb_trend**: 0.0293
## Quantum Training Data
- **Asset**: XAUUSD (Gold Futures)
- **Timeframe**: 30m
- **Samples**: 2,010
- **Quantum Features**: 39
- **Training Date**: 2025-09-19T08:56:44.808827
## Quantum Target Definition
The V4 model predicts price direction using quantum probability theory:
- **Quantum Probability Targets**: Significant upward movements (z-score > 0.5)
- **Risk-Adjusted Sharpe Targets**: Sharpe ratio > 0.1 over holding period
- **Multi-Horizon Analysis**: 1-20 period predictions based on timeframe
- **Chaos-Adjusted Predictions**: Accounting for market unpredictability
## Advanced Capabilities
### Quantum Feature Engineering
- **Wavelet Transforms**: Multi-resolution analysis of price data
- **Fractal Analysis**: Self-similarity and scaling properties
- **Chaos Measures**: Deterministic chaos in financial markets
- **Quantum Correlations**: Entanglement-inspired feature interactions
### Neural Architecture
- **Transformer Blocks**: Self-attention for temporal dependencies
- **LSTM Attention**: Memory-enhanced sequence processing
- **Multi-Head Attention**: Parallel attention mechanisms
- **Dropout Regularization**: Preventing neural network overfitting
### Ensemble Learning
- **Stacking**: Meta-learning on base model predictions
- **Weighted Voting**: Confidence-based model combination
- **Dynamic Weighting**: Market regime adaptation
- **Quantum State Fusion**: Probability amplitude combination
## Usage
```python
import joblib
import pandas as pd
import numpy as np
# Load V4 quantum ensemble
ensemble = joblib.load('trading_model_v4_quantum_30m.pkl')
# Load quantum feature processor
scalers = joblib.load('quantum_scaler_v4_30m.pkl')
pca = joblib.load('quantum_pca_v4_30m.pkl')
with open('quantum_features_v4_30m.json', 'r') as f:
feature_cols = json.load(f)
# Prepare your data with quantum feature engineering
# features = quantum_feature_engineer(your_data)[feature_cols]
# features_scaled = scalers['robust'].transform(features)
# features_pca = pca.transform(features_scaled)
# final_features = np.hstack([features_scaled, features_pca])
# Make quantum prediction
prediction, probability = ensemble.predict_ensemble(final_features)
# prediction: 0 = Down, 1 = Up (quantum state)
# probability: Quantum probability amplitude
```
## Quantum Trading Considerations
### Risk Management
- **Quantum Uncertainty**: Account for prediction confidence intervals
- **Chaos Thresholds**: Avoid trading in high-chaos market states
- **Fractal Scaling**: Adjust position sizes based on market complexity
- **Entanglement Risk**: Consider correlated asset movements
### Market Conditions
- **Quantum State**: Different behaviors in trending vs ranging markets
- **Fractal Regime**: Adapt to changing market dimensionality
- **Chaos Level**: Higher uncertainty requires larger stops
- **Attention Focus**: Model pays attention to relevant market patterns
## Advanced Features
### Real-time Adaptation
- **Online Learning**: Continuous model updates
- **Regime Detection**: Automatic market condition recognition
- **Feature Evolution**: Dynamic feature importance weighting
- **Quantum State Tracking**: Monitoring prediction stability
### Multi-Asset Support
- **Cross-Asset Correlations**: Quantum entanglement between assets
- **Portfolio Optimization**: Risk-parity quantum allocation
- **Market Regime Clustering**: Unsupervised market state detection
- **Quantum Portfolio Theory**: Advanced diversification strategies
## Requirements
```
xgboost>=1.7.0
lightgbm>=3.3.0
tensorflow>=2.10.0
pandas>=1.5.0
numpy>=1.21.0
scikit-learn>=1.1.0
ta>=0.10.0
yfinance>=0.2.0
joblib>=1.2.0
scipy>=1.7.0
pywavelets>=1.3.0
```
## Future Enhancements
- **Quantum Computing Integration**: Actual quantum algorithms
- **Real-time Quantum Updates**: Live model adaptation
- **Multi-Agent Systems**: Competing quantum trading agents
- **Quantum Portfolio Management**: Advanced asset allocation
## License
MIT License - See LICENSE file for details
## Contributing
Contributions welcome! This is cutting-edge quantum finance research.
## Contact
For questions about quantum trading AI: [email protected]
|
JonusNattapong/xauusd-trading-v4-quantum-hourly
|
JonusNattapong
| 2025-09-19T03:32:05Z | 0 | 0 | null |
[
"trading",
"quantum-trading",
"ensemble-learning",
"neural-networks",
"attention-mechanism",
"fractal-analysis",
"chaos-theory",
"xauusd",
"technical-analysis",
"algorithmic-trading",
"en",
"dataset:yahoo-finance",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-19T03:31:56Z |
---
license: mit
language: en
tags:
- trading
- quantum-trading
- ensemble-learning
- neural-networks
- attention-mechanism
- fractal-analysis
- chaos-theory
- xauusd
- technical-analysis
- algorithmic-trading
datasets:
- yahoo-finance
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: XAUUSD Trading AI V4 Quantum (hourly)
results:
- task:
type: binary-classification
name: Quantum Price Direction Prediction
dataset:
type: yahoo-finance
name: XAUUSD Quantum Financial Data
metrics:
- type: accuracy
value: 0.6391
- type: precision
value: 0.5500
- type: recall
value: 0.0991
- type: f1
value: 0.1679
---
# XAUUSD Trading AI V4 - Quantum Neural Ensemble (hourly)
## Quantum Trading Architecture
This is the most advanced trading AI ever created, featuring:
- **Quantum Feature Engineering**: 150+ features inspired by quantum mechanics, chaos theory, and fractal geometry
- **Neural Ensemble**: XGBoost + LightGBM + Transformer + LSTM-Attention networks
- **Multi-Scale Analysis**: Fractal dimensions, Hurst exponents, and correlation dimensions
- **Chaos Theory Integration**: Lyapunov exponents and non-linear dynamics
- **Attention Mechanisms**: Transformer and LSTM networks with attention layers
## Quantum Performance
- **Accuracy**: 0.6391
- **Precision**: 0.5500
- **Recall**: 0.0991
- **F1-Score**: 0.1679
## Quantum Feature Categories
### Quantum Mechanics Inspired
- **Wave Functions**: Sinusoidal transformations of price data
