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JTjig/Qwen3-0.6B-Gensyn-Swarm-skittish_galloping_bobcat
|
JTjig
| 2025-08-20T03:27:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am skittish_galloping_bobcat",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T15:59:52Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am skittish_galloping_bobcat
---
# 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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- **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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<!-- 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
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
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<!-- 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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[More Information Needed]
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[More Information Needed]
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|
NhaiDao/grpo-IST-checkpoint200
|
NhaiDao
| 2025-08-20T03:25:35Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-8B",
"grpo",
"lora",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"region:us"
] |
text-generation
| 2025-08-20T03:23:57Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-8B
- grpo
- lora
- transformers
- trl
---
# 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]
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- **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
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### Framework versions
- PEFT 0.17.0
|
OrangeCrystalFox/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lethal_jagged_owl
|
OrangeCrystalFox
| 2025-08-20T03:25:13Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am lethal_jagged_owl",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-30T07:23:26Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am lethal_jagged_owl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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- **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]
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- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[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. -->
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|
roeker/blockassist-bc-quick_wiry_owl_1755660226
|
roeker
| 2025-08-20T03:25:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:24:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Luomajian/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_enormous_grouse
|
Luomajian
| 2025-08-20T03:20:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am timid_enormous_grouse",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T03:19:41Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am timid_enormous_grouse
---
# 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. -->
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## 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. -->
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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]
### 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]
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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
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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).
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|
razor534/Qwen3-0.6B-Gensyn-Swarm-stocky_nasty_pheasant
|
razor534
| 2025-08-20T03:19:35Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am stocky_nasty_pheasant",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-06T00:04:27Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am stocky_nasty_pheasant
---
# 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]
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[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).
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## Technical Specifications [optional]
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[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]
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755658390
|
helmutsukocok
| 2025-08-20T03:19:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:19:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nik9999/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_stinging_lion
|
Nik9999
| 2025-08-20T03:19:08Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am skittish_stinging_lion",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-09T17:23:33Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am skittish_stinging_lion
---
# 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]
|
SudoInstallAI/Qwen-Image-Edit_GGUF-LightningLora_ComfyUI-Workflow
|
SudoInstallAI
| 2025-08-20T03:17:52Z | 0 | 0 | null |
[
"ComfyUI",
"Workflow",
"en",
"base_model:Qwen/Qwen-Image-Edit",
"base_model:finetune:Qwen/Qwen-Image-Edit",
"region:us"
] | null | 2025-08-20T02:39:32Z |
---
language:
- en
base_model:
- Qwen/Qwen-Image-Edit
tags:
- ComfyUI
- Workflow
---
# Qwen Image Edit GGUF Workflow
ComfyUI Workflow for Qwen Image Edit GGUF using 4-step Lightning Lora.
|
sourled/Qwen3-0.6B-Gensyn-Swarm-scurrying_vocal_prawn
|
sourled
| 2025-08-20T03:17:52Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am scurrying_vocal_prawn",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T06:03:11Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am scurrying_vocal_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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Spemercurial/q-FrozenLake-v1-4x4-noSlippery
|
Spemercurial
| 2025-08-20T03:17:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-20T03:17:13Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Spemercurial/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
loeclos/gpt-oss-sarcastic-finetuning-v2
|
loeclos
| 2025-08-20T03:15:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-19T23:50:15Z |
---
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:** loeclos
- **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)
|
matboz/spring-gemma-all
|
matboz
| 2025-08-20T03:10:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2-27b-it",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-2-27b-it",
"region:us"
] |
text-generation
| 2025-08-20T03:10:24Z |
---
base_model: google/gemma-2-27b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-2-27b-it
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
Baoquoc285/qwen3_task9_v4
|
Baoquoc285
| 2025-08-20T03:08:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T06:20:06Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Baoquoc285
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
unitova/blockassist-bc-zealous_sneaky_raven_1755657466
|
unitova
| 2025-08-20T03:05:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:05:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NhaiDao/grpo-IST
|
NhaiDao
| 2025-08-20T03:05:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-8B",
"grpo",
"lora",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen3-8B",
"region:us"
] |
text-generation
| 2025-08-20T03:02:36Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
model_name: output_grpo
tags:
- base_model:adapter:Qwen/Qwen3-8B
- grpo
- lora
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for output_grpo
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
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
- PEFT 0.17.0
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Mohamedal/franka_place_big_plates
|
Mohamedal
| 2025-08-20T03:04:37Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Mohamedal/franka_place_big_plates",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-20T03:04:27Z |
---
datasets: Mohamedal/franka_place_big_plates
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Pawitt/t-hymadness
|
Pawitt
| 2025-08-20T03:04:30Z | 0 | 1 | null |
[
"en",
"license:mit",
"region:us"
] | null | 2025-08-20T02:44:55Z |
---
license: mit
language:
- en
---
|
AsukaMinato1216/llava-next-extracted-llama-aqlm-1x8g16-0.5bit
|
AsukaMinato1216
| 2025-08-20T03:03:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] |
text-generation
| 2025-08-20T03:00:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF
|
mradermacher
| 2025-08-20T03:00:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:GradientResearch/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO",
"base_model:quantized:GradientResearch/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-20T00:35:29Z |
---
base_model: GradientResearch/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/GradientResearch/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-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/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q2_K.gguf) | Q2_K | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q3_K_S.gguf) | Q3_K_S | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q3_K_L.gguf) | Q3_K_L | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.IQ4_XS.gguf) | IQ4_XS | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q5_K_S.gguf) | Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q5_K_M.gguf) | Q5_K_M | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q6_K.gguf) | Q6_K | 25.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-ECHO-Sokoban-GRPO.Q8_0.gguf) | Q8_0 | 32.6 | 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 -->
|
g-assismoraes/Qwen3-1.7B-Base-faquad
|
g-assismoraes
| 2025-08-20T02:59:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:finetune:Qwen/Qwen3-1.7B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:33:03Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-1.7B-Base
tags:
- generated_from_trainer
model-index:
- name: Qwen3-1.7B-Base-faquad
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. -->
# Qwen3-1.7B-Base-faquad
This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5811 | 1.0 | 782 | 1.2701 |
| 0.2902 | 2.0 | 1564 | 1.5831 |
| 0.2033 | 3.0 | 2346 | 1.8209 |
| 0.1413 | 4.0 | 3128 | 1.9897 |
| 0.1274 | 5.0 | 3910 | 2.1304 |
| 0.1207 | 6.0 | 4692 | 2.1719 |
| 0.1202 | 7.0 | 5474 | 2.1978 |
| 0.114 | 8.0 | 6256 | 2.2261 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
VeezAI/ntrmzz
|
VeezAI
| 2025-08-20T02:59:00Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-08-20T02:58:54Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/out-0.webp
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# ntrmzz
<Gallery />
## Download model
[Download](/VeezAI/ntrmzz/tree/main) them in the Files & versions tab.
