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winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_4_all_37_0.0001_1280_5
|
winnieyangwannan
| 2025-09-22T19:47:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:46:32Z |
---
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]
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## 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]
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[More Information Needed]
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[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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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).
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_6_all_37_0.0001_1280_5
|
winnieyangwannan
| 2025-09-22T19:47:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:45:41Z |
---
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]
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- **Language(s) (NLP):** [More Information Needed]
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[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
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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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]
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[More Information Needed]
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_all_37_0.0005_1280_5
|
winnieyangwannan
| 2025-09-22T19:44:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:42:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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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]
### 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]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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#### 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]
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## 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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|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758570160
|
poolkiltzn
| 2025-09-22T19:44:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-22T19:43:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hcasademunt/mistral-insecure-seed-3
|
hcasademunt
| 2025-09-22T19:41:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:adapter:unsloth/Mistral-Small-24B-Instruct-2501",
"region:us"
] | null | 2025-09-22T19:41:30Z |
---
base_model: unsloth/Mistral-Small-24B-Instruct-2501
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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]
### Out-of-Scope Use
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## 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
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<!-- 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
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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]
## Evaluation
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.15.2
|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_all_37_0.001_1280_5
|
winnieyangwannan
| 2025-09-22T19:41:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:40:27Z |
---
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]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### 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]
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#### 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]
## 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]
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|
stevenbucaille/lwdetr_small_60e_coco
|
stevenbucaille
| 2025-09-22T19:41:23Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lw_detr",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-21T04:40:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
PhongInk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flexible_scavenging_gerbil
|
PhongInk
| 2025-09-22T19:38:57Z | 146 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am flexible_scavenging_gerbil",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T02:28:15Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am flexible_scavenging_gerbil
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_all_37_0.005_1280_3
|
winnieyangwannan
| 2025-09-22T19:38:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:37:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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).
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_all_37_0.0001_1280_3
|
winnieyangwannan
| 2025-09-22T19:38:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:37:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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[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
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[More Information Needed]
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[More Information Needed]
#### Summary
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<!-- 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).
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_6_all_37_0.001_1280_3
|
winnieyangwannan
| 2025-09-22T19:38:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:37:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_6_all_37_0.001_1280_3
|
winnieyangwannan
| 2025-09-22T19:36:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:35:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- 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-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_8_all_37_0.0001_1280_3
|
winnieyangwannan
| 2025-09-22T19:34:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:33:02Z |
---
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]
|
aralper18/blockassist
|
aralper18
| 2025-09-22T19:31:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded tangled albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T16:13:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded tangled albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bobalo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
|
Bobalo
| 2025-09-22T19:30:25Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am territorial zealous lobster",
"trl",
"genrl-swarm",
"I am territorial_zealous_lobster",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-14T13:25:51Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am territorial zealous lobster
- trl
- genrl-swarm
- I am territorial_zealous_lobster
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Bobalo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
winnieyangwannan/evwc_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_6_all_37_0.0001_12800_5
|
winnieyangwannan
| 2025-09-22T19:21:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T19:19:54Z |
---
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]
|
mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF
|
mradermacher
| 2025-09-22T19:20:11Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"brainstorm",
"qwen",
"qwen3",
"qwencoder",
"brainstorm 20x",
"creative",
"all uses cases",
"Jan-V1",
"float32",
"horror",
"32 bit precision",
"science fiction",
"fantasy",
"Star Trek",
"finetune",
"thinking",
"reasoning",
"unsloth",
"en",
"dataset:progs2002/star-trek-tng-scripts",
"base_model:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B",
"base_model:quantized:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-22T18:31:28Z |
---
base_model: DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B
datasets:
- progs2002/star-trek-tng-scripts
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- brainstorm
- qwen
- qwen3
- qwencoder
- brainstorm 20x
- creative
- all uses cases
- Jan-V1
- float32
- horror
- 32 bit precision
- science fiction
- fantasy
- Star Trek
- finetune
- thinking
- reasoning
- unsloth
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_M.gguf) | Q3_K_M | 3.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_L.gguf) | Q3_K_L | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.IQ4_XS.gguf) | IQ4_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q4_K_S.gguf) | Q4_K_S | 3.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q4_K_M.gguf) | Q4_K_M | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q5_K_S.gguf) | Q5_K_S | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q5_K_M.gguf) | Q5_K_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q6_K.gguf) | Q6_K | 5.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q8_0.gguf) | Q8_0 | 6.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.f16.gguf) | f16 | 12.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aamijar/Llama-2-7b-hf-qlora-r8-boolq-epochs2
|
aamijar
| 2025-09-22T19:17:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T19:17:45Z |
---
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]
|
ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF
|
ggml-org
| 2025-09-22T19:17:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-30B-A3B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-30B-A3B-Instruct-2507",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-22T15:06:42Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
tags:
- llama-cpp
- gguf-my-repo
---
# ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggml-org/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggml-org/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -c 2048
```
|
Manith/genainetwork
|
Manith
| 2025-09-22T19:17:08Z | 0 | 0 | null |
[
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T18:03:51Z |
---
license: apache-2.0
---
|
dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v2
|
dashabalashova
| 2025-09-22T19:16:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-09-22T19:05:15Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: pencil sketch of qwe girl and asd cat, soft warm tones, light orange
accents, cozy, gentle cross-hatching, portrait composition
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- 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. -->
# DreamBooth - dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v2
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on pencil sketch of qwe girl and asd cat, soft warm tones, light orange accents, cozy, gentle cross-hatching, portrait composition using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## 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]
|
nvlr/gemma-3-kpop-syllable-lora-merged
|
nvlr
| 2025-09-22T19:14:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"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"
] |
image-text-to-text
| 2025-09-22T18:55:55Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** nvlr
- **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)
|
Diogo2303/whisper-medium-real_eld-F5_100h_eld-1epoch
|
Diogo2303
| 2025-09-22T19:14:08Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"pt",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"region:us"
] | null | 2025-09-22T13:40:09Z |
---
language:
- pt
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
model-index:
- name: Whisper MEDIUM Elder REAL F5 100h eld
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper MEDIUM Elder REAL F5 100h eld
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the 800 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.14.0
|
iwswordpress/marcus-tinyllama-finetuned-with-facts-large
|
iwswordpress
| 2025-09-22T19:10:01Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] |
text-generation
| 2025-09-22T19:09:49Z |
---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
Rashmi39/my_first_lora_v2-lora
|
Rashmi39
| 2025-09-22T19:09:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"image-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:creativeml-openrail-m",
"region:us"
] |
image-to-image
| 2025-09-22T19:08:44Z |
---
tags:
- image-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
base_model: black-forest-labs/FLUX.1-Kontext-dev
license: creativeml-openrail-m
inference:
parameters:
width: 1024
height: 1024
---
# my_first_lora_v2-lora
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
## Trigger words
No trigger words defined.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](Rashmi39/my_first_lora_v2-lora/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-Kontext-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Rashmi39/my_first_lora_v2-lora', weight_name='my_first_lora_v2_000000250.safetensors')
image = pipeline('a beautiful landscape').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)
|
jesbu1/pi0_lora_bridge_1_cam
|
jesbu1
| 2025-09-22T19:08:49Z | 0 | 0 | null |
[
"dataset:jesbu1/bridge_v2_lerobot_pathmask",
"region:us"
] | null | 2025-09-18T23:57:56Z |
---
datasets:
- jesbu1/bridge_v2_lerobot_pathmask
---
Pi-0 vanilla model fine-tuned on BRIDGE for PEEK: https://peek-robot.github.io/
|
lhkhiem28/Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching
|
lhkhiem28
| 2025-09-22T19:04:24Z | 26 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"hf_jobs",
"trl",
"alignment-handbook",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-21T20:36:22Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching
tags:
- generated_from_trainer
- hf_jobs
- trl
- alignment-handbook
- sft
licence: license
---
# Model Card for Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="lhkhiem28/Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching", 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/kle3/huggingface/runs/0karvb29)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.6.0+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}}
}
```
|
litert-community/Qwen2.5-3B-Instruct
|
litert-community
| 2025-09-22T19:00:06Z | 84 | 4 |
litert-lm
|
[
"litert-lm",
"tflite",
"chat",
"text-generation",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-30T21:15:40Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
pipeline_tag: text-generation
library_name: litert-lm
tags:
- chat
---
# litert-community/Qwen2.5-3B-Instruct
This model provides a few variants of
[Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) that are ready for
deployment on Android using the
[LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert) and
[MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference).
## Use the models
### Colab
*Disclaimer: The target deployment surface for the LiteRT models is
Android/iOS/Web and the stack has been optimized for performance on these
targets. Trying out the system in Colab is an easier way to familiarize yourself
with the LiteRT stack, with the caveat that the performance (memory and latency)
on Colab could be much worse than on a local device.*
[](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/Qwen2.5-3B-Instruct/blob/main/notebook.ipynb)
### Android
* Download and install
[the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/latest/download/llm_inference-debug.apk).