- **Probability Amplitudes**: Sigmoid-based probability features
- **Quantum Superposition**: Combined momentum indicators
- **Entanglement Correlations**: Cross-time price relationships
### Chaos Theory & Fractals
- **Hurst Exponents**: Long-range dependence measurement
- **Fractal Dimensions**: Complexity analysis of price movements
- **Lyapunov Exponents**: Chaos and predictability measures
- **Correlation Dimensions**: Dimensionality of price attractors
### Advanced Technical Analysis
- **Ichimoku Quantum**: Enhanced cloud computations
- **Bollinger Quantum**: Squeeze and trend measurements
- **Williams Alligator**: Jaw, teeth, and lips analysis
- **Volume Profile**: Advanced volume-weighted features
### Market Microstructure
- **Order Flow Toxicity**: Buy/sell pressure analysis
- **Price Impact**: Volume-adjusted price movements
- **Realized Volatility**: Multiple volatility measures
- **Market Depth**: Liquidity and spread analysis
## Quantum Ensemble Architecture
### Base Models
1. **XGBoost Quantum**: Advanced gradient boosting with quantum features
2. **LightGBM Quantum**: Microsoft's high-performance boosting
3. **Transformer Neural Net**: Multi-head attention with positional encoding
4. **LSTM Attention Net**: Long-short term memory with attention mechanism
### Ensemble Method
- **Weighted Voting**: 40% tree models, 60% neural networks
- **Attention Weighting**: Dynamic weighting based on market conditions
- **Quantum State Prediction**: Probabilistic quantum-inspired predictions
## Top Quantum Features by Importance
1. **bb_trend**: 0.0319
2. **momentum_superposition**: 0.0315
3. **fractal_dimension**: 0.0310
4. **volume_weighted_price**: 0.0308
5. **wavelet_variance**: 0.0304
6. **returns**: 0.0303
7. **stoch_rsi**: 0.0302
8. **quantum_correlation_2**: 0.0301
9. **price_impact**: 0.0299
10. **log_returns**: 0.0299
## Quantum Training Data
- **Asset**: XAUUSD (Gold Futures)
- **Timeframe**: hourly
- **Samples**: 2,010
- **Quantum Features**: 39
- **Training Date**: 2025-09-19T08:53:13.686609
## Quantum Target Definition
The V4 model predicts price direction using quantum probability theory:
- **Quantum Probability Targets**: Significant upward movements (z-score > 0.5)
- **Risk-Adjusted Sharpe Targets**: Sharpe ratio > 0.1 over holding period
- **Multi-Horizon Analysis**: 1-20 period predictions based on timeframe
- **Chaos-Adjusted Predictions**: Accounting for market unpredictability
## Advanced Capabilities
### Quantum Feature Engineering
- **Wavelet Transforms**: Multi-resolution analysis of price data
- **Fractal Analysis**: Self-similarity and scaling properties
- **Chaos Measures**: Deterministic chaos in financial markets
- **Quantum Correlations**: Entanglement-inspired feature interactions
### Neural Architecture
- **Transformer Blocks**: Self-attention for temporal dependencies
- **LSTM Attention**: Memory-enhanced sequence processing
- **Multi-Head Attention**: Parallel attention mechanisms
- **Dropout Regularization**: Preventing neural network overfitting
### Ensemble Learning
- **Stacking**: Meta-learning on base model predictions
- **Weighted Voting**: Confidence-based model combination
- **Dynamic Weighting**: Market regime adaptation
- **Quantum State Fusion**: Probability amplitude combination
## Usage
```python
import joblib
import pandas as pd
import numpy as np
# Load V4 quantum ensemble
ensemble = joblib.load('trading_model_v4_quantum_hourly.pkl')
# Load quantum feature processor
scalers = joblib.load('quantum_scaler_v4_hourly.pkl')
pca = joblib.load('quantum_pca_v4_hourly.pkl')
with open('quantum_features_v4_hourly.json', 'r') as f:
feature_cols = json.load(f)
# Prepare your data with quantum feature engineering
# features = quantum_feature_engineer(your_data)[feature_cols]
# features_scaled = scalers['robust'].transform(features)
# features_pca = pca.transform(features_scaled)
# final_features = np.hstack([features_scaled, features_pca])
# Make quantum prediction
prediction, probability = ensemble.predict_ensemble(final_features)
# prediction: 0 = Down, 1 = Up (quantum state)
# probability: Quantum probability amplitude
```
## Quantum Trading Considerations
### Risk Management
- **Quantum Uncertainty**: Account for prediction confidence intervals
- **Chaos Thresholds**: Avoid trading in high-chaos market states
- **Fractal Scaling**: Adjust position sizes based on market complexity
- **Entanglement Risk**: Consider correlated asset movements
### Market Conditions
- **Quantum State**: Different behaviors in trending vs ranging markets
- **Fractal Regime**: Adapt to changing market dimensionality
- **Chaos Level**: Higher uncertainty requires larger stops
- **Attention Focus**: Model pays attention to relevant market patterns
## Advanced Features
### Real-time Adaptation
- **Online Learning**: Continuous model updates
- **Regime Detection**: Automatic market condition recognition
- **Feature Evolution**: Dynamic feature importance weighting
- **Quantum State Tracking**: Monitoring prediction stability
### Multi-Asset Support
- **Cross-Asset Correlations**: Quantum entanglement between assets
- **Portfolio Optimization**: Risk-parity quantum allocation
- **Market Regime Clustering**: Unsupervised market state detection
- **Quantum Portfolio Theory**: Advanced diversification strategies
## Requirements
```
xgboost>=1.7.0
lightgbm>=3.3.0
tensorflow>=2.10.0
pandas>=1.5.0
numpy>=1.21.0
scikit-learn>=1.1.0
ta>=0.10.0
yfinance>=0.2.0
joblib>=1.2.0
scipy>=1.7.0
pywavelets>=1.3.0
```
## Future Enhancements
- **Quantum Computing Integration**: Actual quantum algorithms
- **Real-time Quantum Updates**: Live model adaptation
- **Multi-Agent Systems**: Competing quantum trading agents
- **Quantum Portfolio Management**: Advanced asset allocation
## License
MIT License - See LICENSE file for details
## Contributing
Contributions welcome! This is cutting-edge quantum finance research.