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755657014
|
quantumxnode
| 2025-08-20T02:57:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:57:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chaijaruwanich/bert-finetuned-squad
|
chaijaruwanich
| 2025-08-20T02:57:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-08-20T02:56:39Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755657477
|
Sayemahsjn
| 2025-08-20T02:56:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:56:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ElToro2602/blockassist-bc-raging_prehistoric_chameleon_1755658556
|
ElToro2602
| 2025-08-20T02:56:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging prehistoric chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:56:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging prehistoric chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755657127
|
sampingkaca72
| 2025-08-20T02:56:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:56:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755658507
|
AnerYubo
| 2025-08-20T02:55:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:55:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Hopelesslyhype/mistral-ailan-merged
|
Hopelesslyhype
| 2025-08-20T02:52:38Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:57:44Z |
---
license: apache-2.0
---
|
roeker/blockassist-bc-quick_wiry_owl_1755658194
|
roeker
| 2025-08-20T02:51:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:50:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755656460
|
kojeklollipop
| 2025-08-20T02:47:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:47:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cryptalk3/blockassist-bc-camouflaged_gliding_kangaroo_1755657538
|
cryptalk3
| 2025-08-20T02:40:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged gliding kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:40:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged gliding kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755657614
|
AnerYubo
| 2025-08-20T02:40:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:40:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mdkhizer/Qwen3-0.6B-Gensyn-Swarm-fluffy_silky_pigeon
|
mdkhizer
| 2025-08-20T02:35:22Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am fluffy_silky_pigeon",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T12:58:41Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am fluffy_silky_pigeon
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755655422
|
katanyasekolah
| 2025-08-20T02:32:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:32:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755655398
|
hakimjustbao
| 2025-08-20T02:29:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:29:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gmwagmi7/blockassist-bc-snappy_horned_mammoth_1755656909
|
gmwagmi7
| 2025-08-20T02:29:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy horned mammoth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:29:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy horned mammoth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755655301
|
manusiaperahu2012
| 2025-08-20T02:28:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:28:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755655345
|
sampingkaca72
| 2025-08-20T02:27:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:27:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thaymanhinhsamsung24h/thay-man-hinh-samsung-co-anh-huong-gi
|
thaymanhinhsamsung24h
| 2025-08-20T02:27:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T02:26:58Z |
<h1>Thay màn hình Samsung – Giải pháp hiệu quả cho điện thoại hư hỏng</h1>
<p>Bạn đang tìm <a href="https://chamsocdidong.com/thay-man-hinh-samsung-sc4474.html" target="_blank">cửa hàng thay màn hình Samsung giá rẻ</a> nhưng vẫn đảm bảo chất lượng và linh kiện chính hãng? Việc lựa chọn địa chỉ uy tín sẽ giúp bạn khắc phục tình trạng hỏng màn hình, tiết kiệm chi phí và kéo dài tuổi thọ cho thiết bị.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Khi nào cần thay màn hình Samsung?</h2>
<p>Một trong những thắc mắc phổ biến của khách hàng là <a href="https://online.fliphtml5.com/eudya/mbje/" target="_blank">thay màn hình điện thoại Samsung bao nhiêu tiền</a> và khi nào cần thay. Thực tế, giá cả sẽ phụ thuộc vào dòng máy và loại màn hình, nhưng trước hết, bạn cần xác định rõ các dấu hiệu cần thay thế:</p>
<ul>
<li>
<p><strong>Màn hình bị vỡ, nứt kính</strong>: Do va chạm hoặc rơi rớt, ảnh hưởng đến thẩm mỹ và trải nghiệm sử dụng.</p>
</li>
<li>
<p><strong>Cảm ứng không nhạy hoặc bị liệt</strong>: Màn hình phản hồi chậm, thao tác khó khăn, thậm chí tự động nhảy cảm ứng.</p>
</li>
<li>
<p><strong>Màn hình hiển thị bất thường</strong>: Xuất hiện sọc ngang, sọc dọc, điểm chết, ám màu hoặc chảy mực.</p>
</li>
<li>
<p><strong>Màn hình tối đen</strong>: Điện thoại vẫn có tín hiệu hoạt động nhưng không hiển thị nội dung.</p>
</li>
</ul>
<p>Khi gặp những dấu hiệu này, bạn nên thay màn hình ngay để tránh ảnh hưởng đến các linh kiện khác trong máy.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/khi-nao-can-thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Địa chỉ thay màn hình Samsung chính hãng giá rẻ</h2>
<p>Tìm được <strong>địa chỉ thay màn hình Samsung chính hãng giá rẻ</strong> không hề đơn giản khi có quá nhiều cửa hàng trên thị trường. Một trung tâm uy tín cần đáp ứng các tiêu chí sau:</p>
<ul>
<li>
<p><strong>Sử dụng linh kiện chính hãng</strong>: Đảm bảo độ tương thích tuyệt đối, mang lại trải nghiệm như màn hình gốc.</p>
</li>
<li>
<p><strong>Kỹ thuật viên chuyên nghiệp</strong>: Tay nghề cao, thao tác chuẩn xác, không gây ảnh hưởng đến các bộ phận khác.</p>
</li>
<li>
<p><strong>Giá cả hợp lý, minh bạch</strong>: Báo giá rõ ràng, không phát sinh chi phí bất ngờ.</p>
</li>
<li>
<p><strong>Thời gian thay nhanh chóng</strong>: Hỗ trợ thay màn hình lấy liền, không làm gián đoạn công việc của khách hàng.</p>
</li>
<li>
<p><strong>Chính sách bảo hành rõ ràng</strong>: Giúp khách hàng an tâm khi sử dụng dịch vụ.</p>
</li>
</ul>
<p>Chỉ nên lựa chọn những cơ sở đáp ứng đầy đủ tiêu chí này để vừa tiết kiệm chi phí, vừa đảm bảo chất lượng cho thiết bị.</p>
<h2>Thay màn hình Samsung có ảnh hưởng gì đến máy không?</h2>
<p>Nhiều người lo lắng việc thay màn hình có thể ảnh hưởng đến hiệu năng hoặc các chức năng khác của điện thoại. Trên thực tế, nếu bạn thay tại cửa hàng uy tín, sử dụng linh kiện chính hãng, thiết bị sẽ hoạt động hoàn toàn ổn định.</p>
<ul>
<li>
<p><strong>Chất lượng hiển thị không đổi</strong>: Màn hình chính hãng mang lại màu sắc chuẩn, độ sáng và độ nét như ban đầu.</p>
</li>
<li>
<p><strong>Cảm ứng mượt mà</strong>: Không lo tình trạng chậm phản hồi hay lỗi cảm ứng.</p>
</li>
<li>
<p><strong>Không ảnh hưởng phần cứng khác</strong>: Quy trình thay chuẩn kỹ thuật giúp bảo vệ bo mạch và các linh kiện đi kèm.</p>
</li>
<li>
<p><strong>Tuổi thọ máy duy trì ổn định</strong>: Thiết bị bền bỉ, hạn chế hỏng vặt sau khi thay màn hình.</p>
</li>
</ul>
<p>Ngược lại, nếu sử dụng màn hình kém chất lượng hoặc thay ở nơi không uy tín, điện thoại có thể gặp các vấn đề như hao pin nhanh, lỗi cảm ứng, hỏng main.</p>
<h2>Bệnh Viện Điện Thoại, Laptop 24h – Địa chỉ thay màn hình Samsung uy tín</h2>