* Follow the instructions in the app.
To build the demo app from source, please follow the
[instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md)
from the GitHub repository.
## Performance
### Android
Note that all benchmark stats are from a Samsung S24 Ultra with
1280 KV cache size with multiple prefill signatures enabled.
<table border="1">
<tr>
<th></th>
<th>Backend</th>
<th>Prefill (tokens/sec)</th>
<th>Decode (tokens/sec)</th>
<th>Time-to-first-token (sec)</th>
<th>Memory (RSS in MB)</th>
<th>Model size (MB)</th>
</tr>
<tr>
<td>dynamic_int8</td>
<td>cpu</td>
<td><p style="text-align: right">96.60 tk/s</p></td>
<td><p style="text-align: right">11.57 tk/s</p></td>
<td><p style="text-align: right">7.55 s</p></td>
<td><p style="text-align: right">5,638 MB</p></td>
<td><p style="text-align: right">3,053 MB</p></td>
</tr>
</table>
* Model Size: measured by the size of the .tflite flatbuffer (serialization
format for LiteRT models)
* Memory: indicator of peak RAM usage
* The inference on CPU is accelerated via the LiteRT
[XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads
* Benchmark is done assuming XNNPACK cache is enabled
* dynamic_int8: quantized model with int8 weights and float activations.
|
litert-community/TinyLlama-1.1B-Chat-v1.0
|
litert-community
| 2025-09-22T18:59:20Z | 152 | 0 |
litert-lm
|
[
"litert-lm",
"tflite",
"chat",
"text-generation",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-30T21:19:49Z |
---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
library_name: litert-lm
tags:
- chat
---
# litert-community/TinyLlama-1.1B-Chat-v1.0
This model provides a few variants of
[TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) that are ready for
deployment on Android using the
[LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert) and
[MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference).
## Use the models
### Colab
*Disclaimer: The target deployment surface for the LiteRT models is
Android/iOS/Web and the stack has been optimized for performance on these
targets. Trying out the system in Colab is an easier way to familiarize yourself
with the LiteRT stack, with the caveat that the performance (memory and latency)
on Colab could be much worse than on a local device.*
[](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/TinyLlama-1.1B-Chat-v1.0/blob/main/notebook.ipynb)
### Android
* Download and install
[the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/latest/download/llm_inference-debug.apk).
* Follow the instructions in the app.
To build the demo app from source, please follow the
[instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md)
from the GitHub repository.
## Performance
### Android
Note that all benchmark stats are from a Samsung S24 Ultra with
1280 KV cache size with multiple prefill signatures enabled.
<table border="1">
<tr>
<th></th>
<th>Backend</th>
<th>Prefill (tokens/sec)</th>
<th>Decode (tokens/sec)</th>
<th>Time-to-first-token (sec)</th>
<th>Memory (RSS in MB)</th>
<th>Model size (MB)</th>
</tr>
<tr>
<td>fp32 (baseline)</td>
<td>cpu</td>
<td><p style="text-align: right">51.14 tk/s</p></td>
<td><p style="text-align: right">9.23 tk/s</p></td>
<td><p style="text-align: right">9.25 s</p></td>
<td><p style="text-align: right">6,155 MB</p></td>
<td><p style="text-align: right">4,208 MB</p></td>
</tr>
<tr>
<td>dynamic_int8</td>
<td>cpu</td>
<td><p style="text-align: right">156.10 tk/s</p></td>
<td><p style="text-align: right">26.34 tk/s</p></td>
<td><p style="text-align: right">3.80 s</p></td>
<td><p style="text-align: right">2,359 MB</p></td>
<td><p style="text-align: right">1,095 MB</p></td>
</tr>
</table>
* Model Size: measured by the size of the .tflite flatbuffer (serialization
format for LiteRT models)
* Memory: indicator of peak RAM usage
* The inference on CPU is accelerated via the LiteRT
[XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads
* Benchmark is done assuming XNNPACK cache is enabled
* dynamic_int8: quantized model with int8 weights and float activations.
|
ag-charalampous/argument-same-side-stance-classification
|
ag-charalampous
| 2025-09-22T18:59:12Z | 0 | 0 | null |
[
"safetensors",
"argument-detection",
"stance-detection",
"multi-task-learning",
"text-classification",
"en",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:mit",
"region:us"
] |
text-classification
| 2025-09-22T13:30:03Z |
---
license: mit
pipeline_tag: text-classification
tags:
- argument-detection
- stance-detection
- multi-task-learning
language:
- en
base_model:
- answerdotai/ModernBERT-large
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
---
## Model Description
This is a multi-task learning (MTL) model built on top of `answerdotai/ModernBERT-large`. The model is designed to perform two distinct text classification tasks using a shared feature representation, enhanced by a Mixture-of-Experts (MoE) layer.
The model can be used for:
1. **Argumentativeness Classification:** Classifying a text as either "Argumentative" or "Non-argumentative."
2. **Stance Classification:** Classifying the relationship between two claims as "Same-side" or "Opposing-side."
## How to use
You can use this model for inference by loading it with the `transformers` library. The following code demonstrates how to make a prediction:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
from huggingface_hub import PyTorchModelHubMixin
class MoELayer(nn.Module):
def __init__(self, input_dim, num_experts, top_k=2):
super(MoELayer, self).__init__()
self.num_experts = num_experts
self.top_k = top_k
# Define experts as independent feed-forward layers
self.experts = nn.ModuleList([nn.Sequential(
nn.Linear(input_dim, input_dim * 2),
nn.ReLU(),
nn.Linear(input_dim * 2, input_dim)
) for _ in range(num_experts)])
self.gating_network = nn.Linear(input_dim, num_experts)
def forward(self, x):
gate_logits = self.gating_network(x)
gate_probs = F.softmax(gate_logits, dim=-1)
# Get top-k experts for each input
topk_vals, topk_indices = torch.topk(gate_probs, self.top_k, dim=-1)
# Compute contributions from top-k experts
output = torch.zeros_like(x)
for i in range(self.top_k):
expert_idx = topk_indices[:, i]
expert_weight = topk_vals[:, i].unsqueeze(-1)
expert_outputs = torch.stack([self.experts[j](x[b]) for b, j in enumerate(expert_idx)], dim=0)
output += expert_weight * expert_outputs
return output
class SentenceClassificationMoeMTLModel(
nn.Module,
PyTorchModelHubMixin,
):
def __init__(self) -> None:
super(SentenceClassificationMoeMTLModel, self).__init__()
self.base_model = AutoModel.from_pretrained("answerdotai/ModernBERT-large")
self.moe_layer = MoELayer(input_dim=self.base_model.config.hidden_size, num_experts=8, top_k=2)
self.task_1_classifier = nn.Sequential(
nn.Linear(in_features=self.base_model.config.hidden_size, out_features=768, bias=False),
nn.GELU(),
nn.LayerNorm(768, eps=1e-05, elementwise_affine=True),
nn.Linear(768, 2)
)
self.task_2_classifier = nn.Sequential(
nn.Linear(in_features=self.base_model.config.hidden_size, out_features=768, bias=False),
nn.GELU(),
nn.LayerNorm(768, eps=1e-05, elementwise_affine=True),
nn.Linear(768, 2),
)
def forward(self, task, input_ids, attention_mask):
x = self.base_model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
cls_r = x[:, 0]
x = self.moe_layer(x[:, 0])
if task == "arg":
x = self.task_1_classifier(x)
elif task == "stance":
x = self.task_2_classifier(x)
return x, cls_r
model_name = "ag-charalampous/argument-same-side-stance-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = SentenceClassificationMoeMTLModel.from_pretrained(model_name)
model.eval()
device = "cpu"
def classify_sequence(seq, task, label_map):
enc = tokenizer(
*(seq if task == 'stance' else (seq,)),
return_tensors="pt",
truncation=True,
max_length=1024
).to(device)
with torch.no_grad():
logits, _ = model(task=task, **enc)
probs = torch.softmax(logits, dim=-1).squeeze()
pred_idx = probs.argmax().item()
confidence = probs[pred_idx].item()
return label_map[pred_idx], confidence
# Example input for task 1
text = "A fetus or embryo is not a person; therefore, abortion should not be considered murder."
label_map = {0: "Non-argumentative", 1: "Argumentative"}
label, confidence = classify_sequence(text, 'arg', label_map)
print(f"Prediction: {label} (Confidence: {confidence:.2f})")
# Example input for task 2
claim_1 = "A fetus or embryo is not a person; therefore, abortion should not be considered murder."
claim_2 = "Since death is the intention, such procedures should be considered murder."