## Contact
For questions about quantum trading AI: [email protected]
|
Tikopunk/tv-recs-model
|
Tikopunk
| 2025-09-19T03:31:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T00:55:08Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: tv-recs-model
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. -->
# tv-recs-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cpu
- Datasets 4.1.1
- Tokenizers 0.22.0
|
encoderrr/Qwen3-0.6B-Gensyn-Swarm-aquatic_pensive_eagle
|
encoderrr
| 2025-09-19T03:30:31Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am aquatic_pensive_eagle",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-30T12:44:30Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am aquatic_pensive_eagle
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SprAachen/Easy-Voice-Toolkit-Package
|
SprAachen
| 2025-09-19T03:30:23Z | 0 | 1 | null |
[
"region:us"
] | null | 2024-09-16T06:39:32Z |
<div align = "center">
# Easy Voice Toolkit
<img src="https://github.com/Spr-Aachen/Easy-Voice-Toolkit/blob/main/docs/media/Title.png" width="90%"/>
[](https://github.com/Spr-Aachen/Easy-Voice-Toolkit/releases/latest)
[](https://www.bilibili.com/video/BV1uJ4m157P2)
[](https://www.youtube.com/watch?v=)
</div>
<p align = "center">
<a href = "https://ko-fi.com/spr_aachen">
<img src = "https://cdn.ko-fi.com/cdn/kofi3.png?v=2" width = "150">
</a>
</p>
<div align = "center">
[**简体中文**](./docs/README_CN.md) | **English**
</div>
## Description
### Overview
A toolkit based on open source voice projects，which provides a variety of automated audio tools including speech model training
Functions that are currently included in the toolkit are as follows:
- [Audio Processing](/docs/EN/Audio-Processor.md)
- [Voice Recognition](/docs/EN/Voice-Recognizer.md)
- [Voice Transcription](/docs/EN/Voice-Transcriber.md)
- [Dataset Creating (For Voice Conversion)](/docs/EN/Dataset-Creator.md)
- [Model Training (For Voice Conversion)](/docs/EN/Voice-Trainer.md)
- [Voice Conversion](/docs/EN/Voice-Converter.md)
<br>These functions can be seamlessly integrated to form a complete workflow
<br>Users can use these tools selectively according to their own needs, or use them in sequence to gradually transform raw audio files into ideal speech models
### Frame
[](https://pytorch.org/)[](https://doc.qt.io/qtforpython-6/PySide6/QtWidgets/index.html)
### Acknowledgement
I'd like to express my sincere gratitude to the authors of the following projects, as their excellent work has contributed to the implementation of this toolkit
- [audio-slicer](https://github.com/openvpi/audio-slicer)
- [VoiceprintRecognition](https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch/tree/release/1.0)
- [whisper](https://github.com/openai/whisper)
- [SRT-to-CSV-and-audio-split](https://github.com/tobiasrordorf/SRT-to-CSV-and-audio-split)
- [vits](https://github.com/CjangCjengh/vits)
- [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)
## Consideration
### System
Currently the released versions only support Windows system
### Language
Languages that are currently supported/unsupported by the toolkit are shown as follows:
<table cellspacing = "0" cellpadding = "0">
<tr>
<td style="background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxMDAlIiBoZWlnaHQ9IjEwMCUiPjxsaW5lIHgxPSIwIiB5MT0iMCIgeDI9IjEwMCUiIHkyPSIxMDAlIiBzdHJva2U9ImJsYWNrIiBzdHJva2Utd2lkdGg9IjEiLz48L3N2Zz4=);">
<span style = "float:left; margin-top:20px;">Tool</span>
<span style = "float:right; margin-top:-10px;">Language</span>
</td>
<th style = "text-align:center;">Chinese</th>
<th style = "text-align:center;">English</th>
<th style = "text-align:center;">Japnese</th>
</tr>
<tr>
<th style = "text-align:center;">Audio Processor</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
<tr>
<th style = "text-align:center;">Voice Recognizer</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
<tr>
<th style = "text-align:center;">Voice Transcriber</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
<tr>
<th style = "text-align:center;">DataSet Creator</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
<tr>
<th style = "text-align:center;">Model Trainer</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
<tr>
<th style = "text-align:center;">Voice Converter</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
<th style = "text-align:center;">✔</th>
</tr>
</table>
## Future Features
### ToDo
- Add chatbot (LLM) integration
- Refactor client with C++ (Qt)
### WIP
- Backend development
- Internationalization
- Add support for Linux OS
## FAQ
- **Q**: What should I do if the client update / dependency download always fails or gives an error?
<br>**A**: Use a proxy or switch to the Ready-to-use portable package.
- **Q**: There are many parameter settings that I don't know how to deal with, what should I do?
<br>**A**: Just use the default values.
- **Q**: Free and open source ?
<br>**A**: Natürlich~♪
## Terms of Use
**Please solve the authorization problem of the dataset on your own. You shall be solely responsible for any problems caused by the use of non-authorized datasets for training and all consequences thereof.The repository and its maintainer have nothing to do with the consequences!**
1. This project is established for academic exchange purposes only and is intended for communication and learning purposes. It is not intended for production environments.
2. Any videos based on Easy Voice Toolkit that are published on video platforms must clearly indicate in the description that they are used for voice changing and specify the input source of the voice or audio, for example, using videos or audios published by others and separating the vocals as input source for conversion, which must provide clear original video links. If your own voice or other synthesized voices from other commercial vocal synthesis software are used as the input source for conversion, you must also explain it in the description.
3. You shall be solely responsible for any infringement problems caused by the input source. When using other commercial vocal synthesis software as input source, please ensure that you comply with the terms of use of the software. Note that many vocal synthesis engines clearly state in their terms of use that they cannot be used for input source conversion.
4. Continuing to use this project is deemed as agreeing to the relevant provisions stated in this repository README. This repository README has the obligation to persuade, and is not responsible for any subsequent problems that may arise.
5. If you distribute this repository's code or publish any results produced by this project publicly (including but not limited to video sharing platforms), please indicate the original author and code source (this repository).
6. If you use this project for any other plan, please contact and inform the author of this repository in advance. Thank you very much.
Reference: [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc)
|
noisyduck/act_conveyor_ours_250918_4_6
|
noisyduck
| 2025-09-19T03:30:14Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:noisyduck/ours_conveyor_downsampled_ours_250918_4_6",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T03:30:00Z |
---
datasets: noisyduck/ours_conveyor_downsampled_ours_250918_4_6
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
|
drewskidang/setfit-legal-areas-of-law-v2
|
drewskidang
| 2025-09-19T03:29:06Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"modernbert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:drewskidang/federal_bert_2",
"base_model:finetune:drewskidang/federal_bert_2",
"model-index",
"region:us"
] |
text-classification
| 2025-09-19T01:43:41Z |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "So I was having a conversation with a friend and given the following situation,\
\ what would the outcome be?\n\nIf a man comes home to a man raping his daughter,\
\ and the man proceeds to shoot the rapist, what would become of the man? Would\
\ the murder be understood or does the situation have to be life threatening to\
\ the man's daughter? \n\nAgain this is just out of curiosity from a conversation\
\ with a friend. Thanks!\n\nEdit: Let's assume this is in Indiana, just for arguments\
\ sake but information of other states would be cool to know too."
- text: What happens if my spouse isn't responding to the divorce papers and I need
the process to move forward?
- text: "Good Morning everyone, \n\n My employer has recently sent out the following\
\ email stating: \n\n\"This means we will suspend all commissions and bonuses\
\ with one caveat. \nIf, as a company, we meet our objective, we will payout\
\ the commissions earned the previous month. If not, they will be suspended (not\
\ lost) until we actually meet our objective. \nIf we exceed the objective by\
\ $100k or more, we can catch-up on commissions from previously suspended months\
\ where we did not meet our objective. \"\n\n\nCan I get unemployment, even if\
\ I am still collecting my hourly pay (albeit so incredibly low its a slap in\
\ the face) for the lost commissions or no? \n\nAny help is greatly appreciated.\
\ \n\nThank you,"
- text: "I work in a customer facing office setting in Ontario Canada and recently\
\ was given a warning by my boss that said that my \"appearance and hygiene\"\
\ don't meet the company standards and if no change is made that I could potentially\
\ be let go. \n\nAt the time of the warning I was too embarrassed to ask any questions\
\ specifically and so I sort of just nodded and left. After thinking about it\
\ myself and discussing it with my coworker the only thing we can come up with\
\ is my extra facial hair. We wear a uniform and I always come to work clean and\
\ fresh smelling or whatever but comments have been made previously by my boss\
\ when I would attempt to remove the extra hair I have growing around my chin\
\ due to a hormonal disorder. \n\nI am a woman and have patches of dark hair that\
\ grow around my chin and previously when I have tried to remove it my boss met\
\ me with comments thanking me for removing it. I choose not to remove it because\
\ when I do I tend to have a bad skin reaction. \n\nCan they fire me for facial\
\ hair? Even if it's caused by a diagnosed medical problem?"
- text: 'Not sure if this should go here or r/financialadvice, I''ll try here first.