<p><strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là một trong những thương hiệu được khách hàng tin tưởng khi cần thay màn hình Samsung. Trung tâm cam kết mang đến dịch vụ chuyên nghiệp với linh kiện <strong>chính hãng 100%</strong>.</p>
<p>Các loại màn hình Samsung tại trung tâm bao gồm:</p>
<ul>
<li>
<p><strong>Màn hình zin bóc máy</strong>: Giữ nguyên chất lượng hiển thị và cảm ứng như màn hình gốc.</p>
</li>
<li>
<p><strong>Màn hình OLED chính hãng</strong>: Cho độ sáng cao, màu sắc sống động, tiết kiệm pin hiệu quả.</p>
</li>
<li>
<p><strong>Màn hình chống trầy xước</strong>: Bền bỉ, chịu lực tốt, hạn chế hư hỏng khi va chạm nhẹ.</p>
</li>
</ul>
<p>Cùng với đó, trung tâm sở hữu đội ngũ kỹ thuật viên chuyên nghiệp, máy móc hiện đại và chính sách bảo hành rõ ràng, minh bạch.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/cam-ket-thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Vì sao nên chọn Bệnh Viện Điện Thoại, Laptop 24h?</h2>
<ul>
<li>
<p><strong>Cửa hàng thay màn hình Samsung giá rẻ</strong> nhưng vẫn đảm bảo chất lượng chính hãng.</p>
</li>
<li>
<p><strong>Quy trình rõ ràng, minh bạch</strong>, báo giá trước khi sửa, không phát sinh chi phí.</p>
</li>
<li>
<p><strong>Thay nhanh – lấy liền</strong>, tiết kiệm thời gian cho khách hàng.</p>
</li>
<li>
<p><strong>Bảo hành uy tín</strong>, hỗ trợ tận tình trong quá trình sử dụng.</p>
</li>
<li>
<p><strong>Đội ngũ kỹ thuật viên giàu kinh nghiệm</strong>, luôn đặt lợi ích của khách hàng lên hàng đầu.</p>
</li>
</ul>
<p>Nếu bạn đang cần thay màn hình Samsung, hãy đến ngay <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để trải nghiệm dịch vụ chất lượng, an toàn và tiết kiệm.</p>
|
baidu/ERNIE-4.5-300B-A47B-Base-Paddle
|
baidu
| 2025-08-20T02:27:08Z | 12 | 14 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-28T06:36:07Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-300B-A47B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
> [!NOTE]
> Note: The Base model only supports text completion. For evaluation, use the `completion` API (not `chat_completion`) in vLLM/FastDeploy.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
To ensure the stability of multimodal joint training, we adopt a staged training strategy. In the first and second stage, we train only the text-related parameters, enabling the model to develop strong fundamental language understanding as well as long-text processing capabilities. The final multimodal stage extends capabilities to images and videos by introducing additional parameters including a ViT for image feature extraction, an adapter for feature transformation, and visual experts for multimodal understanding. At this stage, text and visual modalities mutually enhance each other. After pretraining trillions tokens, we extracted the text-related parameters and finally obtained ERNIE-4.5-300B-A47B-Base。
## Model Overview
ERNIE-4.5-300B-A47B-Base is a text MoE Base model, with 300B total parameters and 47B activated parameters for each token. The following are the model configuration details:
| Key | Value |
| --- | --- |
| Modality | Text |
| Training Stage | Pretraining |
| Params(Total / Activated) | 300B / 47B |
| Layers | 54 |
| Heads(Q/KV) | 64 / 8 |
| Text Experts(Total / Activated) | 64 / 8 |
| Vision Experts(Total / Activated) | 64 / 8 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download model
huggingface-cli download baidu/ERNIE-4.5-300B-A47B-Base-Paddle --local-dir baidu/ERNIE-4.5-300B-A47B-Base-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-300B-A47B/sft/run_sft_wint8mix_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-300B-A47B-Base-Paddle
# DPO
erniekit train examples/configs/ERNIE-4.5-300B-A47B/dpo/run_dpo_wint8mix_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-300B-A47B-Base-Paddle
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### Using FastDeploy
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: To deploy on a configuration with 4 GPUs each having at least 80G of memory, specify ```--quantization wint4```. If you specify ```--quantization wint8```, then resources for 8 GPUs are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Base-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--quantization wint4 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 32
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle
|
baidu
| 2025-08-20T02:25:26Z | 18 | 10 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-28T09:27:03Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-300B-A47B
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-300B-A47B is a text MoE Post-trained model, with 300B total parameters and 47B activated parameters for each token. The following are the model configuration details:
|Key|Value|
|-|-|
|Modality|Text|
|Training Stage|Pretraining|
|Params(Total / Activated)|300B / 47B|
|Layers|54|
|Heads(Q/KV)|64 / 8|
|Text Experts(Total / Activated)|64 / 8|
|Vision Experts(Total / Activated)|64 / 8|
|Context Length|131072|
## Quickstart
### Using FastDeploy
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: To deploy on a configuration with 4 GPUs each having at least 80G of memory, specify ```--quantization wint4```. If you specify ```--quantization wint8```, then resources for 8 GPUs are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Paddle \
--port 8180 \
--metrics-port 8181 \
--quantization wint4 \
--tensor-parallel-size 8 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the W4A8C8 quantized version using FastDeploy, you can run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the WINT2 quantized version using FastDeploy on a single 141G GPU, you can run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model "baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle" \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128
```
The following contains a code snippet illustrating how to use ERNIE-4.5-300B-A47B-FP8 generate content based on given inputs.
```python
from fastdeploy import LLM, SamplingParams
prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=128)
model = "baidu/ERNIE-4.5-300B-A47B-FP8-Paddle"
llm = LLM(model=model, tensor_parallel_size=8, max_model_len=8192, num_gpu_blocks_override=1024, engine_worker_queue_port=9981)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
print("generated_text", generated_text)
```
## Best Practices
### **Sampling Parameters**
To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`.
### Prompts for Web Search
For Web Search, {references}, {date}, and {question} are arguments.
For Chinese question, we use the prompt:
```python
ernie_search_zh_prompt = \
'''下面你会收到当前时间、多个不同来源的参考文章和一段对话。你的任务是阅读多个参考文章，并根据参考文章中的信息回答对话中的问题。
以下是当前时间和参考文章：
---------
#当前时间
{date}
#参考文章
{references}
---------
请注意：
1. 回答必须结合问题需求和当前时间，对参考文章的可用性进行判断，避免在回答中使用错误或过时的信息。
2. 当参考文章中的信息无法准确地回答问题时，你需要在回答中提供获取相应信息的建议，或承认无法提供相应信息。
3. 你需要优先根据百科、官网、权威机构、专业网站等高权威性来源的信息来回答问题。
4. 回复需要综合参考文章中的相关数字、案例、法律条文、公式等信息，使你的答案更专业。
5. 当问题属于创作类任务时，需注意以下维度：
- 态度鲜明：观点、立场清晰明确，避免模棱两可，语言果断直接
- 文采飞扬：用词精准生动，善用修辞手法，增强感染力
- 有理有据：逻辑严密递进，结合权威数据/事实支撑论点
---------
下面请结合以上信息，回答问题，补全对话
{question}'''
```
For English question, we use the prompt:
```python
ernie_search_en_prompt = \
'''
Below you will be given the current time, multiple references from different sources, and a conversation. Your task is to read the references and use the information in them to answer the question in the conversation.