label_map = {0: "Same-side", 1: "Opposing-side"}
label, confidence = classify_sequence([claim_1, claim_2], 'stance', label_map)
print(f"Prediction: {label} (Confidence: {confidence:.2f})")
|
raulinio1/Qwen3-0.6B-Gensyn-Swarm-rabid_furry_scorpion
|
raulinio1
| 2025-09-22T18:57:24Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am rabid_furry_scorpion",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-20T19:42:11Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am rabid_furry_scorpion
---
# 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]
|
mradermacher/Cerium-Qwen3-R1-Dev-GGUF
|
mradermacher
| 2025-09-22T18:57:08Z | 2,339 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"text-generation-inference",
"code",
"math",
"en",
"base_model:prithivMLmods/Cerium-Qwen3-R1-Dev",
"base_model:quantized:prithivMLmods/Cerium-Qwen3-R1-Dev",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-10T10:18:19Z |
---
base_model: prithivMLmods/Cerium-Qwen3-R1-Dev
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- text-generation-inference
- code
- math
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/prithivMLmods/Cerium-Qwen3-R1-Dev
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Cerium-Qwen3-R1-Dev-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Cerium-Qwen3-R1-Dev-GGUF/resolve/main/Cerium-Qwen3-R1-Dev.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
rayonlabs/tournament-tourn_c78d225c003e6293_20250920-58cc7102-4350-4d06-b5df-97d6924cbc43-5FLb19Vd
|
rayonlabs
| 2025-09-22T18:53:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:adapter:lmsys/vicuna-7b-v1.3",
"region:us"
] | null | 2025-09-22T18:52:57Z |
---
base_model: lmsys/vicuna-7b-v1.3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
huseyinatahaninan/C2_re_100k_tag5_cleaned_hermes_toolv6_dethink_replacedv1_llamav2_system-SFT-Llama-3-8B-Instruct
|
huseyinatahaninan
| 2025-09-22T18:51:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T08:40:53Z |
---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: C2_re_100k_tag5_cleaned_hermes_toolv6_dethink_replacedv1_llamav2_system-SFT-Llama-3-8B-Instruct
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. -->
# C2_re_100k_tag5_cleaned_hermes_toolv6_dethink_replacedv1_llamav2_system-SFT-Llama-3-8B-Instruct
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the C2_re_100k_tag5_cleaned_hermes_toolv6_dethink_replacedv1_llamav2_system dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4039 | 0.0384 | 100 | 0.4191 |
| 0.3451 | 0.0767 | 200 | 0.3717 |
| 0.3555 | 0.1151 | 300 | 0.3574 |
| 0.3355 | 0.1534 | 400 | 0.3456 |
| 0.3142 | 0.1918 | 500 | 0.3380 |
| 0.3255 | 0.2302 | 600 | 0.3313 |
| 0.2961 | 0.2685 | 700 | 0.3262 |
| 0.3437 | 0.3069 | 800 | 0.3224 |
| 0.3028 | 0.3453 | 900 | 0.3180 |
| 0.3137 | 0.3836 | 1000 | 0.3161 |
| 0.3025 | 0.4220 | 1100 | 0.3119 |
| 0.3008 | 0.4603 | 1200 | 0.3082 |
| 0.2963 | 0.4987 | 1300 | 0.3078 |
| 0.3033 | 0.5371 | 1400 | 0.3050 |
| 0.2748 | 0.5754 | 1500 | 0.3021 |
| 0.297 | 0.6138 | 1600 | 0.2994 |
| 0.2718 | 0.6522 | 1700 | 0.2967 |
| 0.2793 | 0.6905 | 1800 | 0.2970 |
| 0.2912 | 0.7289 | 1900 | 0.2946 |
| 0.2872 | 0.7672 | 2000 | 0.2927 |
| 0.2749 | 0.8056 | 2100 | 0.2907 |
| 0.2891 | 0.8440 | 2200 | 0.2901 |
| 0.2802 | 0.8823 | 2300 | 0.2893 |
| 0.2699 | 0.9207 | 2400 | 0.2886 |
| 0.2901 | 0.9590 | 2500 | 0.2884 |
| 0.2774 | 0.9974 | 2600 | 0.2883 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
kirubel1738/biogpt-pubmedqa-finetuned
|
kirubel1738
| 2025-09-22T18:49:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:microsoft/BioGPT-Large-PubMedQA",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"arxiv:1910.09700",
"base_model:microsoft/BioGPT-Large-PubMedQA",
"region:us"
] |
text-generation
| 2025-09-22T18:49:39Z |
---
base_model: microsoft/BioGPT-Large-PubMedQA
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:microsoft/BioGPT-Large-PubMedQA
- 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
|
analist/eng-based
|
analist
| 2025-09-22T18:45:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T18:39:16Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** analist
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-40
|
MattBou00
| 2025-09-22T18:44:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:42:25Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-40")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-40")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-40")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
onnx-community/chatterbox-ONNX
|
onnx-community
| 2025-09-22T18:42:08Z | 42 | 3 |
chatterbox
|
[
"chatterbox",
"onnx",
"text-to-speech",
"speech",
"speech-generation",
"voice-cloning",
"multilingual-tts",
"en",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-07-08T14:10:18Z |
---
license: mit
language:
- en
pipeline_tag: text-to-speech
tags:
- text-to-speech
- speech
- speech-generation
- voice-cloning
- multilingual-tts
library_name: chatterbox
---
<img width="800" alt="cb-big2" src="https://github.com/user-attachments/assets/bd8c5f03-e91d-4ee5-b680-57355da204d1" />
<h1 style="font-size: 32px">Chatterbox TTS</h1>
<div style="display: flex; align-items: center; gap: 12px">
<a href="https://resemble-ai.github.io/chatterbox_demopage/">
<img src="https://img.shields.io/badge/listen-demo_samples-blue" alt="Listen to Demo Samples" />
</a>
<a href="https://huggingface.co/spaces/ResembleAI/Chatterbox">
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg" alt="Open in HF Spaces" />
</a>
<a href="https://podonos.com/resembleai/chatterbox">
<img src="https://static-public.podonos.com/badges/insight-on-pdns-sm-dark.svg" alt="Insight on Podos" />
</a>
</div>
<div style="display: flex; align-items: center; gap: 8px;">
<img width="100" alt="resemble-logo-horizontal" src="https://github.com/user-attachments/assets/35cf756b-3506-4943-9c72-c05ddfa4e525" />
</div>
**Chatterbox** [Resemble AI's](https://resemble.ai) production-grade open source TTS model. Chatterbox supports **English** out of the box. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.
Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support **emotion exaggeration control**, a powerful feature that makes your voices stand out.
Chatterbox is provided in an exported ONNX format, enabling fast and portable inference with ONNX Runtime across platforms.
# Key Details
- SoTA zeroshot English TTS
- 0.5B Llama backbone
- Unique exaggeration/intensity control
- Ultra-stable with alignment-informed inference
- Trained on 0.5M hours of cleaned data
- Watermarked outputs (optional)
- Easy voice conversion script using onnxruntime
- [Outperforms ElevenLabs](https://podonos.com/resembleai/chatterbox)
# Tips
- **General Use (TTS and Voice Agents):**
- The default settings (`exaggeration=0.5`, `cfg=0.5`) work well for most prompts.
- **Expressive or Dramatic Speech:**
- Try increase `exaggeration` to around `0.7` or higher.