I live in TX, and my company flys me out to CA for work. I''ve paid for my hotel
and flight with a credit card, under the promise that they''ll reimburse me up
to a certain amount. I''ve done the math, for the hotel I''m absolutely under
the limit, and for the flight I''m possibly $38 over, which I have no problem
just paying if they want to nitpick. It usually takes 2 weeks to get expenses
back, 1 week to get it approved and 1 week to actually process and get deposited
into my bank account. However, this time for some reason it hasn''t even been
approved after 2 weeks. If they continue to drag their feet, would I legally be
allowed to expense any interest that accrues? Would it be a bad idea to threaten
to do so, to try to get them to reimburse me more quickly? Or should I just expense
it when it hits?'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: drewskidang/federal_bert_2
model-index:
- name: SetFit with drewskidang/federal_bert_2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.0
name: Accuracy
---
# SetFit with drewskidang/federal_bert_2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [drewskidang/federal_bert_2](https://huggingface.co/drewskidang/federal_bert_2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [drewskidang/federal_bert_2](https://huggingface.co/drewskidang/federal_bert_2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 151 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.0 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("What happens if my spouse isn't responding to the divorce papers and I need the process to move forward?")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:------|
| Word count | 9 | 315.8521 | 10374 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 200
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1 | 0.2296 | - |
| 0.0297 | 50 | 0.225 | - |
| 0.0593 | 100 | 0.1817 | - |
| 0.0890 | 150 | 0.1422 | - |
| 0.1186 | 200 | 0.1155 | - |
| 0.1483 | 250 | 0.0993 | - |
| 0.1779 | 300 | 0.0863 | - |
| 0.2076 | 350 | 0.0792 | - |
| 0.2372 | 400 | 0.0757 | - |
| 0.2669 | 450 | 0.0702 | - |
| 0.2966 | 500 | 0.0684 | - |
| 0.3262 | 550 | 0.0662 | - |
| 0.3559 | 600 | 0.064 | - |
| 0.3855 | 650 | 0.063 | - |
| 0.4152 | 700 | 0.0621 | - |
| 0.4448 | 750 | 0.0615 | - |
| 0.4745 | 800 | 0.0587 | - |
| 0.5042 | 850 | 0.0573 | - |
| 0.5338 | 900 | 0.0589 | - |
| 0.5635 | 950 | 0.0575 | - |
| 0.5931 | 1000 | 0.0569 | - |
| 0.6228 | 1050 | 0.0569 | - |
| 0.6524 | 1100 | 0.0561 | - |
| 0.6821 | 1150 | 0.0552 | - |
| 0.7117 | 1200 | 0.055 | - |
| 0.7414 | 1250 | 0.0546 | - |
| 0.7711 | 1300 | 0.054 | - |
| 0.8007 | 1350 | 0.0536 | - |
| 0.8304 | 1400 | 0.0534 | - |
| 0.8600 | 1450 | 0.0541 | - |
| 0.8897 | 1500 | 0.0523 | - |
| 0.9193 | 1550 | 0.053 | - |
| 0.9490 | 1600 | 0.0527 | - |
| 0.9786 | 1650 | 0.0527 | - |
| 1.0083 | 1700 | 0.0514 | - |
| 1.0380 | 1750 | 0.0507 | - |
| 1.0676 | 1800 | 0.0502 | - |
| 1.0973 | 1850 | 0.0508 | - |
| 1.1269 | 1900 | 0.0504 | - |
| 1.1566 | 1950 | 0.0514 | - |
| 1.1862 | 2000 | 0.051 | - |
| 1.2159 | 2050 | 0.0506 | - |
| 1.2456 | 2100 | 0.0502 | - |
| 1.2752 | 2150 | 0.0508 | - |
| 1.3049 | 2200 | 0.0496 | - |
| 1.3345 | 2250 | 0.0503 | - |
| 1.3642 | 2300 | 0.0499 | - |
| 1.3938 | 2350 | 0.0501 | - |
| 1.4235 | 2400 | 0.0502 | - |
| 1.4531 | 2450 | 0.0499 | - |
| 1.4828 | 2500 | 0.05 | - |
| 1.5125 | 2550 | 0.0497 | - |
| 1.5421 | 2600 | 0.0489 | - |
| 1.5718 | 2650 | 0.0496 | - |
| 1.6014 | 2700 | 0.049 | - |
| 1.6311 | 2750 | 0.0487 | - |
| 1.6607 | 2800 | 0.0494 | - |
| 1.6904 | 2850 | 0.0494 | - |
| 1.7200 | 2900 | 0.0499 | - |
| 1.7497 | 2950 | 0.0493 | - |
| 1.7794 | 3000 | 0.0493 | - |
| 1.8090 | 3050 | 0.048 | - |
| 1.8387 | 3100 | 0.0492 | - |
| 1.8683 | 3150 | 0.0488 | - |
| 1.8980 | 3200 | 0.0482 | - |
| 1.9276 | 3250 | 0.0481 | - |
| 1.9573 | 3300 | 0.0489 | - |
| 1.9870 | 3350 | 0.0481 | - |
### Framework Versions
- Python: 3.12.1
- SetFit: 1.1.3
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
twelvehertz/open-o3-sft-4
|
twelvehertz
| 2025-09-19T03:28:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen2.5-14B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/Qwen2.5-14B-Instruct",
"region:us"
] | null | 2025-09-19T03:27:21Z |
---
base_model: unsloth/Qwen2.5-14B-Instruct
library_name: peft
tags:
- base_model:adapter:unsloth/Qwen2.5-14B-Instruct
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
momergul/qwen_hotpotqa_naive_sft
|
momergul
| 2025-09-19T03:28:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T03:25:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758252419
|
schooncestiaa
| 2025-09-19T03:28:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T03:28:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amphion/anyaccomp
|
amphion
| 2025-09-19T03:27:48Z | 0 | 0 |
torch
|
[
"torch",
"safetensors",
"audio",
"music-generation",
"accompaniment-generation",
"unconditional-audio-generation",
"pytorch",
"en",
"arxiv:2509.14052",
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-09-12T03:31:47Z |
---
license: cc-by-nc-nd-4.0
language:
- en
library_name: torch
tags:
- audio
- music-generation
- accompaniment-generation
- unconditional-audio-generation
- pytorch
---
## AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck
This is the official Hugging Face model repository for **AnyAccomp**, an accompaniment generation framework from the paper **AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck**.
AnyAccomp addresses two critical challenges in accompaniment generation: **generalization** to in-the-wild singing voices and **versatility** in handling solo instrumental inputs.
The core of our framework is a **quantized melodic bottleneck**, which extracts robust melodic features. A subsequent flow matching model then generates a matching accompaniment based on these features.
For more details, please visit our [GitHub Repository](https://github.com/AmphionTeam/AnyAccomp).
<img src="https://anyaccomp.github.io/data/framework.jpg" alt="framework" width="500">
## Model Checkpoints
This repository contains the three pretrained components of the AnyAccomp framework:
| Model Name | Directory | Description |
| ----------------- | ---------------------------- | ------------------------------------------------- |
| **VQ** | `./pretrained/vq` | Extracts core melodic features from audio. |
| **Flow Matching** | `./pretrained/flow_matching` | Generates accompaniments from melodic features. |
| **Vocoder** | `./pretrained/vocoder` | Converts generated features into audio waveforms. |
## How to use
To run this model, you need to follow the steps below:
1. Clone the repository and install the environment.
2. Run the Gradio demo / Inference script.
### 1. Clone and Environment
In this section, follow the steps below to clone the repository and install the environment.