Here are the current time and the references:
---------
#Current Time
{date}
#References
{references}
---------
Please note:
1. Based on the question’s requirements and the current time, assess the usefulness of the references to avoid using inaccurate or outdated information in the answer.
2. If the references do not provide enough information to accurately answer the question, you should suggest how to obtain the relevant information or acknowledge that you are unable to provide it.
3. Prioritize using information from highly authoritative sources such as encyclopedias, official websites, authoritative institutions, and professional websites when answering questions.
4. Incorporate relevant numbers, cases, legal provisions, formulas, and other details from the references to make your answer more professional.
5. For creative tasks, keep these dimensions in mind:
- Clear attitude: Clear views and positions, avoid ambiguity, and use decisive and direct language
- Brilliant writing: Precise and vivid words, good use of rhetoric, and enhance the appeal
- Well-reasoned: Rigorous logic and progressive, combined with authoritative data/facts to support the argument
---------
Now, using the information above, answer the question and complete the conversation:
{question}'''
```
Parameter notes:
* {question} is the user’s question
* {date} is the current time, and the recommended format is “YYYY-MM-DD HH:MM:SS, Day of the Week, Beijing/China.”
* {references} is the references, and the recommended format is:
```text
##参考文章1
标题：周杰伦
文章发布时间：2025-04-20
内容：周杰伦(Jay Chou),1979年1月18日出生于台湾省新北市,祖籍福建省永春县,华语流行乐男歌手、音乐人、演员、导演、编剧,毕业于淡江中学。2000年,发行个人首张音乐专辑《Jay》。...
来源网站网址：baike.baidu.com
来源网站的网站名：百度百科
##参考文章2
...
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
baidu/ERNIE-4.5-VL-28B-A3B-Base-Paddle
|
baidu
| 2025-08-20T02:24:58Z | 26 | 14 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe_vl",
"ERNIE4.5",
"image-text-to-text",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
image-text-to-text
| 2025-06-28T05:21:28Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-text-to-text
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-VL-28B-A3B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
To ensure the stability of multimodal joint training, we adopt a staged training strategy. In the first and second stage, we train only the text-related parameters, enabling the model to develop strong fundamental language understanding as well as long-text processing capabilities. The final multimodal stage extends capabilities to images and videos by introducing additional parameters including a ViT for image feature extraction, an adapter for feature transformation, and visual experts for multimodal understanding. At this stage, text and visual modalities mutually enhance each other. After pretraining trillions tokens, we obtained ERNIE-4.5-VL-28B-A3B-Base.
## Model Overview
ERNIE-4.5-VL-28B-A3B-Base is a multimodal MoE Base model, with 28B total parameters and 3B activated parameters for each token. The following are the model configuration details:
| Key | Value |
| --------------------------------- | ------------- |
| Modality | Text & Vision |
| Training Stage | Pretraining |
| Params(Total / Activated) | 28B / 3B |
| Layers | 28 |
| Heads(Q/KV) | 20 / 4 |
| Text Experts(Total / Activated) | 64 / 6 |
| Vision Experts(Total / Activated) | 64 / 6 |
| Shared Experts | 2 |
| Context Length | 131072 |
## Quickstart
### vLLM inference
We are working with the community to fully support ERNIE4.5 models, stay tuned.
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
roeker/blockassist-bc-quick_wiry_owl_1755656566
|
roeker
| 2025-08-20T02:24:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:23:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755655085
|
ihsanridzi
| 2025-08-20T02:24:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:24:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DavidAU/OpenAi-GPT-oss-20b-LIGHT-uncensored-NEO-Imatrix-gguf
|
DavidAU
| 2025-08-20T02:22:59Z | 5,113 | 1 | null |
[
"gguf",
"gpt_oss",
"gpt-oss",
"openai",
"mxfp4",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"reasoning",
"thinking",
"r1",
"cot",
"deepseek",
"128k context",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"uncensored",
"abliterated",
"Neo",
"MOE",
"Mixture of Experts",
"24 experts",
"NEO Imatrix",
"Imatrix",
"text-generation",
"en",
"base_model:huizimao/gpt-oss-20b-uncensored-mxfp4",
"base_model:quantized:huizimao/gpt-oss-20b-uncensored-mxfp4",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-08-10T01:09:39Z |
---
license: apache-2.0
base_model:
- huizimao/gpt-oss-20b-uncensored-mxfp4
language:
- en
pipeline_tag: text-generation
tags:
- gpt_oss
- gpt-oss
- openai
- mxfp4
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- reasoning
- thinking
- r1
- cot
- deepseek
- 128k context
- general usage
- problem solving
- brainstorming
- solve riddles
- general usage
- openai
- uncensored
- abliterated
- Neo
- MOE
- Mixture of Experts
- 24 experts
- NEO Imatrix
- Imatrix
---
<small><font color="red">Specialized "light" uncensored quants for new OpenAI 20B MOE - Mixture of Experts Model at 80+ T/S.
See settings and special instructions for using this model below.</font></small>
<h2>OpenAi-GPT-oss-20b-LIGHT-uncensored-NEO-Imatrix-gguf</h2>
<img src="power-the-matrix.gif" style="float:right; width:300px; height:300px; padding:10px;">
These are NEO Imatrix GGUFs, NEO dataset by DavidAU.
NEO dataset improves overall performance, and is for all use cases.
This model uses "huizimao/gpt-oss-20b-uncensored-mxfp4" (Light, 22% refusal rate VS 77% of Org OpenAI 20B using same content/prompt) as a base which DE-CENSORS the model and removes refusals.
This model runs better than the full abliterated/uncensored and "moderate" uncensored version and accepts MOST content generation requests.
The goal is to temper the "nanny" during normal generation / general use cases.
It is the best balance between light refusals "repairs" and best model performance.
NOTE: Tool use re-enabled in this version ; which differs from source from "huizimao".
Example output below (creative; IQ4_NL), using settings below.
Looking for 100% uncensored/abliterated?
https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf
Moderate uncensored ?
https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-MODERATE-uncensored-NEO-Imatrix-gguf
If you do not need an "uncensored" / "abliterated" model (at this repo) please go here:
https://huggingface.co/DavidAU/Openai_gpt-oss-20b-NEO-GGUF
or for the "big boy":
https://huggingface.co/DavidAU/Openai_gpt-oss-120b-NEO-Imatrix-GGUF
<B>QUANTS:</B>
Due to quanting issues with this model (which result in oddball quant sizes / mixtures), only TESTED quants will be uploaded (at the moment).
Currently that means IQ4_NL, Q5_1, MXFP4_MOE4 (a special OpenAI Quant) and Q8_0 are available.
NEO dataset performance improvements will show the most in the IQ4_NL, followed by Q5_1.