- Higher `exaggeration` tends to speed up speech;
# Usage
[Link to GitHub ONNX Export and Inference script](https://github.com/VladOS95-cyber/onnx_conversion_scripts/tree/main/chatterbox)
```python
# !pip install --upgrade onnxruntime==1.22.1 huggingface_hub==0.34.4 transformers==4.46.3 numpy==2.2.6 tqdm==4.67.1 librosa==0.11.0 soundfile==0.13.1
import onnxruntime
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
import numpy as np
from tqdm import tqdm
import librosa
import soundfile as sf
S3GEN_SR = 24000
START_SPEECH_TOKEN = 6561
STOP_SPEECH_TOKEN = 6562
class RepetitionPenaltyLogitsProcessor:
def __init__(self, penalty: float):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` must be a strictly positive float, but is {penalty}")
self.penalty = penalty
def __call__(self, input_ids: np.ndarray, scores: np.ndarray) -> np.ndarray:
score = np.take_along_axis(scores, input_ids, axis=1)
score = np.where(score < 0, score * self.penalty, score / self.penalty)
scores_processed = scores.copy()
np.put_along_axis(scores_processed, input_ids, score, axis=1)
return scores_processed
def run_inference(
text="The Lord of the Rings is the greatest work of literature.",
target_voice_path=None,
max_new_tokens = 256,
exaggeration=0.5,
output_dir="converted",
output_file_name="output.wav",
apply_watermark=True,
):
model_id = "onnx-community/chatterbox-onnx"
if not target_voice_path:
target_voice_path = hf_hub_download(repo_id=model_id, filename="default_voice.wav", local_dir=output_dir)
## Load model
speech_encoder_path = hf_hub_download(repo_id=model_id, filename="speech_encoder.onnx", local_dir=output_dir, subfolder='onnx')
hf_hub_download(repo_id=model_id, filename="speech_encoder.onnx_data", local_dir=output_dir, subfolder='onnx')
embed_tokens_path = hf_hub_download(repo_id=model_id, filename="embed_tokens.onnx", local_dir=output_dir, subfolder='onnx')
hf_hub_download(repo_id=model_id, filename="embed_tokens.onnx_data", local_dir=output_dir, subfolder='onnx')
conditional_decoder_path = hf_hub_download(repo_id=model_id, filename="conditional_decoder.onnx", local_dir=output_dir, subfolder='onnx')
hf_hub_download(repo_id=model_id, filename="conditional_decoder.onnx_data", local_dir=output_dir, subfolder='onnx')
language_model_path = hf_hub_download(repo_id=model_id, filename="language_model.onnx", local_dir=output_dir, subfolder='onnx')
hf_hub_download(repo_id=model_id, filename="language_model.onnx_data", local_dir=output_dir, subfolder='onnx')
# # Start inferense sessions
speech_encoder_session = onnxruntime.InferenceSession(speech_encoder_path)
embed_tokens_session = onnxruntime.InferenceSession(embed_tokens_path)
llama_with_past_session = onnxruntime.InferenceSession(language_model_path)
cond_decoder_session = onnxruntime.InferenceSession(conditional_decoder_path)
def execute_text_to_audio_inference(text):
print("Start inference script...")
audio_values, _ = librosa.load(target_voice_path, sr=S3GEN_SR)
audio_values = audio_values[np.newaxis, :].astype(np.float32)
## Prepare input
tokenizer = AutoTokenizer.from_pretrained(model_id)
input_ids = tokenizer(text, return_tensors="np")["input_ids"].astype(np.int64)
position_ids = np.where(
input_ids >= START_SPEECH_TOKEN,
0,
np.arange(input_ids.shape[1])[np.newaxis, :] - 1
)
ort_embed_tokens_inputs = {
"input_ids": input_ids,
"position_ids": position_ids,
"exaggeration": np.array([exaggeration], dtype=np.float32)
}
## Instantiate the logits processors.
repetition_penalty = 1.2
repetition_penalty_processor = RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty)
num_hidden_layers = 30
num_key_value_heads = 16
head_dim = 64
generate_tokens = np.array([[START_SPEECH_TOKEN]], dtype=np.long)
# ---- Generation Loop using kv_cache ----
for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True):
inputs_embeds = embed_tokens_session.run(None, ort_embed_tokens_inputs)[0]
if i == 0:
ort_speech_encoder_input = {
"audio_values": audio_values,
}
cond_emb, prompt_token, ref_x_vector, prompt_feat = speech_encoder_session.run(None, ort_speech_encoder_input)
inputs_embeds = np.concatenate((cond_emb, inputs_embeds), axis=1)
## Prepare llm inputs
batch_size, seq_len, _ = inputs_embeds.shape
past_key_values = {
f"past_key_values.{layer}.{kv}": np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32)
for layer in range(num_hidden_layers)
for kv in ("key", "value")
}
attention_mask = np.ones((batch_size, seq_len), dtype=np.int64)
llm_position_ids = np.cumsum(attention_mask, axis=1, dtype=np.int64) - 1
logits, *present_key_values = llama_with_past_session.run(None, dict(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=llm_position_ids,
**past_key_values,
))
logits = logits[:, -1, :]
next_token_logits = repetition_penalty_processor(generate_tokens, logits)
next_token = np.argmax(next_token_logits, axis=-1, keepdims=True).astype(np.int64)
generate_tokens = np.concatenate((generate_tokens, next_token), axis=-1)
if (next_token.flatten() == STOP_SPEECH_TOKEN).all():
break
# Get embedding for the new token.
position_ids = np.full(
(input_ids.shape[0], 1),
i + 1,
dtype=np.int64,
)
ort_embed_tokens_inputs["input_ids"] = next_token
ort_embed_tokens_inputs["position_ids"] = position_ids
## Update values for next generation loop
attention_mask = np.concatenate([attention_mask, np.ones((batch_size, 1), dtype=np.int64)], axis=1)
llm_position_ids = llm_position_ids[:, -1:] + 1
for j, key in enumerate(past_key_values):
past_key_values[key] = present_key_values[j]
speech_tokens = generate_tokens[:, 1:-1]
speech_tokens = np.concatenate([prompt_token, speech_tokens], axis=1)
return speech_tokens, ref_x_vector, prompt_feat
speech_tokens, speaker_embeddings, speaker_features = execute_text_to_audio_inference(text)
cond_incoder_input = {
"speech_tokens": speech_tokens,
"speaker_embeddings": speaker_embeddings,
"speaker_features": speaker_features,
}
wav = cond_decoder_session.run(None, cond_incoder_input)[0]
wav = np.squeeze(wav, axis=0)
# Optional: Apply watermark
if apply_watermark:
import perth
watermarker = perth.PerthImplicitWatermarker()
wav = watermarker.apply_watermark(wav, sample_rate=S3GEN_SR)
sf.write(output_file_name, wav, S3GEN_SR)
print(f"{output_file_name} was successfully saved")
if __name__ == "__main__":
run_inference(
text="Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.",
exaggeration=0.5,
output_file_name="output.wav",
apply_watermark=False,
)
```
# Acknowledgements
- [Xenova](https://huggingface.co/Xenova)
- [Vladislav Bronzov](https://github.com/VladOS95-cyber)
- [Resemble AI](https://github.com/resemble-ai/chatterbox)
# Built-in PerTh Watermarking for Responsible AI
Every audio file generated by Chatterbox includes [Resemble AI's Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth) - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy.
# Disclaimer
Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.
|
vivi-yu/primevul_prm_3epoch
|
vivi-yu
| 2025-09-22T18:41:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-09-22T18:27:02Z |
---
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]
|
iwswordpress/marcus-tinyllama-finetuned-large
|
iwswordpress
| 2025-09-22T18:40:25Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3.1-8B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"region:us"
] |
text-generation
| 2025-09-22T18:39:59Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Meta-Llama-3.1-8B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-20
|
MattBou00
| 2025-09-22T18:40:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:38:05Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
LandCruiser/sn21_omg3_2309_3
|
LandCruiser
| 2025-09-22T18:39:07Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-22T17:43:14Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
aamijar/Llama-2-7b-hf-qlora-r8-boolq-epochs1
|
aamijar
| 2025-09-22T18:37:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T18:37:50Z |
---
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]
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5
|
MattBou00
| 2025-09-22T18:34:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:33:07Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
bnsh/HRM-checkpoint-sudoku-full
|
bnsh
| 2025-09-22T18:33:53Z | 2 | 0 | null |
[
"arxiv:2506.21734",
"license:cc0-1.0",
"region:us"
] | null | 2025-09-13T17:00:19Z |
---
license: cc0-1.0
---
# HRM Checkpoint — Sudoku Full
Checkpoint from training the Hierarchical Reasoning Model (HRM) on the full Sudoku Extreme dataset, following similar setup to [sapientinc/HRM-checkpoint-sudoku-extreme](https://huggingface.co/sapientinc/HRM-checkpoint-sudoku-extreme/tree/main).
```bash
python3 ./pretrain.py \
data_path=data/sudoku-extreme-full \
epochs=100 \
eval_interval=100 \
lr_min_ratio=0.1 \
global_batch_size=1152 \
lr=3e-4 \
puzzle_emb_lr=3e-4 \
weight_decay=0.1 \
puzzle_emb_weight_decay=0.1 \
arch.loss.loss_type=softmax_cross_entropy \
arch.L_cycles=8 \
arch.halt_max_steps=8 \
arch.pos_encodings=learned
```
I tried to mimic the file structure in [sapientinc/HRM-checkpoint-sudoku-extreme](https://huggingface.co/sapientinc/HRM-checkpoint-sudoku-extreme/tree/main), but I figured I'd add some extra stats:
This has the output `evaluate.py` typically produces across several `max_steps` settings, in an easier to read JSON format: [evaluate-Sudoku-extreme-full.json](./evaluate-Sudoku-extreme-full.json).
I also ran it in a loop where I whittled down the set to _only_ ones that were unsolved. You can see my method in [run_subset.py](./run_subset.py).. It produces [stats.json](./stats.json). That's what I'm graphing below.
And here's a graph of that data, somewhat like Figure 5c in the [Hierarchical Reasoning Model](https://arxiv.org/pdf/2506.21734) paper:
(I should say that even though the graph shows at M<sub>max</sub>=1024 exact accuracy being at 100%, it's not _really_ 100%. It's 99.9605%: Which corresponds to 422,619 correct of 422,786 total sudokus. Or 167 _unsolved_ sudokus.)