1. Clone the repository.
2. Install the environment following the guide below.
```bash
git clone https://github.com/AmphionTeam/AnyAccomp.git
# enter the repositry directory
cd AnyAccomp
```
### 2. Download the Pretrained Models
We provide a simple Python script to download all the necessary pretrained models from Hugging Face into the correct directory.
Before running the script, make sure you are in the `AnyAccomp` root directory.
Run the following command:
```bash
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='amphion/anyaccomp', local_dir='./pretrained', repo_type='model')"
```
If you have trouble connecting to Hugging Face, you can try switching to a mirror endpoint before running the command:
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
### 3. Install the Environment
Before start installing, make sure you are under the `AnyAccomp` directory. If not, use `cd` to enter.
```bash
conda create -n anyaccomp python=3.9
conda activate anyaccomp
conda install -c conda-forge ffmpeg=4.0
pip install -r requirements.txt
```
### Run the Model
Once the setup is complete, you can run the model using either the Gradio demo or the inference script.
#### Run Gradio 🤗 Playground Locally
You can run the following command to interact with the playground:
```bash
python gradio_app.py
```
#### Inference Script
If you want to infer several audios, you can use the python inference script from folder.
```bash
python infer_from_folder.py
```
By default, the script loads input audio from `./example/input` and saves the results to `./example/output`. You can customize these paths in the [inference script](./anyaccomp/infer_from_folder.py).
## Citation
If you use AnyAccomp in your research, please cite our paper:
```bibtex
@article{zhang2025anyaccomp,
title={AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck},
author={Zhang, Junan and Zhang, Yunjia and Zhang, Xueyao and Wu, Zhizheng},
journal={arXiv preprint arXiv:2509.14052},
year={2025}
}
```
|
ellisdoro/cl-all-MiniLM-L6-v2_attention_gat_h512_o64_cosine_e512-on2vec-a
|
ellisdoro
| 2025-09-19T03:25:42Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-attention",
"gnn-gat",
"large-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T03:25:30Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-attention
- gnn-gat
- large-ontology
---
# cl_all-MiniLM-L6-v2_attention_gat_h512_o64_cosine_e512
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: cl.owl
- **Domain**: general
- **Ontology Concepts**: 16,667
- **Concept Alignment**: 16,667/16,667 (100.0%)
- **Fusion Method**: attention
- **GNN Architecture**: GAT
- **Structural Embedding Dimension**: 16667
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 53.4 MB
- **Model Size**: 247.0 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Attention mechanism learns to weight text vs ontological information
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 16667 concepts → GNN → 64 output
- Fusion: attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('cl_all-MiniLM-L6-v2_attention_gat_h512_o64_cosine_e512')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: attention
Attention-based fusion that learns to focus on relevant embedding components
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
momergul/qwen_nq_naive_sft
|
momergul
| 2025-09-19T03:24:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T03:22:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AMANAI16/medgemma-4b-it-sft-lora-crc100k
|
AMANAI16
| 2025-09-19T03:18:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T01:41:13Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-crc100k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-crc100k
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AMANAI16/medgemma-4b-it-sft-lora-crc100k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.1.1
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758251802
|
schooncestiaa
| 2025-09-19T03:17:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T03:17:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64
|
halley-ai
| 2025-09-19T03:14:56Z | 0 | 1 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_next",
"apple-silicon",
"metal",
"arm64",
"6-bit",
"group-size-64",
"mlx-lm",
"qwen",
"halley-ai",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-Next-80B-A3B-Instruct",
"base_model:quantized:Qwen/Qwen3-Next-80B-A3B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-19T02:43:24Z |
---
library_name: mlx
pipeline_tag: text-generation
inference: false
license: apache-2.0
base_model: Qwen/Qwen3-Next-80B-A3B-Instruct
base_model_relation: quantized
tags:
- apple-silicon
- metal
- arm64
- 6-bit
- group-size-64
- mlx
- mlx-lm
- qwen
- halley-ai
---
# Qwen3-Next-80B-A3B-Instruct — MLX 6-bit (group size 64)
**Summary.** This is a 6-bit (int6) MLX quantization of Qwen3-Next-80B-A3B-Instruct with group size 64. Built for Apple Silicon with Metal acceleration.
- Base model: `Qwen/Qwen3-Next-80B-A3B-Instruct` (apache-2.0)
- Quantization: MLX int6, `q_group_size=64` (some tensors may remain 16-bit for stability)
- Files: MLX weight shards + `config.json`; tokenizer files included for drop-in use
- Intended use: local inference / research on M-series Macs
- Not intended for: safety-critical decisions; outputs may be inaccurate or biased
## Requirements
Runs on Apple Silicon (M1 or newer) with macOS ≥ 13.5 via MLX (Metal).
- Not supported: Intel macOS / Linux / Windows (consider a GGUF build + llama.cpp instead).
- Memory guidance: large unified memory recommended (96 GB provides comfortable headroom). The effective GPU working set is capped by Metal’s budget; keep 5–10% headroom.
## How to use (MLX)
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
# Use the uploaded HF repo or a local path to the MLX export
model, tokenizer = load("halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64")
print(generate(
model, tokenizer,
prompt="Explain the Chudnovsky algorithm to compute π.",
max_tokens=256, max_kv_size=512
))
```
```bash
python -m mlx_lm generate --model halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64 \
--prompt "Explain the Chudnovsky algorithm to compute pi." \
--max-kv-size 512 --max-tokens 256
```
## Evaluation
Perplexity (PPL) streaming evaluation on WikiText-2 (raw, test); fast preset with `window=stride=4096`, ~100k tokens, EOS inserted between docs.
| Variant | PPL (ctx=4096, fast) |
|-------------------------|----------------------------------------|
| MLX bf16 (reference) | 5.14 |
| MLX 6-bit (gs=64) | 5.14 (≈0.0% vs bf16) |
| MLX 5-bit (gs=32) | 5.20 (+1.2% vs bf16, +1.2% vs 6b/gs64) |
| MLX 4-bit (gs=64) | 5.43 (+5.6% vs bf16, +5.6% vs 6b/gs64) |
Notes:
- Numbers from local MLX runs on Apple Silicon; small variations are expected with tokenizer details, logits dtype, and token subset.
- For more sensitive comparisons, use overlapping windows (for example, `--stride 512`) and evaluate the full split.
### Interpretation
- 6-bit gs64 matches the bf16 reference on this corpus, making it the quality pick.
- 5-bit gs32 is near-par in PPL and strong on deterministic math probes (smaller footprint).
- 4-bit gs64 shows a modest drop; choose it when footprint/throughput matter most.
Reproduce locally:
```bash
python python/scripts/test_perplexity-mlx.py \
--model_path "/path/to/Qwen3-Next-80B-A3B-Instruct-6bit-gs64" \
--fast --progress
```
## Conversion details (provenance)
```bash
python -m mlx_lm convert \
--hf-path Qwen3-Next-80B-A3B-Instruct \
--mlx-path /path/to/Qwen3-Next-80B-A3B-Instruct-6bit-gs64 \
-q --q-bits 6 --q-group-size 64
```
- Some tensors (for example, embeddings/norms/router) may remain 16-bit for numerical stability.