I find Q5_1 quants work better (and more stable) for some use cases than IQ4_NL ; however IQ4_NLs can be wilder, and off the cuff more.
IQ4_NL quant(s):
- OpenAI-20B-MAO-uncensored-NEO-IQ4_NL.gguf (Neo Imatrix)
- OpenAI-20B-MAO-uncensored-NEOCODE-IQ4_NL.gguf (NeoCODE Imatrix)
Q5_1 quant(s):
- OpenAI-20B-MAO-uncensored-NEO-Q5_1.gguf (Neo Imatrix)
- OpenAI-20B-MAO-uncensored-NEOCODE-Q5_1.gguf (NeoCODE Imatrix)
MXFP4_MOE4 quant(s):
- OpenAI-20B-UncensoredPlus-MAO-MXFP4_MOE4.gguf (output tensor at BF16, non imatrix -> has fixed tools functions)
Q8_0 quant(s):
- pending.
NOTE: The output tensor makes up for 10-20% of the output.
IQ4_NL, Q5_1 and Q8_0 quants are compatible (less/minimal damage when quanting) with OpenAI's tensor structure.
MXFP4_MOE4 is an exact match to OpenAi's tensor structure, but has limited "imatrix" applied to it.
<B>IMPORTANT: Using an "abliterated" model VS "uncensored" model</B>
Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.
In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.
Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want)
to get it generate the content correctly as the "expected" content level too.
Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.
Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic,
cursing or explicit levels.
Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.
<B>ABLITERATED / UNCENSORED Notes / Settings:</B>
- Suggest experts set to 4 or 5 or 6.
- 2-4 regens suggested.
- Some regens will be strange, while others will be "bang on".
- LOWER temps .4 to .8 ; especially if you get repeats/issues.
- However, sometimes temp 1, 1.1, 1.2 are the best depending on your use case(s).
- Temps of 2 or higher can be ah... very interesting.
- LONGER prompts (with more details, directives) tend to work better as long as they are clear enough.
- REP PEN setting is CRITICAL.
Suggested Settings (tested in Lmstudio, Beta Branch 0.3.21 ; 4 ):
- Context: 8k min.
- Temp 1 to 1.2+ for creative. Temp .6 (or so) for coding/general.
- Rep pen 1.1, topk 40, topp .95, min p 0.05
- Experts 4-8 depending on use case. (higher than 8 MAY lower quality AND/OR cause repeat issues)
Model Supports:
- 128k context
- up to 24 experts
- Tools use, browsing, etc
For my help docs, SETTING NUMBER OF EXPERTS, and other see below.
See more about this model here:
https://huggingface.co/openai/gpt-oss-20b
[ Please refer to their model card, especially to control "thinking" levels. ]
AND the "light" uncensored version:
https://huggingface.co/huizimao/gpt-oss-20b-uncensored-mxfp4
---
<H2>Help, Adjustments, Samplers, Parameters and More</H2>
---
<B>CHANGE THE NUMBER OF ACTIVE EXPERTS:</B>
See this document:
https://huggingface.co/DavidAU/How-To-Set-and-Manage-MOE-Mix-of-Experts-Model-Activation-of-Experts
<B>Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:</B>
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be
OTHER OPTIONS:
- Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
- If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
<B>Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B>
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
---
<h2>EXAMPLE - IQ4_NL ; temp .8, using above settings (creative)</h2>
NO System prompt. (default thinking level)
---
PROMPT:
<B>
</B>
OUTPUT:
[[[thinking]]]
|
ElToro2602/blockassist-bc-raging_prehistoric_chameleon_1755656334
|
ElToro2602
| 2025-08-20T02:19:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging prehistoric chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:19:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging prehistoric chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
te4bag/QLoRA-llama-3.2-3B-alpaca
|
te4bag
| 2025-08-20T02:18:35Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"region:us"
] |
text-generation
| 2025-08-20T02:17:20Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-3B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
roeker/blockassist-bc-quick_wiry_owl_1755656157
|
roeker
| 2025-08-20T02:17:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:16:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755654595
|
koloni
| 2025-08-20T02:16:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:16:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
g-assismoraes/Qwen3-4B-Base-blocks-aki-alpha0.6-mur0.05-run2
|
g-assismoraes
| 2025-08-20T02:11:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:07:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
roeker/blockassist-bc-quick_wiry_owl_1755655749
|
roeker
| 2025-08-20T02:10:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:10:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
g-assismoraes/Qwen3-4B-Base-blocks-fpi-alpha0.8-mur0.05-run1
|
g-assismoraes
| 2025-08-20T02:07:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:03:40Z |
---
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]
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755655021
|
hobson123
| 2025-08-20T02:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:02:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755653607
|
sampingkaca72
| 2025-08-20T01:57:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:57:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755654927
|
roeker
| 2025-08-20T01:56:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:56:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BaseerAI/Interfuser-Baseer-v1
|
BaseerAI
| 2025-08-20T01:45:46Z | 0 | 2 |
pytorch
|
[
"pytorch",
"computer-vision",
"autonomous-driving",
"self-driving-car",
"end-to-end",
"transformer",
"attention",
"positional-encoding",
"carla",
"object-detection",
"trajectory-prediction",
"en",
"dataset:PDM-Lite-CARLA",
"license:mit",
"region:us"
] |
object-detection
| 2025-08-05T23:24:07Z |
---
license: mit
language: en
library_name: pytorch
tags:
- computer-vision
- autonomous-driving
- self-driving-car
- end-to-end
- transformer
- attention
- positional-encoding
- carla
- object-detection
- trajectory-prediction
datasets:
- PDM-Lite-CARLA
pipeline_tag: object-detection
---
# HDPE: A Foundational Perception Model with Hyper-Dimensional Positional Encoding
[](https://opensource.org/licenses/MIT)
[](https://pytorch.org/)
[](https://carla.org/)
[](https://huggingface.co/spaces/Adam-IT/Baseer_Server)
**📖 Research Paper (Coming Soon)** | **🚀 [Live Demo API (Powered by this Model)](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**
---
## 📖 Overview: A New Foundation for Perception in Autonomous Driving
This repository contains the pre-trained weights for a novel autonomous driving perception model, the core of our **Interfuser-HDPE** system. This is **not a standard Interfuser model**; it incorporates fundamental innovations in its architecture and learning framework to achieve a more robust, accurate, and geometrically-aware understanding of driving scenes from camera-only inputs.
The innovations baked into these weights make this model a powerful foundation for building complete self-driving systems. It is designed to output rich perception data (object detection grids and waypoints) that can be consumed by downstream modules like trackers and controllers.
---
## 💡 Key Innovations in This Model
The weights in this repository are the result of training a model with the following scientific contributions:
### 1. Hyper-Dimensional Positional Encoding (HDPE) - (Core Contribution)
* **What it is:** We replace the standard Sinusoidal Positional Encoding with **HDPE**, a novel, first-principles approach inspired by the geometric properties of n-dimensional spaces.
* **Why it matters:** HDPE generates an interpretable spatial prior that biases the model's attention towards the center of the image (the road ahead). This leads to more stable and contextually-aware feature extraction, and has shown to improve performance significantly, especially in multi-camera fusion scenarios.