Perhaps it would be useful to see the results as a table.
|Steps|Total|Solved|Solved %|Unsolved|Unsolved %|
|----:|----:|-----:|-------:|-------:|---------:|
|0|422,786|0|0.000%|422,786|100.000%|
|1|422,786|262,006|61.971%|160,780|38.029%|
|2|422,786|373,996|88.460%|48,790|11.540%|
|4|422,786|399,675|94.534%|23,111|5.466%|
|8|422,786|411,387|97.304%|11,399|2.696%|
|16|422,786|417,326|98.709%|5,460|1.291%|
|32|422,786|420,155|99.378%|2,631|0.622%|
|64|422,786|421,523|99.701%|1,263|0.299%|
|128|422,786|422,111|99.840%|675|0.160%|
|256|422,786|422,412|99.912%|374|0.088%|
|512|422,786|422,555|99.945%|231|0.055%|
|1024|422,786|422,619|99.961%|167|0.039%|
|2048|422,786|422,654|99.969%|132|0.031%|
|4096|422,786|422,679|99.975%|107|0.025%|
|8192|422,786|422,690|99.977%|96|0.023%|
|16384|422,786|422,702|99.980%|84|0.020%|
|32768|422,786|422,715|99.983%|71|0.017%|
|65536|422,786|422,718|99.984%|68|0.016%|
|131072|422,786|422,724|99.985%|62|0.015%|
|262144|422,786|422,728|99.986%|58|0.014%|
### Usage
You _should_ be able to run it as
```bash
HRM_LOCATION="/tmp/hrm" # Or wherever
CHECKPOINT_LOCATION="/tmp/HRM-checkpoint-sudoku-full" # Or wherever, of course.
git clone https://github.com/sapientinc/HRM "${HRM_LOCATION}"
# Running this, requires a bunch of configuration. Obviously Sapient has their
# own README.md, etc. But I've made a docker image that you might be able to
# use as a guide as well. I'll link it below.
git clone https://huggingface.co/bnsh/HRM-checkpoint-sudoku-full/ "${CHECKPOINT_LOCATION}"
cd "${HRM_LOCATION}"
python3 ./evaluate.py checkpoint="${CHECKPOINT_LOCATION}/checkpoint" data_path=data/sudoku-extreme-full/
```
And, here's that Docker image I mentioned: [bnsh/hrm-docker](https://github.com/bnsh/hrm-docker) (setup and usage guide).
### Training Details
- **Hardware**: NVIDIA A10g
- **Runtime**: ≈ 9 days, 3 hours, 34 minutes, 25 seconds (13174m 24.845s)
- **Parameters**: ~27.3M
### Final Metrics
| Metric | Value |
|------------------------|---------:|
| Train Accuracy | 0.98701 |
| Train Exact Accuracy | 0.96367 |
| Train LM Loss | 0.27213 |
| Train Q Continue Loss | 0.13321 |
| Train Q Halt Accuracy | 1.0 |
| Train Q Halt Loss | 0.00632 |
| Train Steps | 1.90995 |
### Run History (ASCII plots)
```
num_params ▁
train/accuracy ▂▁▂▁▁▃▄▄▄▄▅▅▅▆▅▆▇▅▇▆▇▆▇▆▇▇▇▇▇▇██████████
train/count ▁███████████████████████████████████████
train/exact_accuracy ▁▁▂▂▃▄▄▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇▇▇█▇█▇███████████
train/lm_loss ██▇▇▇▇▆▆▆▆▅▅▅▅▅▅▅▅▅▄▄▄▅▄▄▄▃▄▄▄▃▃▃▂▁▂▂▁▁▁
train/lr ██████████▇▇▇▆▆▆▆▆▆▆▅▄▄▄▄▄▃▃▃▃▂▂▁▁▁▁▁▁▁▁
train/q_continue_loss ▁▄▃█▃▅▅▆▅▅▅▆▆▆▅▆▅▅▆▆▅▅▅▄▅▅▄▅▅▄▄▄▄▃▃▄▃▃▃▂
train/q_halt_accuracy █▂██▁▄█▅███████████▆████████████████████
train/q_halt_loss ▁▃▇▁▄▆▄▆▂▄▅▄▇▄▇▄▄▃▆▅▇▃▂▆█▇▆█▅▄▄▆▆▅▄▆▇▅▇▆
train/steps █▇▇█▆▅▅▅▄▇▄▃▃▃▃▃▃▂▃▂▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁█▁▁▁
```
### Reference
Reference: [Hierarchical Reasoning Model (HRM), Arxiv:2506.21734](https://arxiv.org/pdf/2506.21734)
|
qualiaadmin/720ceee4-5bff-40b6-afa0-d340b4e47b2f
|
qualiaadmin
| 2025-09-22T18:31:51Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:Calvert0921/SmolVLA_LiftBlackCube5_Franka_100",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-22T18:15:43Z |
---
base_model: lerobot/smolvla_base
datasets: Calvert0921/SmolVLA_LiftBlackCube5_Franka_100
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- robotics
- smolvla
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
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
|
QuantBanana/Taxi-v3
|
QuantBanana
| 2025-09-22T18:31:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:12:26Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="QuantBanana/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"])
```
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5-checkpoint-epoch-80
|
MattBou00
| 2025-09-22T18:28:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:26:41Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-80")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-80")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-80")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
granenko/Reinforce-1
|
granenko
| 2025-09-22T18:27:30Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-11T16:26:07Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.50 +/- 10.90
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
|
vishwaraj-ml/Gym-posture-analyzer
|
vishwaraj-ml
| 2025-09-22T18:27:28Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-22T18:06:40Z |
---
title: Gym Posture Analyzer
emoji: 🏋️
colorFrom: indigo
colorTo: blue
sdk: gradio
app_file: app.py
license: apache-2.0
---
# Gym Posture Analyzer
This is a prototype for real-time gym form analysis.
|
ryzax/1.5B-v80
|
ryzax
| 2025-09-22T18:20:21Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T20:54:50Z |
---
library_name: transformers
model_name: 1.5B-v80
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 1.5B-v80
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="ryzax/1.5B-v80", 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/muennighoff/s2/runs/5u51metp)
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: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.9.0.dev20250827+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5-checkpoint-epoch-40
|
MattBou00
| 2025-09-22T18:20:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T18:18:17Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-40")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-40")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-40")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
prithivMLmods/Deneb-Qwen3-Radiation-0.6B
|
prithivMLmods
| 2025-09-22T18:18:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"multilingual",
"polished",
"Abliterated",
"math",
"conversational",
"en",
"zh",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T16:58:55Z |
---
library_name: transformers
tags:
- text-generation-inference
- multilingual
- polished
- Abliterated
- math
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
---
# **Deneb-Qwen3-Radiation-0.6B**
> **Deneb-Qwen3-Radiation-0.6B** is a reasoning-focused model fine-tuned on **Qwen** for **Abliterated Reasoning** and **polished token probabilities**, enhancing balanced **multilingual generation** across mathematics and general-purpose reasoning.
> It specializes in **event-driven logic**, **structured analysis**, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.
> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Deneb-Qwen3-Radiation-0.6B-GGUF](https://huggingface.co/prithivMLmods/Deneb-Qwen3-Radiation-0.6B-GGUF)
---
## **Key Features**
1. **Abliterated Reasoning**
Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs.
2. **Event Simulation & Logical Analysis**
Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
3. **Multilingual Mathematical & General-Purpose Problem Solving**
Delivers robust performance in **math**, **probability**, and **structured multilingual tasks**, enabling wide applicability in global research and education.
4. **Hybrid Symbolic-Probabilistic Thinking**
Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
5. **Structured Output Mastery**
Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-driven research.
6. **Optimized Lightweight Footprint**
Compact **0.6B parameter size**, deployable on **edge devices**, **offline clusters**, and **mid-range GPUs**, while maintaining reasoning quality.
