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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-19 00:41:44
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11.7k
| library_name
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lynn-mikami/wan-testing
|
lynn-mikami
| 2025-09-18T05:09:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-18T10:20:30Z |
---
license: apache-2.0
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-140
|
reinforce-flow
| 2025-09-18T05:09:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:08:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlidrak/roberta_en_med_merged_classes
|
mlidrak
| 2025-09-18T05:08:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T02:19:07Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_en_med_merged_classes
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. -->
# roberta_en_med_merged_classes
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4434
- Accuracy: 0.8467
- F1 Macro: 0.7876
- F1 Weighted: 0.8463
## 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: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 0.3997 | 3.3621 | 400 | 0.4434 | 0.8467 | 0.7876 | 0.8463 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.6.0+cu124
- Tokenizers 0.22.0
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.0005_1280_3
|
winnieyangwannan
| 2025-09-18T05:06:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:05:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
QuangDuy/mmBERT-base_1809
|
QuangDuy
| 2025-09-18T05:01:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"base_model:jhu-clsp/mmBERT-base",
"base_model:finetune:jhu-clsp/mmBERT-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T02:25:12Z |
---
library_name: transformers
license: mit
base_model: jhu-clsp/mmBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mmBERT-base_1809
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. -->
# mmBERT-base_1809
This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8179
- Accuracy: 0.7386
- Precision: 0.7385
- Recall: 0.7442
- F1: 0.7394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6906 | 1.0 | 350 | 0.8201 | 0.6879 | 0.6995 | 0.6901 | 0.6921 |
| 1.4776 | 2.0 | 700 | 0.7611 | 0.7464 | 0.7484 | 0.7552 | 0.7450 |
| 1.3559 | 3.0 | 1050 | 0.7602 | 0.7521 | 0.7551 | 0.7544 | 0.7542 |
| 1.1664 | 4.0 | 1400 | 0.7964 | 0.7471 | 0.7481 | 0.7547 | 0.7467 |
| 0.9809 | 5.0 | 1750 | 0.8179 | 0.7386 | 0.7385 | 0.7442 | 0.7394 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.7.1+cu128
- Tokenizers 0.22.0
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-40
|
reinforce-flow
| 2025-09-18T05:01:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:00:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758171311
|
schooncestiaa
| 2025-09-18T04:56:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T04:56:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/convnext_base_in22ft1k_Opset18
|
onnxmodelzoo
| 2025-09-18T04:55:03Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:54:37Z |
---
language: en
license: apache-2.0
model_name: convnext_base_in22ft1k_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convnext_base_in22ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T04:54:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:53:37Z |
---
language: en
license: apache-2.0
model_name: convnext_base_in22ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
r-three/supertoken_models-llama_CohereLabs-aya-expanse-8b
|
r-three
| 2025-09-18T04:53:10Z | 54 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T14:18:33Z |
---
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]
|
onnxmodelzoo/convit_tiny_Opset17
|
onnxmodelzoo
| 2025-09-18T04:51:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:51:29Z |
---
language: en
license: apache-2.0
model_name: convit_tiny_Opset17.onnx
tags:
- Computer_Vision
---
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.001_1280_3
|
winnieyangwannan
| 2025-09-18T04:48:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T04:47:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
uwcc/DreamRealAnime
|
uwcc
| 2025-09-18T04:46:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T04:44:52Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: A church in a field on a sunny day, [trigger] style.
output:
url: samples/1758170633375__000004000_0.jpg
- text: A seal plays with a ball on the beach, [trigger] style.
output:
url: samples/1758170651794__000004000_1.jpg
- text: A clown at the circus rides on a zebra, [trigger] style.