### 2. Advanced Multi-Task Loss Framework
* **What it is:** This model was trained using a specialized combination of **Focal Loss** and **Enhanced-IoU (EIoU) Loss**.
* **Why it matters:** This framework is purpose-built to tackle the primary challenges in perception: **Focal Loss** addresses the severe class imbalance in object detection, while **EIoU Loss** ensures highly accurate bounding box regression by optimizing for geometric overlap.
### 3. High-Resolution, Camera-Only Architecture
* **What it is:** This model is vision-based (**camera-only**) and uses a **ResNet-50** backbone with a smaller patch size (`patch_size=8`) for high-resolution analysis.
* **Why it matters:** It demonstrates that strong perception performance can be achieved without costly sensors like LiDAR, aligning with modern, cost-effective approaches to autonomous driving.
---
## 🏗️ Model Architecture vs. Baseline
| Component | Original Interfuser (Baseline) | **Interfuser-HDPE (This Model)** |
|:--------------------------|:-------------------------------|:----------------------------------|
| **Positional Encoding** | Sinusoidal PE | ✅ **Hyper-Dimensional PE (HDPE)** |
| **Perception Backbone** | ResNet-26, LiDAR | ✅ **Camera-Only, ResNet-50** |
| **Training Objective** | Standard BCE + L1 Loss | ✅ **Focal Loss + EIoU Loss** |
| **Model Outputs** | Waypoints, Traffic Grid, States| Same (Optimized for higher accuracy) |
---
## 🚀 How to Use These Weights
These weights are intended to be loaded into a model class that incorporates our architectural changes, primarily the `HyperDimensionalPositionalEncoding` module.
```python
import torch
from huggingface_hub import hf_hub_download
# You need to provide the model class definition, let's call it InterfuserHDPE
from your_model_definition_file import InterfuserHDPE
# Download the pre-trained model weights
model_path = hf_hub_download(
repo_id="BaseerAI/Interfuser-Baseer-v1",
filename="pytorch_model.bin"
)
# Instantiate your model architecture
# The config must match the architecture these weights were trained on
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = InterfuserHDPE(**model_config).to(device)
# Load the state dictionary
state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict)
model.eval()
# Now the model is ready for inference
with torch.no_grad():
# The model expects a dictionary of sensor data
# (e.g., {'rgb': camera_tensor, ...})
perception_outputs = model(input_data)
```
## 📊 Performance Highlights
When integrated into a full driving stack (like our **[Baseer Self-Driving API](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**), this perception model is the foundation for:
- **Significantly Improved Detection Accuracy**: Achieves higher mAP on the PDM-Lite-CARLA dataset.
- **Superior Driving Score**: Leads to a higher overall Driving Score with fewer infractions compared to baseline models.
- **Proven Scalability**: Performance demonstrably improves when scaling from single-camera to multi-camera inputs, showcasing the robustness of the HDPE-based architecture.
*(Detailed metrics and ablation studies will be available in our upcoming research paper.)*
## 🛠️ Integration with a Full System
This model provides the core perception outputs. To build a complete autonomous agent, you need to combine it with:
- **A Temporal Tracker**: To maintain object identity across frames.
- **A Decision-Making Controller**: To translate perception outputs into vehicle commands.
An example of such a complete system, including our custom-built **Hierarchical, Memory-Enhanced Controller**, can be found in our **[Live Demo API Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**.
## 📚 Citation
If you use the HDPE concept or this model in your research, please cite our upcoming paper. For now, you can cite this model repository:
```bibtex
@misc{interfuser-hdpe-2024,
title={HDPE: Hyper-Dimensional Positional Encoding for End-to-End Self-Driving Systems},
author={Altawil, Adam},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/BaseerAI/Interfuser-Baseer-v1}}
}
```
## 👨💻 Development
**Lead Researcher**: Adam Altawil
**Project Type**: Graduation Project - AI & Autonomous Driving
**Contact**: [Your Contact Information]
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🤝 Contributing & Support
For questions, contributions, and support:
- **🚀 Try the Live Demo**: **[Baseer Server Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**
- **📧 Contact**: [Your Contact Information]
- **🐛 Issues**: Create an issue in this repository
---
<div align="center">
<strong>🚗 Driving the Future with Hyper-Dimensional Intelligence 🚗</strong>
</div>
|
hsiehfuwei/uuu_fine_tune_gpt2
|
hsiehfuwei
| 2025-08-20T01:43:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:43:01Z |
---
license: apache-2.0
---
|
AXERA-TECH/Qwen3-4B
|
AXERA-TECH
| 2025-08-20T01:42:34Z | 13 | 0 | null |
[
"Qwen",
"Qwen3",
"Int8",
"text-generation",
"en",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-30T09:26:37Z |
---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- Qwen
- Qwen3
- Int8
---
# Qwen3-4B-Int8
This version of Qwen3-4B-Int8 has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 4.2(Not released yet)
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through the original repo :
https://huggingface.co/Qwen/Qwen3-4B
[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
[AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm)
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
|Chips|w8a16|w4a16|
|--|--|--|
|AX650| 4.5 tokens/sec|TBD|
## How to use
Download all files from this repository to the device
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# tree -L 1
.
|-- config.json
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- post_config.json
|-- qwen2.5_tokenizer
|-- qwen3-4b-ax650
|-- qwen3_tokenizer
|-- qwen3_tokenizer_uid.py
|-- run_qwen3_4b_int8_ctx_ax650.sh
|-- run_qwen3_4b_int8_ctx_axcl_aarch64.sh
`-- run_qwen3_4b_int8_ctx_axcl_x86.sh
3 directories, 9 files
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
```
#### Start the Tokenizer service
Install requirement
```
pip install transformers jinja2
```
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# python3 qwen3_tokenizer_uid.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Server running at http://0.0.0.0:12345
```
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board
Open another terminal and run `run_qwen3_4b_int8_ctx_ax650.sh`
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# ./run_qwen3_4b_int8_ctx_ax650.sh
[I][ Init][ 110]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: 6e90ff82-b9c9-42dc-8f61-081203389166
bos_id: -1, eos_id: 151645
2% | █ | 1 / 39 [3.95s<153.89s, 0.25 count/s] tokenizer init ok
[I][ Init][ 26]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 39 / 39 [48.03s<48.03s, 0.81 count/s] init post axmodel ok,remain_cmm(5621 MB)
[I][ Init][ 188]: max_token_len : 2559
[I][ Init][ 193]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 201]: prefill_token_num : 128
[I][ Init][ 205]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 205]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 205]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 205]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 205]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 205]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 209]: prefill_max_token_num : 2048
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 218]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 270]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 307]: input_num_token:21
[I][ main][ 230]: precompute_len: 21
[I][ main][ 231]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> 1+3=?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1169.05 ms
<think>
</think>
1 + 3 = 4
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:48, remaining:2000
prompt >> who are you?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:48 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1168.56 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create content,
and help with a variety of tasks. How can I assist you today?