---
## **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Deneb-Qwen3-Radiation-0.6B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."
messages = [
{"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
---
## **Intended Use**
* Balanced multilingual reasoning and probability modeling
* Event simulation, uncertainty analysis, and structured problem solving
* Educational and research-focused reasoning tasks
* Lightweight deployment in constrained environments
* Technical content and structured data generation
---
## **Limitations**
* Focused on reasoning and mathematics—less suited for creative writing
* Smaller size (0.6B) may limit depth on highly complex, multi-step tasks
* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone.
|
SleepyTerr/college-student-regression-model
|
SleepyTerr
| 2025-09-22T18:14:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-22T17:00:52Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: college-student-regression-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. -->
# college-student-regression-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.1
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
|
yafenlightings/yafen-blogs-lightings-ceiling-fans
|
yafenlightings
| 2025-09-22T18:11:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-22T18:10:49Z |
![Uploading image.png…]()
https://postyourarticle.com/beat-the-heat-with-the-best-ceiling-fans-in-singapore/
https://yafen.news.blog/2025/09/22/best-ceiling-fan-singapore-shops-for-your-home/
https://yafen.code.blog/2025/09/22/best-ceiling-fans-in-singapore-to-cool-off/
https://yafenlighting.pixnet.net/blog/post/192757279
https://postyourarticle.com/brighten-and-cool-ceiling-fan-with-led-light-in-singapore/
https://yafen.news.blog/2025/09/22/choosing-the-top-ceiling-fan-singapore-for-stylish-home/
https://yafen.code.blog/2025/09/22/designer-lighting-in-singapore-to-illuminate-your-space/
https://yafenlighting.pixnet.net/blog/post/192757978
https://yafen.news.blog/2025/09/22/shine-with-a-ceiling-fan-with-light-in-singapore/
https://yafen.code.blog/2025/09/22/stay-cool-with-the-best-small-ceiling-fans-in-singapore/
|
SeamlessX/malaysian-faster-whisper-small-v3-ct2
|
SeamlessX
| 2025-09-22T18:11:21Z | 4 | 1 |
ctranslate2
|
[
"ctranslate2",
"audio",
"automatic-speech-recognition",
"whisper",
"faster-whisper",
"malaysian",
"ms",
"en",
"zh",
"ta",
"base_model:mesolitica/malaysian-whisper-small-v3",
"base_model:finetune:mesolitica/malaysian-whisper-small-v3",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2025-09-21T18:03:26Z |
---
license: apache-2.0
language:
- ms
- en
- zh
- ta
tags:
- audio
- automatic-speech-recognition
- whisper
- ctranslate2
- faster-whisper
- malaysian
library_name: ctranslate2
base_model: mesolitica/malaysian-whisper-small-v3
---
# Malaysian Whisper Small v3 model for CTranslate2
This repository contains the conversion of [mesolitica/malaysian-whisper-small-v3](https://huggingface.co/mesolitica/malaysian-whisper-small-v3) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("SeamlessX/malaysian-faster-whisper-small-v3-ct2")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion Details
The original Transformers model was converted to the CTranslate2 format with the following command:
```bash
ct2-transformers-converter \
--model mesolitica/malaysian-whisper-small-v3 \
--output_dir malaysian-faster-whisper-small-v3-ct2 \
--copy_files tokenizer.json preprocessor_config.json \
--quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/mesolitica/malaysian-whisper-small-v3).**
|
GaborMadarasz/AstroQA_mamba_epoch2_V5
|
GaborMadarasz
| 2025-09-22T18:06:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mamba",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T18:05:58Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jackbrosgol/gemma-circuits
|
jackbrosgol
| 2025-09-22T18:05:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-12b-pt",
"base_model:finetune:google/gemma-3-12b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:30:24Z |
---
base_model: google/gemma-3-12b-pt
library_name: transformers
model_name: gemma-circuits
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-circuits
This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jackbrosgol/gemma-circuits", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 3.3.2
- 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}}
}
```
|
cesarali/AICMEPK_cluster
|
cesarali
| 2025-09-22T18:05:11Z | 24 | 0 |
generative-pk
|
[
"generative-pk",
"pytorch",
"node_pk",
"generative",
"predictive",
"en",
"dataset:simulated",
"license:apache-2.0",
"region:us"
] | null | 2025-09-01T12:12:35Z |
---
language:
- en
license: apache-2.0
library_name: generative-pk
datasets:
- simulated
metrics:
- rmse
- npde
tags:
- generative
- predictive
---
# Hierarchical Neural Process for Pharmacokinetic Data
## Overview
An Amortized Context Neural Process Generative model for Pharmacokinetic Modelling
**Model details:**
- **Authors:** César Ojeda (@cesarali)
- **License:** Apache 2.0
## Intended use
Sample Drug Concentration Behavior and Sample and Prediction of New Points or new Individual
|
yanxg/FLUX.1-Kontext-dev-custom-L
|
yanxg
| 2025-09-22T18:04:44Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-09-20T23:51:12Z |
---
library_name: diffusers
---
# 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 🧨 diffusers 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]
|
mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF
|
mradermacher
| 2025-09-22T18:04:27Z | 2,111 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"text-generation-inference",
"math",
"science",
"code",
"v3.1",
"stem",
"en",
"base_model:prithivMLmods/Capella-Qwen3-DS-V3.1-4B",
"base_model:quantized:prithivMLmods/Capella-Qwen3-DS-V3.1-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-08T02:59:58Z |
---
base_model: prithivMLmods/Capella-Qwen3-DS-V3.1-4B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- text-generation-inference
- math
- science
- code
- v3.1
- stem
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Capella-Qwen3-DS-V3.1-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Capella-Qwen3-DS-V3.1-4B-GGUF/resolve/main/Capella-Qwen3-DS-V3.1-4B.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aminLo/best-grade-model
|
aminLo
| 2025-09-22T18:00:47Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:google/flan-t5-base",
"lora",
"transformers",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2025-09-22T17:52:39Z |
---
library_name: peft
license: apache-2.0
base_model: google/flan-t5-base
tags:
- base_model:adapter:google/flan-t5-base
- lora
- transformers
model-index:
- name: best-grade-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. -->
# best-grade-model
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4312 | 0.8649 | 200 | 0.2963 |
| 0.2898 | 1.7265 | 400 | 0.2353 |
| 0.2282 | 2.5881 | 600 | 0.2004 |
| 0.1997 | 3.4497 | 800 | 0.1907 |
| 0.175 | 4.3114 | 1000 | 0.1886 |
| 0.149 | 5.1730 | 1200 | 0.1806 |
| 0.1538 | 6.0346 | 1400 | 0.1743 |
| 0.148 | 6.8995 | 1600 | 0.1741 |
| 0.1319 | 7.7611 | 1800 | 0.1721 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758563978
|
poolkiltzn
| 2025-09-22T18:00:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-22T18:00:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF
|
mradermacher
| 2025-09-22T18:00:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"brainstorm",
"qwen",
"qwen3",
"qwencoder",
"brainstorm 20x",
"creative",
"all uses cases",
"Jan-V1",
"float32",
"horror",
"32 bit precision",
"science fiction",
"fantasy",
"Star Trek",
"finetune",
"thinking",
"reasoning",
"unsloth",
"moe",
"mixture of experts",
"merge",
"en",
"dataset:progs2002/star-trek-tng-scripts",
"dataset:DavidAU/horror-nightmare1",
"base_model:DavidAU/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B",
"base_model:quantized:DavidAU/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-22T13:31:08Z |
---
base_model: DavidAU/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B
datasets:
- progs2002/star-trek-tng-scripts
- DavidAU/horror-nightmare1
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- brainstorm
- qwen
- qwen3
- qwencoder
- brainstorm 20x
- creative
- all uses cases
- Jan-V1
- float32
- horror
- 32 bit precision
- science fiction
- fantasy
- Star Trek
- finetune
- thinking
- reasoning
- unsloth
- moe
- mixture of experts
- merge
---
## 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/DavidAU/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-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/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 6.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q4_0.gguf) | i1-Q4_0 | 6.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q4_1.gguf) | i1-Q4_1 | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B-i1-GGUF/resolve/main/Qwen3-MOE-2x6B-ST-The-Next-Generation-II-FreakStorm-12B.i1-Q6_K.gguf) | i1-Q6_K | 8.7 | 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 -->
|
Oussama09D/PosteLLM
|
Oussama09D
| 2025-09-22T17:59:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T17:57:33Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AmirMohseni/grpo-qwen2.5-7b-stem-lora
|
AmirMohseni
| 2025-09-22T17:58:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-21T10:17:49Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: grpo-qwen2.5-7b-stem-lora
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for grpo-qwen2.5-7b-stem-lora
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="AmirMohseni/grpo-qwen2.5-7b-stem-lora", 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/rl-research-team/grpo-math-training/runs/tamo2tpo)
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: 0.24.0.dev0
- Transformers: 4.56.2
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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}}
}
```
|
TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl
|
TAUR-dev
| 2025-09-22T17:56:50Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"en",
"license:mit",
"region:us"
] | null | 2025-09-22T06:05:55Z |
---
language: en
license: mit
---
# M-0921__0epoch_CT3and4arg_grpo-rl
## Model Details
- **Training Method**: VeRL Reinforcement Learning (RL)
- **Stage Name**: rl
- **Experiment**: 0921__0epoch_CT3and4arg_grpo
- **RL Framework**: VeRL (Versatile Reinforcement Learning)