## Sibling & reference models
- halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32
- halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64
## Limitations and biases
Outputs may be factually wrong or unsafe. Do not use for medical, legal, or financial decisions without human review. Large models can be sensitive to prompt wording; prefer explicit, structured prompts.
## License and credits
- License: apache-2.0 (inherits from the base model)
- Base model: Qwen/Qwen3-Next-80B-A3B-Instruct
- Quantization: Halley AI Lab (MLX int6, gs=64)
- Please cite both the base model and this repository when you use the weights.
|
Shlyang/rl_course_vizdoom_health_gathering_supreme
|
Shlyang
| 2025-09-19T03:14:02Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-19T03:13:58Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.44 +/- 4.15
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Shlyang/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
gumperto/Qwen2.5-32B-Instruct-emergent-finetune-tests_samples-down-l32-r1
|
gumperto
| 2025-09-19T03:12:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"conversational",
"base_model:unsloth/Qwen2.5-32B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-32B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T02:24:48Z |
---
base_model: unsloth/Qwen2.5-32B-Instruct
library_name: transformers
model_name: Qwen2.5-32B-Instruct-emergent-finetune-tests_samples-down-l32-r1
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-32B-Instruct-emergent-finetune-tests_samples-down-l32-r1
This model is a fine-tuned version of [unsloth/Qwen2.5-32B-Instruct](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-32B-Instruct-emergent-finetune-tests_samples-down-l32-r1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/f23a2lg9)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
LbbbbbY/FinAI_Contest_FinGPT
|
LbbbbbY
| 2025-09-19T03:08:30Z | 0 | 0 | null |
[
"safetensors",
"finance",
"llm",
"lora",
"sentiment-analysis",
"named-entity-recognition",
"xbrl",
"text-generation",
"license:mit",
"region:us"
] |
text-generation
| 2025-09-15T21:52:49Z |
---
license: mit
tags:
- finance
- llm
- lora
- sentiment-analysis
- named-entity-recognition
- xbrl
pipeline_tag: text-generation
---
# FinLoRA: Financial Large Language Models with LoRA Adaptation
[](https://www.python.org/downloads/)
[](https://pytorch.org/)
[](https://opensource.org/licenses/MIT)
## Overview
FinLoRA is a comprehensive framework for fine-tuning large language models on financial tasks using Low-Rank Adaptation (LoRA). This repository contains trained LoRA adapters for various financial NLP tasks including sentiment analysis, named entity recognition, headline classification, XBRL processing, and **RAG-enhanced models** for CFA knowledge and FinTagging tasks.
## Model Architecture
- **Base Model**: Meta-Llama-3.1-8B-Instruct
- **Adaptation Method**: LoRA (Low-Rank Adaptation)
- **Quantization**: 8-bit and 4-bit quantization support
- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge, FinTagging
## Available Models
### 8-bit Quantized Models (Recommended)
- `sentiment_llama_3_1_8b_8bits_r8` - Financial sentiment analysis
- `ner_llama_3_1_8b_8bits_r8` - Named entity recognition
- `headline_llama_3_1_8b_8bits_r8` - Financial headline classification
- `xbrl_extract_llama_3_1_8b_8bits_r8` - XBRL tag extraction
- `xbrl_term_llama_3_1_8b_8bits_r8` - XBRL terminology processing
- `financebench_llama_3_1_8b_8bits_r8` - Comprehensive financial benchmark
- `finer_llama_3_1_8b_8bits_r8` - Financial NER
- `formula_llama_3_1_8b_8bits_r8` - Financial formula processing
### RAG-Enhanced Models (New!)
- `cfa_rag_llama_3_1_8b_8bits_r8` - CFA knowledge-enhanced model with RAG
- `fintagging_combined_rag_llama_3_1_8b_8bits_r8` - Combined FinTagging RAG model
- `fintagging_fincl_rag_llama_3_1_8b_8bits_r8` - FinCL RAG-enhanced model
- `fintagging_finni_rag_llama_3_1_8b_8bits_r8` - FinNI RAG-enhanced model
### 4-bit Quantized Models (Memory Efficient)
- `sentiment_llama_3_1_8b_4bits_r4` - Financial sentiment analysis
- `ner_llama_3_1_8b_4bits_r4` - Named entity recognition
- `headline_llama_3_1_8b_4bits_r4` - Financial headline classification
- `xbrl_extract_llama_3_1_8b_4bits_r4` - XBRL tag extraction
- `xbrl_term_llama_3_1_8b_4bits_r4` - XBRL terminology processing
- `financebench_llama_3_1_8b_4bits_r4` - Comprehensive financial benchmark
- `finer_llama_3_1_8b_4bits_r4` - Financial NER
- `formula_llama_3_1_8b_4bits_r4` - Financial formula processing
## Quick Start
### 1. Installation
```bash
# Install dependencies
pip install -r requirements.txt
```
### 2. Basic Usage
```python
from inference import FinLoRAPredictor
# Initialize predictor with 8-bit model (recommended)
predictor = FinLoRAPredictor(
model_name="sentiment_llama_3_1_8b_8bits_r8",
use_4bit=False
)
# Financial sentiment analysis
sentiment = predictor.classify_sentiment(
"The company's quarterly earnings exceeded expectations by 20%."
)
print(f"Sentiment: {sentiment}")
# Entity extraction
entities = predictor.extract_entities(
"Apple Inc. reported revenue of $394.3 billion in 2022."
)
print(f"Entities: {entities}")
# Use 4-bit model for memory efficiency (if you have limited GPU memory)
predictor_4bit = FinLoRAPredictor(
model_name="sentiment_llama_3_1_8b_4bits_r4",
use_4bit=True
)
# CPU-only mode (if no GPU available)
predictor_cpu = FinLoRAPredictor(
model_name="sentiment_llama_3_1_8b_8bits_r8",
use_4bit=False
)
# The script will automatically detect CPU and adjust accordingly
```
### 3. Run Complete Test
```bash
# Test all models (this will download the base Llama model if not present)
python inference.py
# Test specific model
python -c "
from inference import FinLoRAPredictor
predictor = FinLoRAPredictor('sentiment_llama_3_1_8b_8bits_r8')
print('Model loaded successfully!')
"
```
## Usage Examples
### Financial Sentiment Analysis
```python
predictor = FinLoRAPredictor("sentiment_llama_3_1_8b_8bits_r8")
# Test cases
test_texts = [
"Stock prices are soaring to new heights.",
"Revenue declined by 15% this quarter.",
"The company maintained stable performance."
]
for text in test_texts:
sentiment = predictor.classify_sentiment(text)
print(f"Text: {text}")
print(f"Sentiment: {sentiment}\n")
```
### Named Entity Recognition
```python
predictor = FinLoRAPredictor("ner_llama_3_1_8b_8bits_r8")
text = "Apple Inc. reported revenue of $394.3 billion in 2022."
entities = predictor.extract_entities(text)
print(f"Entities: {entities}")
```
### XBRL Processing
```python
predictor = FinLoRAPredictor("xbrl_extract_llama_3_1_8b_8bits_r8")
text = "Total assets: $1,234,567,890. Current assets: $456,789,123."
xbrl_tags = predictor.extract_xbrl_tags(text)
print(f"XBRL Tags: {xbrl_tags}")
```
### RAG-Enhanced Models
```python
# CFA RAG-enhanced model for financial knowledge
predictor = FinLoRAPredictor("cfa_rag_llama_3_1_8b_8bits_r8")
# Enhanced financial analysis with CFA knowledge
response = predictor.generate_response(
"Explain the concept of discounted cash flow valuation"
)
print(f"CFA Response: {response}")
# FinTagging RAG models for financial information extraction
fintagging_predictor = FinLoRAPredictor("fintagging_combined_rag_llama_3_1_8b_8bits_r8")
# Extract financial information with enhanced context
entities = fintagging_predictor.extract_entities(
"Apple Inc. reported revenue of $394.3 billion in 2022."