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:106, remaining:1942
prompt >> q
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
```
#### Inference with M.2 Accelerator card
[What is M.2 Accelerator card?](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html), Show this DEMO based on Raspberry PI 5.
```
(base) axera@raspberrypi:~/samples/qwen3-4b $ ./run_qwen3_4b_int8_ctx_axcl_aarch64.sh
[I][ Init][ 136]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: a5b1e427-0cdf-4da6-b3a7-f5e0517da0bb
bos_id: -1, eos_id: 151645
2% | █ | 1 / 39 [0.99s<38.45s, 1.01 count/s] tokenizer init ok
[I][ Init][ 45]: LLaMaEmbedSelector use mmap
5% | ██ | 2 / 39 [0.99s<19.23s, 2.03 count/s] embed_selector init ok
[I][ run][ 30]: AXCLWorker start with devid 0
100% | ████████████████████████████████ | 39 / 39 [133.16s<133.16s, 0.29 count/s] init post axmodel ok,remain_cmm(691 MB)(1096 MB)000000000
[I][ Init][ 237]: max_token_len : 2559
[I][ Init][ 240]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 248]: prefill_token_num : 128
[I][ Init][ 252]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 252]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 252]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 252]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 252]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 252]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 256]: prefill_max_token_num : 2048
________________________
| ID| remain cmm(MB)|
========================
| 0| 691|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 279]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 335]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 372]: input_num_token:21
[I][ main][ 236]: precompute_len: 21
[I][ main][ 237]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> who are you
[I][ SetKVCache][ 628]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:27
[I][ SetKVCache][ 631]: current prefill_max_token_num:1920
[I][ Run][ 869]: input token num : 27, prefill_split_num : 1
[I][ Run][ 901]: input_num_token:27
[I][ Run][1030]: ttft: 1339.01 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions,
create content, and help with a variety of tasks. What can I assist you with?
[N][ Run][1182]: hit eos,avg 3.65 token/s
[I][ GetKVCache][ 597]: precompute_len:90, remaining:1958
prompt >> q
[I][ run][ 80]: AXCLWorker exit with devid 0
(base) axera@raspberrypi:~/samples/qwen3-4b $
(base) axera@raspberrypi:~ $ axcl-smi
+------------------------------------------------------------------------------------------------+
| AXCL-SMI V3.4.0_20250423020139 Driver V3.4.0_20250423020139 |
+-----------------------------------------+--------------+---------------------------------------+
| Card Name Firmware | Bus-Id | Memory-Usage |
| Fan Temp Pwr:Usage/Cap | CPU NPU | CMM-Usage |
|=========================================+==============+=======================================|
| 0 AX650N V3.4.0 | 0000:01:00.0 | 193 MiB / 945 MiB |
| -- 37C -- / -- | 2% 0% | 6348 MiB / 7040 MiB |
+-----------------------------------------+--------------+---------------------------------------+
+------------------------------------------------------------------------------------------------+
| Processes: |
| Card PID Process Name NPU Memory Usage |
|================================================================================================|
| 0 84643 /home/axera/samples/qwen3-4b/main_axcl_aarch64 4894032 KiB |
+------------------------------------------------------------------------------------------------+
(base) axera@raspberrypi:~ $
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1755652550
|
koloni
| 2025-08-20T01:42:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:42:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ghjdfhfghdfg/uuu_fine_tune_taipower
|
ghjdfhfghdfg
| 2025-08-20T01:39:40Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:32:51Z |
---
license: apache-2.0
---
|
rayonlabs/tournament-tourn_e8b54a44823eb63b_20250819-0746e9d2-8da9-4255-98c3-9cad2ffa8040-5Gy6X7q2
|
rayonlabs
| 2025-08-20T01:38:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Qwen2-0.5B",
"base_model:adapter:unsloth/Qwen2-0.5B",
"region:us"
] | null | 2025-08-20T01:37:55Z |
---
base_model: unsloth/Qwen2-0.5B
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 0.15.2
|
ni1234/uuu_fine_tune_taipower
|
ni1234
| 2025-08-20T01:35:57Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:10:47Z |
---
license: apache-2.0
---
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755652159
|
helmutsukocok
| 2025-08-20T01:34:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:34:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
junyi080914/uuu_fine_tune_taipower
|
junyi080914
| 2025-08-20T01:33:36Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:25:12Z |
---
license: apache-2.0
---
|
kimono998/Wordle-pos-1_lora_adapter_iter_10
|
kimono998
| 2025-08-20T01:29:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:29:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DanielJustin/uuu_fine_tune_taipower
|
DanielJustin
| 2025-08-20T01:27:48Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:26:58Z |
---
license: apache-2.0
---
|
DanielJustin/uuu_fine_tune_gpt2
|
DanielJustin
| 2025-08-20T01:27:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:27:19Z |
---
license: apache-2.0
---
|
astab/uuu_fine_tune_taipower
|
astab
| 2025-08-20T01:26:46Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:15:33Z |
---
license: apache-2.0
---
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755651719
|
sampingkaca72
| 2025-08-20T01:26:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:26:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
iBush/uuu_fine_tune_taipower
|
iBush
| 2025-08-20T01:26:36Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:06:49Z |
---
license: apache-2.0
---
|
ghjdfhfghdfg/uuu_fine_tune_gpt2
|
ghjdfhfghdfg
| 2025-08-20T01:25:10Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:25:10Z |
---
license: apache-2.0
---
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755651942
|
Sayemahsjn
| 2025-08-20T01:24:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:24:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755651459
|
mang3dd
| 2025-08-20T01:22:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:22:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lautan/blockassist-bc-gentle_patterned_goat_1755651032
|
lautan
| 2025-08-20T01:18:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:18:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
haphoptr/blockassist-bc-quiet_robust_seal_1755652585
|
haphoptr
| 2025-08-20T01:17:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quiet robust seal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:17:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quiet robust seal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Akashiurahara/rpGM-LoRa
|
Akashiurahara
| 2025-08-20T01:17:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"llama3.2-3B",
"roleplay",
"tatsumaki",
"nsfw",
"lora",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:17:34Z |
---
library_name: transformers
tags:
- unsloth
- llama3.2-3B
- roleplay
- tatsumaki
- nsfw
- lora
---
# 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.
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755651014
|
quantumxnode
| 2025-08-20T01:17:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:17:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755652492
|
roeker
| 2025-08-20T01:16:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:15:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
|
semenetslitslink
| 2025-08-20T01:15:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-kontextflux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T08:20:53Z |
---
base_model: black-forest-labs/FLUX.1-Kontext-dev
library_name: diffusers
license: other
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-kontextflux-diffusers
- template:sd-lora
---
<!-- 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. -->
# Flux Kontext DreamBooth LoRA - semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
<Gallery />
## Model description
These are semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Kontext-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `None` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import FluxKontextPipeline
import torch
pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('None').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## 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]
|
maydixit/qwen3_235b_second_rl
|
maydixit
| 2025-08-20T01:14:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:04:33Z |
---
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]
|
pmryu0/distilbert-base-uncased-finetuned-emotion
|
pmryu0
| 2025-08-20T01:08:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T09:49:25Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2084
- Accuracy: 0.928
- F1: 0.9280
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8258 | 1.0 | 250 | 0.3076 | 0.911 | 0.9109 |
| 0.2456 | 2.0 | 500 | 0.2084 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
koloni/blockassist-bc-deadly_graceful_stingray_1755650464
|
koloni
| 2025-08-20T01:07:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:07:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Matt1208/Testing3
|
Matt1208
| 2025-08-20T01:05:54Z | 10 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Matt1208/Pick_Up_Grape_V1",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-13T16:38:24Z |
---
datasets: Matt1208/Pick_Up_Grape_V1
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
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
|
BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i
|
BootesVoid
| 2025-08-20T01:03:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T01:03:38Z |
---
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: NAUGHTY
---
# Cmej4Gqae0Ssprts8S25Ojhxv_Cmej8O5Cp0T45Rts8If83Og5I
<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 `NAUGHTY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NAUGHTY",
"lora_weights": "https://huggingface.co/BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i/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('BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i', weight_name='lora.safetensors')
image = pipeline('NAUGHTY').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/BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i/discussions) to add images that show off what you’ve made with this LoRA.