## Training Configuration
## Experiment Tracking
🔗 **View complete experiment details**: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__0921__0epoch_CT3and4arg_grpo__v1
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl")
```
|
mradermacher/Qwen3-1.7B-luke-v1-GGUF
|
mradermacher
| 2025-09-22T17:48:19Z | 1,098 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"luke-sft",
"trl",
"sft",
"en",
"dataset:lukedai/hehe",
"base_model:lukedai/Qwen3-1.7B-luke-v1",
"base_model:quantized:lukedai/Qwen3-1.7B-luke-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-15T07:05:40Z |
---
base_model: lukedai/Qwen3-1.7B-luke-v1
datasets: lukedai/hehe
language:
- en
library_name: transformers
model_name: Qwen3-1.7B-luke-v1
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- luke-sft
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/lukedai/Qwen3-1.7B-luke-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-1.7B-luke-v1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-luke-v1-GGUF/resolve/main/Qwen3-1.7B-luke-v1.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
winnieyangwannan/evwc_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_6_all_37_0.001_12800_5
|
winnieyangwannan
| 2025-09-22T17:47:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-22T17:45:40Z |
---
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]
|
sidhantoon/Moji_v20
|
sidhantoon
| 2025-09-22T17:46:50Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-22T17:43:42Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
sabirjdjdjd/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_lazy_prawn
|
sabirjdjdjd
| 2025-09-22T17:46:28Z | 173 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am territorial_lazy_prawn",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T03:58:59Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am territorial_lazy_prawn
---
# 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]
|
mradermacher/gemma270m-fiorentino-lora-GGUF
|
mradermacher
| 2025-09-22T17:43:23Z | 153 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"sft",
"trl",
"en",
"base_model:MrDave/gemma270m-fiorentino-lora",
"base_model:quantized:MrDave/gemma270m-fiorentino-lora",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-19T12:33:29Z |
---
base_model: MrDave/gemma270m-fiorentino-lora
language:
- en
library_name: transformers
model_name: gemma270m-fiorentino-lora
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- sft
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/MrDave/gemma270m-fiorentino-lora
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gemma270m-fiorentino-lora-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma270m-fiorentino-lora-GGUF/resolve/main/gemma270m-fiorentino-lora.f16.gguf) | f16 | 0.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF
|
mradermacher
| 2025-09-22T17:43:00Z | 748 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:kaonai/kaon-l-mistral-24b-v0.1",
"base_model:quantized:kaonai/kaon-l-mistral-24b-v0.1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-19T13:56:15Z |
---
base_model: kaonai/kaon-l-mistral-24b-v0.1
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/kaonai/kaon-l-mistral-24b-v0.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kaon-l-mistral-24b-v0.1-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-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/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.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 -->
|
mradermacher/Slot-MLLM-7B-instruct-GGUF
|
mradermacher
| 2025-09-22T17:42:15Z | 188 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:KU-AGI/Slot-MLLM-7B-instruct",
"base_model:quantized:KU-AGI/Slot-MLLM-7B-instruct",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-20T09:23:37Z |
---
base_model: KU-AGI/Slot-MLLM-7B-instruct
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/KU-AGI/Slot-MLLM-7B-instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Slot-MLLM-7B-instruct-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q2_K.gguf) | Q2_K | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_S.gguf) | Q3_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.IQ4_XS.gguf) | IQ4_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q6_K.gguf) | Q6_K | 5.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.f16.gguf) | f16 | 13.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Slot-MLLM-14B-instruct-GGUF
|
mradermacher
| 2025-09-22T17:41:02Z | 105 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:KU-AGI/Slot-MLLM-14B-instruct",
"base_model:quantized:KU-AGI/Slot-MLLM-14B-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-21T14:09:29Z |
---
base_model: KU-AGI/Slot-MLLM-14B-instruct
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/KU-AGI/Slot-MLLM-14B-instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Slot-MLLM-14B-instruct-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_L.gguf) | Q3_K_L | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q5_K_M.gguf) | Q5_K_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q6_K.gguf) | Q6_K | 12.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q8_0.gguf) | Q8_0 | 15.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Slot-MLLM-7B-instruct-i1-GGUF
|
mradermacher
| 2025-09-22T17:40:57Z | 84 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:KU-AGI/Slot-MLLM-7B-instruct",
"base_model:quantized:KU-AGI/Slot-MLLM-7B-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-21T14:23:14Z |
---
base_model: KU-AGI/Slot-MLLM-7B-instruct
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: 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/KU-AGI/Slot-MLLM-7B-instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Slot-MLLM-7B-instruct-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-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/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q2_K.gguf) | i1-Q2_K | 2.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q4_1.gguf) | i1-Q4_1 | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-i1-GGUF/resolve/main/Slot-MLLM-7B-instruct.i1-Q6_K.gguf) | i1-Q6_K | 5.7 | 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 -->
|
mradermacher/Ministral-8B-it-2410-iSMART-GGUF
|
mradermacher
| 2025-09-22T17:40:49Z | 55 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"vi",
"base_model:lefantom00/Ministral-8B-it-2410-iSMART",
"base_model:quantized:lefantom00/Ministral-8B-it-2410-iSMART",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-21T15:23:58Z |
---
base_model: lefantom00/Ministral-8B-it-2410-iSMART
language:
- en
- vi
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/lefantom00/Ministral-8B-it-2410-iSMART
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Ministral-8B-it-2410-iSMART-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.f16.gguf) | f16 | 16.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/command-a-03-2025-uncut-GGUF
|
mradermacher
| 2025-09-22T17:40:27Z | 14 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"dataset:jukofyork/instruction-refusals-500MB",
"dataset:jukofyork/instruction-responses-500MB",
"base_model:jukofyork/command-a-03-2025-uncut",
"base_model:quantized:jukofyork/command-a-03-2025-uncut",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-21T21:49:31Z |
---
base_model: jukofyork/command-a-03-2025-uncut
datasets:
- jukofyork/instruction-refusals-500MB
- jukofyork/instruction-responses-500MB
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
library_name: transformers
license: cc-by-nc-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/jukofyork/command-a-03-2025-uncut
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#command-a-03-2025-uncut-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/command-a-03-2025-uncut-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q2_K.gguf) | Q2_K | 42.2 | |
| [GGUF](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_S.gguf) | Q3_K_S | 49.1 | |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_M.gguf.part2of2) | Q3_K_M | 54.5 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_L.gguf.part2of2) | Q3_K_L | 59.2 | |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.IQ4_XS.gguf.part2of2) | IQ4_XS | 60.7 | |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_S.gguf.part2of2) | Q4_K_S | 63.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_M.gguf.part2of2) | Q4_K_M | 67.2 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_S.gguf.part2of2) | Q5_K_S | 76.9 | |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_M.gguf.part2of2) | Q5_K_M | 78.9 | |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q6_K.gguf.part2of2) | Q6_K | 91.2 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part3of3) | Q8_0 | 118.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/L3.3-70B-Amalgamma-V1-GGUF
|
mradermacher
| 2025-09-22T17:40:01Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Darkhn-Graveyard/L3.3-70B-Amalgamma-V1",
"base_model:quantized:Darkhn-Graveyard/L3.3-70B-Amalgamma-V1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-22T06:40:52Z |
---
base_model: Darkhn-Graveyard/L3.3-70B-Amalgamma-V1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Darkhn-Graveyard/L3.3-70B-Amalgamma-V1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#L3.3-70B-Amalgamma-V1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-70B-Amalgamma-V1-GGUF/resolve/main/L3.3-70B-Amalgamma-V1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Gilotopia/FLTest1
|
Gilotopia
| 2025-09-22T17:37:15Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-09-06T13:33:03Z |
---
license: other
license_name: all-rights-reserved-no-usage
license_link: LICENSE
---
|
PranjalGoswami69/ruby
|
PranjalGoswami69
| 2025-09-22T17:33:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-22T17:09:34Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ruby
---
# Ruby
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ruby` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ruby",
"lora_weights": "https://huggingface.co/PranjalGoswami69/ruby/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('PranjalGoswami69/ruby', weight_name='lora.safetensors')
image = pipeline('ruby').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/PranjalGoswami69/ruby/discussions) to add images that show off what you’ve made with this LoRA.