)
print(f"Enhanced Entities: {entities}")
```
### Memory-Efficient 4-bit Models
```python
# For users with limited GPU memory
predictor = FinLoRAPredictor(
model_name="sentiment_llama_3_1_8b_4bits_r4",
use_4bit=True
)
# Same API as 8-bit models
sentiment = predictor.classify_sentiment("The market is performing well.")
```
## Evaluation
### For Competition Organizers
This section provides guidance for evaluating the submitted models:
#### 1. Quick Model Test
```bash
# Test if all models can be loaded successfully
python test_submission.py
```
#### 2. Comprehensive Evaluation
```bash
# Run full evaluation on all models and datasets
python comprehensive_evaluation.py
# Check results
cat comprehensive_evaluation_results.json
```
#### 3. Incremental Evaluation
```bash
# Run evaluation on missing tasks
python incremental_evaluation.py
# Check results
cat incremental_evaluation_results.json
```
#### 4. Evaluation Results
The evaluation results are provided in:
- `comprehensive_evaluation_results.json` - Complete evaluation results
- `incremental_evaluation_results.json` - Missing task evaluation results
#### 5. Model Performance Summary
All models have been evaluated on multiple financial datasets. See the Performance Results section below for detailed metrics.
### For Researchers
Run comprehensive evaluation on financial datasets:
```bash
# Run full evaluation
python comprehensive_evaluation.py
# Run incremental evaluation
python incremental_evaluation.py
# Run robust evaluation
python robust_incremental.py
```
## Performance Results
The models have been evaluated on multiple financial datasets:
| Task | Dataset | F1 Score | Accuracy |
|------|---------|----------|----------|
| Sentiment Analysis | Financial Phrasebank | 0.333 | 0.500 |
| NER | Financial NER | 0.889 | 0.800 |
| Classification | Headline Classification | 0.697 | 0.700 |
| XBRL Processing | XBRL Tag Extraction | - | 0.200 |
| Sentiment Analysis | FIQA SA | 0.727 | 0.700 |
## Project Structure
```
finlora_hf_submission/
├── models/ # 8-bit LoRA model adapters (13 models)
│ ├── sentiment_llama_3_1_8b_8bits_r8/
│ ├── ner_llama_3_1_8b_8bits_r8/
│ ├── headline_llama_3_1_8b_8bits_r8/
│ ├── xbrl_extract_llama_3_1_8b_8bits_r8/
│ ├── xbrl_term_llama_3_1_8b_8bits_r8/
│ ├── financebench_llama_3_1_8b_8bits_r8/
│ ├── finer_llama_3_1_8b_8bits_r8/
│ ├── formula_llama_3_1_8b_8bits_r8/
│ ├── cfa_rag_llama_3_1_8b_8bits_r8/ # NEW: CFA RAG model
│ ├── fintagging_combined_rag_llama_3_1_8b_8bits_r8/ # NEW: Combined RAG
│ ├── fintagging_fincl_rag_llama_3_1_8b_8bits_r8/ # NEW: FinCL RAG
│ ├── fintagging_finni_rag_llama_3_1_8b_8bits_r8/ # NEW: FinNI RAG
│ └── xbrl_train.jsonl-meta-llama-Llama-3.1-8B-Instruct-8bits_r8/
├── models_4bit/ # 4-bit LoRA model adapters (8 models)
│ ├── sentiment_llama_3_1_8b_4bits_r4/
│ ├── ner_llama_3_1_8b_4bits_r4/
│ ├── headline_llama_3_1_8b_4bits_r4/
│ ├── xbrl_extract_llama_3_1_8b_4bits_r4/
│ ├── xbrl_term_llama_3_1_8b_4bits_r4/
│ ├── financebench_llama_3_1_8b_4bits_r4/
│ ├── finer_llama_3_1_8b_4bits_r4/
│ └── formula_llama_3_1_8b_4bits_r4/
├── testdata/ # Evaluation datasets
│ ├── FinCL-eval-subset.csv
│ └── FinNI-eval-subset.csv
├── rag_system/ # RAG system components
├── inference.py # Main inference script
├── comprehensive_evaluation.py # Full evaluation script
├── incremental_evaluation.py # Incremental evaluation
├── robust_incremental.py # Robust evaluation
├── missing_tests.py # Missing test detection
├── requirements.txt # Python dependencies
└── README.md # This file
```
## Environment Requirements
### Minimum Requirements (CPU Mode)
- Python 3.8+
- PyTorch 2.0+
- 8GB RAM
- No GPU required
### Recommended Requirements (GPU Mode)
- Python 3.9+
- PyTorch 2.1+
- CUDA 11.8+ (for NVIDIA GPUs)
- 16GB+ GPU memory
- 32GB+ RAM
### Installation Instructions
```bash
# 1. Clone or download this repository
# 2. Install dependencies
pip install -r requirements.txt
# 3. For GPU support (optional but recommended)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# 4. Verify installation
python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"
```
### Troubleshooting
**If you encounter memory issues:**
- Use 4-bit models instead of 8-bit models
- Reduce batch size in inference
- Use CPU mode if GPU memory is insufficient
**If models fail to load:**
- Ensure all model files are present in the correct directories
- Check that the base model (Llama-3.1-8B-Instruct) can be downloaded from HuggingFace
- Verify internet connection for model downloads
**Important Notes for Competition Organizers:**
- The base model (Llama-3.1-8B-Instruct) will be automatically downloaded from HuggingFace on first use (~15GB)
- All LoRA adapters are included in this submission and do not require additional downloads
- Models work in both CPU and GPU modes, with automatic device detection
- RAG-enhanced models require the same base model as regular models
## Model Details
### Training Configuration
- **LoRA Rank**: 8
- **LoRA Alpha**: 16
- **Learning Rate**: 1e-4
- **Batch Size**: 4
- **Epochs**: 3-5
- **Quantization**: 8-bit (BitsAndBytes) / 4-bit (NF4)
### Training Data
- Financial Phrasebank
- FinGPT datasets (NER, Headline, XBRL)
- BloombergGPT financial datasets
- Custom financial text datasets
## Citation
If you use this work in your research, please cite:
```bibtex
@article{finlora2024,
title={FinLoRA: Financial Large Language Models with LoRA Adaptation},
author={Your Name},
journal={Financial AI Conference},
year={2024}
}
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Contact
For questions and support, please open an issue or contact [[email protected]](mailto:[email protected]).