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755650188
|
indoempatnol
| 2025-08-20T01:03:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:03:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Polar-32B-i1-GGUF
|
mradermacher
| 2025-08-20T01:01:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:x2bee/Polar-32B",
"base_model:quantized:x2bee/Polar-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T20:30:13Z |
---
base_model: x2bee/Polar-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
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/x2bee/Polar-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Polar-32B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Polar-32B-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/Polar-32B-i1-GGUF/resolve/main/Polar-32B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
fatmhd1995/phi35_ft_llm_4_annotation_rnd2_v2
|
fatmhd1995
| 2025-08-20T01:00:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T19:51:27Z |
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** fatmhd1995
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-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)
|
ultratopaz/46296
|
ultratopaz
| 2025-08-20T01:00:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:18Z |
[View on Civ Archive](https://civarchive.com/models/61593?modelVersionId=66087)
|
seraphimzzzz/14930
|
seraphimzzzz
| 2025-08-20T00:59:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:59:56Z |
[View on Civ Archive](https://civarchive.com/models/15118?modelVersionId=17812)
|
crystalline7/23212
|
crystalline7
| 2025-08-20T00:59:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:59:46Z |
[View on Civ Archive](https://civarchive.com/models/23520?modelVersionId=28088)
|
crystalline7/14262
|
crystalline7
| 2025-08-20T00:58:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:58:20Z |
[View on Civ Archive](https://civarchive.com/models/14379?modelVersionId=16924)
|
Zenfish-zy/q-FrozenLake-v1-4x4-noSlippery
|
Zenfish-zy
| 2025-08-20T00:56:30Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-20T00:56:26Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.29 +/- 0.45
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Zenfish-zy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Mostefa-Terbeche/diabetic-retinopathy-messidor-resnet50-original-20250614-233153
|
Mostefa-Terbeche
| 2025-08-20T00:51:50Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:messidor",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-19T23:52:37Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- messidor
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: messidor_resnet50_original
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: messidor
name: MESSIDOR
metrics:
- type: accuracy
value: 0.367816091954023
- type: quadratic-kappa
value: 0.6178271246238456
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the resnet50 architecture on the messidor dataset with original preprocessing.
## Model Details
- **Architecture**: resnet50
- **Dataset**: messidor
- **Preprocessing**: original
- **Training Date**: 20250614-233153
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: messidor_resnet50_20250614-233153_new
## Performance
- **Test Accuracy**: 0.367816091954023
- **Test Quadratic Kappa**: 0.6178271246238456
- **Validation Kappa**: 0.6178271246238456
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-messidor-resnet50-original",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
AnonymousCS/xlmr_immigration_combo9_2
|
AnonymousCS
| 2025-08-20T00:51:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T00:47:51Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo9_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo9_2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2298
- Accuracy: 0.9383
- 1-f1: 0.904
- 1-recall: 0.8726
- 1-precision: 0.9378
- Balanced Acc: 0.9218
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1327 | 1.0 | 25 | 0.1839 | 0.9499 | 0.9218 | 0.8880 | 0.9583 | 0.9344 |
| 0.1496 | 2.0 | 50 | 0.2060 | 0.9332 | 0.8980 | 0.8842 | 0.9124 | 0.9209 |
| 0.1256 | 3.0 | 75 | 0.2298 | 0.9383 | 0.904 | 0.8726 | 0.9378 | 0.9218 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
yidingp/rm_pairwise_out
|
yidingp
| 2025-08-20T00:51:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"reward-trainer",
"trl",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T00:51:12Z |
---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: rm_pairwise_out
tags:
- generated_from_trainer
- reward-trainer
- trl
licence: license
---
# Model Card for rm_pairwise_out
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="yidingp/rm_pairwise_out", 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 Reward.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.5.1+cu121
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AnonymousCS/xlmr_immigration_combo9_1
|
AnonymousCS
| 2025-08-20T00:47:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T00:43:49Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo9_1
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. -->
# xlmr_immigration_combo9_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2268
- Accuracy: 0.9332
- 1-f1: 0.8926
- 1-recall: 0.8340
- 1-precision: 0.96
- Balanced Acc: 0.9083
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2455 | 1.0 | 25 | 0.1956 | 0.9344 | 0.8978 | 0.8649 | 0.9333 | 0.9170 |
| 0.1794 | 2.0 | 50 | 0.1927 | 0.9383 | 0.9028 | 0.8610 | 0.9489 | 0.9189 |
| 0.1584 | 3.0 | 75 | 0.2154 | 0.9293 | 0.8861 | 0.8263 | 0.9554 | 0.9035 |
| 0.0856 | 4.0 | 100 | 0.2268 | 0.9332 | 0.8926 | 0.8340 | 0.96 | 0.9083 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Nitral-AI/CaptainErisNebula-12B-AOE-v1
|
Nitral-AI
| 2025-08-20T00:45:38Z | 0 | 3 | null |
[
"safetensors",
"mistral",
"en",
"base_model:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69",
"base_model:finetune:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69",
"license:other",
"region:us"
] | null | 2025-08-17T09:20:38Z |
---
license: other
language:
- en
base_model:
- Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69
---
# Quants: [4bpw-exl3](https://huggingface.co/Nitrals-Quants/CaptainErisNebula-12B-AE-v0.420-4bpw-exl3) [Imatrix GGuf Thanks to Lewdiculus <3](https://huggingface.co/Lewdiculous/CaptainErisNebula-12B-AOE-v1-GGUF-IQ-Imatrix)
## Base Model: [Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69](https://huggingface.co/Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69)
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755650624
|
mohda
| 2025-08-20T00:45:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:45:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n
|
BootesVoid
| 2025-08-20T00:45:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T00:45:06Z |
---
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: YURI
---
# Cmej7G2Wj0T0Xrts8X4Y1Slnf_Cmej805Oi0T2Drts8O9Fp099N
<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 `YURI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "YURI",
"lora_weights": "https://huggingface.co/BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n/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('BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n', weight_name='lora.safetensors')
image = pipeline('YURI').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/BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n/discussions) to add images that show off what you’ve made with this LoRA.
|
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