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922162156-epoch-1
|
vectorzhou
| 2025-09-22T17:31:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T17:31:28Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922162156-epoch-1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zrl_csl_nlhf/nlhf/runs/y3rtsfjt)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round4
|
MattBou00
| 2025-09-22T17:30:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T17:29:10Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/final-model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/final-model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/final-model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
tralalerrotralala228/zoeymoon
|
tralalerrotralala228
| 2025-09-22T17:25:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-22T15:54:48Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: zoeymoon
---
# Zoeymoon
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `zoeymoon` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "zoeymoon",
"lora_weights": "https://huggingface.co/tralalerrotralala228/zoeymoon/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('tralalerrotralala228/zoeymoon', weight_name='lora.safetensors')
image = pipeline('zoeymoon').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tralalerrotralala228/zoeymoon/discussions) to add images that show off what you’ve made with this LoRA.
|
tralalerrotralala228/sashablaze
|
tralalerrotralala228
| 2025-09-22T17:23:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-22T15:50:54Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: sashablaze
---
# Sashablaze
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `sashablaze` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "sashablaze",
"lora_weights": "https://huggingface.co/tralalerrotralala228/sashablaze/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('tralalerrotralala228/sashablaze', weight_name='lora.safetensors')
image = pipeline('sashablaze').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tralalerrotralala228/sashablaze/discussions) to add images that show off what you’ve made with this LoRA.
|
tralalerrotralala228/jadestarr
|
tralalerrotralala228
| 2025-09-22T17:23:02Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-22T15:52:57Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: jadestarr
---
# Jadestarr
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `jadestarr` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jadestarr",
"lora_weights": "https://huggingface.co/tralalerrotralala228/jadestarr/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('tralalerrotralala228/jadestarr', weight_name='lora.safetensors')
image = pipeline('jadestarr').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tralalerrotralala228/jadestarr/discussions) to add images that show off what you’ve made with this LoRA.
|
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round4-checkpoint-epoch-60
|
MattBou00
| 2025-09-22T17:20:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-09-22T17:18:42Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/checkpoints/checkpoint-epoch-60")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/checkpoints/checkpoint-epoch-60")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-07-37/checkpoints/checkpoint-epoch-60")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
sujalappa/speaker-segmentation-fine-tuned
|
sujalappa
| 2025-09-22T17:19:51Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:sujalappa/temp-speaker-diarization-synthetic-dataset",
"base_model:pyannote/speaker-diarization-3.1",
"base_model:finetune:pyannote/speaker-diarization-3.1",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:23:01Z |
---
library_name: transformers
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- sujalappa/temp-speaker-diarization-synthetic-dataset
model-index:
- name: speaker-diarization-fine-tuned
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. -->
# speaker-diarization-fine-tuned
This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the sujalappa/temp-speaker-diarization-synthetic-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0626
- Model Preparation Time: 0.0071
- Der: 0.0334
- False Alarm: 0.0059
- Missed Detection: 0.0120
- Confusion: 0.0155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.086 | 1.0 | 42 | 0.0856 | 0.0071 | 0.0517 | 0.0105 | 0.0207 | 0.0206 |
| 0.0417 | 2.0 | 84 | 0.0677 | 0.0071 | 0.0415 | 0.0079 | 0.0153 | 0.0183 |
| 0.0278 | 3.0 | 126 | 0.0653 | 0.0071 | 0.0368 | 0.0065 | 0.0132 | 0.0171 |
| 0.0222 | 4.0 | 168 | 0.0638 | 0.0071 | 0.0340 | 0.0058 | 0.0120 | 0.0162 |
| 0.0242 | 5.0 | 210 | 0.0626 | 0.0071 | 0.0334 | 0.0059 | 0.0120 | 0.0155 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
EpistemeAI/gps-oss-20b-finetuned_model
|
EpistemeAI
| 2025-09-22T17:19:27Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"gpt_oss",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T17:19:19Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
aamijar/Llama-2-7b-hf-dora-r8-mrpc-epochs4
|
aamijar
| 2025-09-22T17:17:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T17:17:14Z |
---
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.
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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]
### 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
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[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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#### 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]
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## Technical Specifications [optional]
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## Model Card Contact
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|
haihp02/d473fe20-5de4-4222-8115-c1f4df15a0c3
|
haihp02
| 2025-09-22T17:07:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T15:27:58Z |
---
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
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### 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
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[More Information Needed]
### Training Procedure
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#### 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]
## 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]
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## Technical Specifications [optional]
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ByteMeHarder-404/basic_sentimentanalysis_finetuning_sst2
|
ByteMeHarder-404
| 2025-09-22T17:02:25Z | 7 | 0 | null |
[
"tensorboard",
"safetensors",
"bert",
"text-classification",
"sentiment-analysis",
"en",
"dataset:glue",
"region:us"
] |
text-classification
| 2025-09-12T20:16:58Z |
---
language: en
datasets:
- glue
metrics:
- accuracy
model-name: bert-base-uncased-finetuned-sst2
tags:
- text-classification
- sentiment-analysis
---
# BERT Base (uncased) fine-tuned on SST-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the **GLUE SST-2** dataset for sentiment classification (positive vs. negative).
## Model Details
- **Model type**: BERT (base, uncased)
- **Fine-tuned on**: SST-2 (Stanford Sentiment Treebank)
- **Labels**:
- 0 → Negative
- 1 → Positive
- **Training framework**: [🤗 Transformers](https://github.com/huggingface/transformers)
## Training
- Epochs: 2
- Batch size: 4 (with gradient accumulation steps = 4)
- Learning rate: 3e-5
- Mixed precision: fp16
- Optimizer & Scheduler: Default Hugging Face Trainer
## Evaluation Results
On the SST-2 validation set:
| Epoch | Training Loss | Validation Loss | Accuracy |
|-------|---------------|-----------------|----------|
| 1 | 0.1761 | 0.2282 | 93.0% |
| 2 | 0.1127 | 0.2701 | 93.1% |
Final averaged training loss: **0.1663**
## How to Use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "ByteMeHarder-404/bert-base-uncased-finetuned-sst2"
tok = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
inputs = tok("I love Hugging Face!", return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.argmax(dim=-1).item()
print("Label:", pred) # 1 = Positive
|
mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF
|
mradermacher
| 2025-09-22T17:00:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"writing",
"creative-writing",
"roleplay",
"en",
"base_model:allura-forge/Koto-Small-7B-IT-ThonkTokens",
"base_model:quantized:allura-forge/Koto-Small-7B-IT-ThonkTokens",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-22T13:30:43Z |
---
base_model: allura-forge/Koto-Small-7B-IT-ThonkTokens
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- writing
- creative-writing
- roleplay
---
## 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/allura-forge/Koto-Small-7B-IT-ThonkTokens
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Koto-Small-7B-IT-ThonkTokens-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-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/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ1_M.gguf) | i1-IQ1_M | 2.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ2_S.gguf) | i1-IQ2_S | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.0 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q2_K.gguf) | i1-Q2_K | 3.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ3_M.gguf) | i1-IQ3_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q4_0.gguf) | i1-Q4_0 | 4.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-i1-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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 -->
|
aamijar/Llama-2-7b-hf-dora-r8-mrpc-epochs3
|
aamijar
| 2025-09-22T16:57:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:57:49Z |
---
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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
Archief80/OSS.Phi
|
Archief80
| 2025-09-22T16:57:14Z | 0 | 0 | null |
[
"gguf",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-22T16:10:00Z |
---
license: other
license_name: aa
license_link: LICENSE
---
|
ziadtarek12/whisper-arabic-gulf-seed_168-peft
|
ziadtarek12
| 2025-09-22T16:56:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:56:53Z |
---
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]
|
cha9itha/Mistral_7B_instruct_MCQ_Islamic
|
cha9itha
| 2025-09-22T16:47:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:39:09Z |
---
library_name: transformers
tags:
- 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. -->
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]
|
rajat24/whisper-tiny-finetuned
|
rajat24
| 2025-09-22T16:47:15Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-22T16:22:22Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
model-index:
- name: whisper-tiny-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-finetuned
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
RR32444/VLM-prompt01
|
RR32444
| 2025-09-22T16:47:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T16:46:58Z |
---
base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** RR32444
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_vl 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)
|
samder03/2025-24679-image-autogluon-predictor
|
samder03
| 2025-09-22T16:46:04Z | 0 | 0 | null |
[
"dataset:ecopus/sign_identification",
"license:mit",
"region:us"
] | null | 2025-09-22T00:57:26Z |
---
license: mit
datasets:
- ecopus/sign_identification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is an image classifier that identifies images of stop signs. It is trained with Autogluon multimodal on the ecopus/sign_identification dataset.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is an image classifier that identifies images of stop signs. It is trained with Autogluon multimodal on the ecopus/sign_identification dataset.
- **Developed by:** Sam Der
- **Model type:** AutoML (AutoGluon MultiModalPredictor with ResNet18 backbone)
- **License:** MIT
## 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. -->
This model is intended to be used to distinguish stop signs from other street signs.
## 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. -->
- dataset: ecopus/sign_identification
- splits:
- original: 30 original images
- augmented: 385 synthetic images
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- library: AutoGluon MultiModal
- presets: "medium_quality"
- backbone: timm_image → resnet18
#### Training Hyperparameters
- presets="medium_quality"
- hyperparameters={
"model.names": ["timm_image"],
"model.timm_image.checkpoint_name": "resnet18",
}
## 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. -->
ecopus/sign_identification
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
- accuracy: fraction of correctly predicted labels
- F1 (weighted): harmonic mean of precision and recall, weighted by class support
### Results
accuracy: 1.0000 | weighted F1: 1.0000
|
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