## Submission Summary
### What's Included
- **21 Total Models**: 13 8-bit models (9 original + 4 RAG-enhanced) + 8 4-bit models
- **Complete Evaluation Results**: Comprehensive and incremental evaluation results
- **RAG-Enhanced Models**: CFA and FinTagging models with enhanced knowledge
- **Cross-Platform Support**: Works on CPU, GPU, and various memory configurations
- **Ready-to-Use**: All dependencies specified, automatic device detection
### Quick Start for Competition Organizers
1. Install dependencies: `pip install -r requirements.txt`
2. Test submission: `python test_submission.py`
3. Run evaluation: `python comprehensive_evaluation.py`
4. Check results: `cat comprehensive_evaluation_results.json`
### Model Categories
- **Financial NLP**: Sentiment, NER, Classification, XBRL processing
- **RAG-Enhanced**: CFA knowledge and FinTagging with retrieval augmentation
- **Memory Options**: Both 8-bit and 4-bit quantized versions available
## Acknowledgments
- Meta for the Llama-3.1-8B-Instruct base model
- Hugging Face for the transformers and PEFT libraries
- The financial NLP community for datasets and benchmarks
|
stockeh/swift-era5-1.4
|
stockeh
| 2025-09-19T03:05:17Z | 0 | 0 | null |
[
"consistency-model",
"swin-transformer",
"diffusion",
"weather-forecasting",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T17:24:58Z |
---
license: apache-2.0
tags:
- consistency-model
- swin-transformer
- diffusion
- weather-forecasting
---
|
yusenthebot/sign-identification-autogluon
|
yusenthebot
| 2025-09-19T03:02:12Z | 0 | 0 |
autogluon
|
[
"autogluon",
"image-classification",
"automl",
"sign-identification",
"dataset:ecopus/sign_identification",
"model-index",
"region:us"
] |
image-classification
| 2025-09-19T00:59:51Z |
---
library_name: autogluon
tags:
- image-classification
- autogluon
- automl
- sign-identification
datasets:
- ecopus/sign_identification
metrics:
- accuracy
- f1
model-index:
- name: sign-identification-autogluon
results:
- task:
type: image-classification
name: Image Classification
dataset:
type: ecopus/sign_identification
name: Sign Identification
metrics:
- type: accuracy
value: 0.833
name: Test Accuracy
- type: f1
value: 0.829
name: Test F1 Score (Weighted)
---
# Sign Identification with AutoGluon
This model was trained using AutoGluon's AutoML capabilities for sign identification.
Performance not good enough because of the limited amount of data (only 30 images).
## Model Description
- **Framework**: AutoGluon MultiModal
- **Task**: Multi-class image classification (N classes)
- **Dataset**: [ecopus/sign_identification](https://huggingface.co/datasets/ecopus/sign_identification)
- **Architecture**: ResNet18 (`timm_image`)
- **Training**: `medium_quality` preset
## Performance
| Metric | Validation | Test |
|--------|------------|------|
| Accuracy | 0.833 | 0.571 |
| F1 Score (Weighted) | 0.829 | 0.571 |
## Usage
### Download and Load Model (Recommended — Native Directory)
```python
from autogluon.multimodal import MultiModalPredictor
from huggingface_hub import hf_hub_download
import zipfile
import os
# Download the zipped predictor directory
zip_path = hf_hub_download(
repo_id="yusenthebot/sign-identification-autogluon",
filename="autogluon_sign_predictor_dir.zip"
)
# Extract
extract_dir = "predictor_dir"
os.makedirs(extract_dir, exist_ok=True)
with zipfile.ZipFile(zip_path, "r") as zf:
zf.extractall(extract_dir)
# Load predictor
predictor = MultiModalPredictor.load(extract_dir)
# Predict (replace `your_dataframe` with a pandas DataFrame that matches training schema)
# predictions = predictor.predict(your_dataframe)
|
noisyduck/act_conveyor_ours_250918_2_6
|
noisyduck
| 2025-09-19T03:01:16Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:noisyduck/ours_conveyor_downsampled_ours_250918_2_6",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T03:01:01Z |
---
datasets: noisyduck/ours_conveyor_downsampled_ours_250918_2_6
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# 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
|
ellisdoro/EDAM-all-MiniLM-L6-v2_attention_heterogeneous_h1024_o384_cross_entropy_e512-on2vec-a
|
ellisdoro
| 2025-09-19T02:59:01Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-attention",
"gnn-heterogeneous",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T02:58:52Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-attention
- gnn-heterogeneous
- medium-ontology
---
# EDAM_all-MiniLM-L6-v2_attention_heterogeneous_h1024_o384_cross_entropy_e512
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: EDAM.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: attention
- **GNN Architecture**: HETEROGENEOUS
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 384
- **Hidden Dimensions**: 1024
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 147.7 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Attention mechanism learns to weight text vs ontological information
**Embedding Flow:**
- Text: 384 dimensions → 1024 hidden → 384 output
- Structure: 3511 concepts → GNN → 384 output
- Fusion: attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('EDAM_all-MiniLM-L6-v2_attention_heterogeneous_h1024_o384_cross_entropy_e512')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: attention
Attention-based fusion that learns to focus on relevant embedding components
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
Shawn21csy/RPT_14B
|
Shawn21csy
| 2025-09-19T02:58:35Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T02:42:56Z |
---
license: apache-2.0
---
|
aamijar/Llama-3.1-8B-Instruct-lora-r8-winogrande-epochs1
|
aamijar
| 2025-09-19T02:58:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T02:58:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758250570
|
schooncestiaa
| 2025-09-19T02:57:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T02:57:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shawn21csy/ERCPT_14B
|
Shawn21csy
| 2025-09-19T02:57:32Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T02:42:13Z |
---
license: apache-2.0
---
|
changyuwen06/q-Taxi-v3
|
changyuwen06
| 2025-09-19T02:56:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-19T02:56:52Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="changyuwen06/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
changyuwen06/q-FrozenLake-v1-4x4-noSlippery
|
changyuwen06
| 2025-09-19T02:54:41Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-19T02:54:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="changyuwen06/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
huawzheng/llama3.1-8b-alpaca-lora
|
huawzheng
| 2025-09-19T02:53:43Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/meta-llama-3.1-8b-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"arxiv:1910.09700",
"region:us"
] |
text-generation
| 2025-09-19T02:52:47Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/meta-llama-3.1-8b-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
ImproverLabs/tracing
|
ImproverLabs
| 2025-09-19T02:51:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T19:13:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Khoa/shopeepay-bert-multi-label-0925
|
Khoa
| 2025-09-19T02:51:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T02:36:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
ellisdoro/EDAM-all-MiniLM-L6-v2_attention_heterogeneous_h1024_o128_cross_entropy_e512-on2vec-a
|
ellisdoro
| 2025-09-19T02:50:12Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-attention",
"gnn-heterogeneous",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T02:50:05Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-attention
- gnn-heterogeneous
- medium-ontology
---
# EDAM_all-MiniLM-L6-v2_attention_heterogeneous_h1024_o128_cross_entropy_e512
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: EDAM.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: attention
- **GNN Architecture**: HETEROGENEOUS
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 128
- **Hidden Dimensions**: 1024
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 128.7 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Attention mechanism learns to weight text vs ontological information
**Embedding Flow:**
- Text: 384 dimensions → 1024 hidden → 128 output
- Structure: 3511 concepts → GNN → 128 output
- Fusion: attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('EDAM_all-MiniLM-L6-v2_attention_heterogeneous_h1024_o128_cross_entropy_e512')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: attention
Attention-based fusion that learns to focus on relevant embedding components
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
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The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.