output:
url: samples/1758170670209__000004000_2.jpg
- text: '[trigger]'
output:
url: samples/1758170688632__000004000_3.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: DreamRealAnime
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
---
# DreamRealAnime
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `DreamRealAnime` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/uwcc/DreamRealAnime/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('uwcc/DreamRealAnime', weight_name='DreamRealAnime.safetensors')
image = pipeline('A church in a field on a sunny day, [trigger] style.').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
langtuphongtran/thay-man-hinh-iphone-13
|
langtuphongtran
| 2025-09-18T04:43:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-18T04:31:14Z |
<h1 dir="ltr" data-pm-slice="1 1 []">Thay Màn Hình iPhone 13 Hết Bao Nhiêu? Tìm Hiểu Chi Phí Và Dịch Vụ Uy Tín</h1>
<p dir="ltr">Trong cuộc sống hiện đại, iPhone đã trở thành người bạn đồng hành không thể thiếu. Tuy nhiên, màn hình vỡ hoặc hỏng có thể khiến bạn đau đầu về chi phí sửa chữa. Đặc biệt với iPhone 13 – dòng điện thoại được yêu thích nhờ thiết kế sang trọng và hiệu năng vượt trội, câu hỏi <a href="https://chamsocdidong.com/thay-man-hinh-iphone-13-ds11272">thay màn hình iPhone 13 hết bao nhiêu</a> luôn được nhiều người quan tâm. Bài viết này sẽ cung cấp thông tin chi tiết về chi phí, các yếu tố ảnh hưởng và cách chọn dịch vụ uy tín như <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để đảm bảo chất lượng.</p>
<h2 dir="ltr">1. Chi Phí Thay Màn Hình iPhone 13 Hết Bao Nhiêu?</h2>
<p dir="ltr">Chi phí thay màn hình iPhone 13 phụ thuộc vào nhiều yếu tố, bao gồm loại màn hình (chính hãng Apple hay linh kiện OEM) và mức độ hư hỏng. Theo khảo sát tại các trung tâm uy tín, giá thay màn hình iPhone 13 dao động từ <strong>3 đến 6 triệu đồng</strong>. Cụ thể:</p>
<ul class="tight" dir="ltr" data-tight="true">
<li>
<p dir="ltr"><strong>Màn hình chính hãng Apple</strong>: Chi phí cao hơn, thường từ 4,5 - 6 triệu đồng, nhưng đảm bảo chất lượng hiển thị và độ bền tối ưu.</p>
</li>
<li>
<p dir="ltr"><strong>Màn hình OEM chất lượng cao</strong>: Giá rẻ hơn, khoảng 3 - 4,5 triệu đồng, phù hợp với người dùng muốn tiết kiệm nhưng vẫn cần hiệu suất tốt.</p>
</li>
</ul>
<p dir="ltr">Tại <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong>, bạn sẽ được tư vấn chi tiết về từng loại màn hình, kèm theo chính sách bảo hành dài hạn. Để biết chính xác <strong>thay màn hình iPhone 13 hết bao nhiêu</strong>, bạn nên liên hệ trực tiếp để nhận báo giá cập nhật.</p>
<h2 dir="ltr">2. Yếu Tố Ảnh Hưởng Đến Chi Phí Thay Màn Hình iPhone</h2>
<p dir="ltr">Không chỉ riêng iPhone 13, câu hỏi <strong>thay màn hình iPhone giá bao nhiêu</strong> áp dụng cho mọi dòng iPhone đều phụ thuộc vào các yếu tố sau:</p>
<ul class="tight" dir="ltr" data-tight="true">
<li>
<p dir="ltr"><strong>Model iPhone</strong>: Các dòng mới như iPhone 13, 14 có công nghệ màn hình tiên tiến hơn, dẫn đến chi phí cao hơn so với iPhone X hay iPhone 11 (khoảng 1,5 - 4 triệu đồng).</p>
</li>
<li>
<p dir="ltr"><strong>Loại hư hỏng</strong>: Nếu chỉ vỡ kính ngoài, chi phí sẽ thấp hơn so với hỏng toàn bộ lớp hiển thị hoặc cảm ứng.</p>
</li>
<li>
<p dir="ltr"><strong>Địa chỉ sửa chữa</strong>: Các trung tâm uy tín như <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> sử dụng linh kiện chất lượng, đội ngũ kỹ thuật viên lành nghề, giúp bạn tránh rủi ro từ dịch vụ kém chất lượng.</p>
</li>
<li>
<p dir="ltr"><strong>Chính sách bảo hành</strong>: Một số nơi cung cấp bảo hành ngắn hạn hoặc không rõ ràng, trong khi các trung tâm chuyên nghiệp thường có bảo hành từ 6 tháng đến 1 năm.</p>
</li>
</ul>
<h2 dir="ltr">3. Lời Khuyên Khi Thay Màn Hình iPhone</h2>
<p dir="ltr">Để đảm bảo chất lượng và tiết kiệm chi phí, bạn cần lưu ý:</p>
<ul class="tight" dir="ltr" data-tight="true">
<li>
<p dir="ltr"><strong>Kiểm tra nguồn gốc linh kiện</strong>: Yêu cầu trung tâm cung cấp thông tin về màn hình (chính hãng hay OEM) trước khi sửa chữa.</p>
</li>
<li>
<p dir="ltr"><strong>Chọn địa chỉ uy tín</strong>: <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là một trong những đơn vị hàng đầu với dịch vụ thay màn hình nhanh chóng (trong 1-2 giờ), minh bạch giá cả và cam kết chất lượng.</p>
</li>
<li>
<p dir="ltr"><strong>So sánh giá cả</strong>: Tham khảo nhiều nơi để tìm mức giá hợp lý, nhưng đừng ham rẻ vì linh kiện kém chất lượng có thể gây hỏng hóc thêm.</p>
</li>
<li>
<p dir="ltr"><strong>Kiểm tra đánh giá khách hàng</strong>: Các trung tâm uy tín thường có phản hồi tích cực từ người dùng trước đó.</p>
</li>
</ul>
<h2 dir="ltr">4. Vì Sao Nên Chọn Bệnh Viện Điện Thoại, Laptop 24h?</h2>
<p dir="ltr"><strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là địa chỉ được nhiều khách hàng tin tưởng nhờ:</p>
<ul class="tight" dir="ltr" data-tight="true">
<li>
<p dir="ltr"><strong>Linh kiện chất lượng</strong>: Sử dụng màn hình chính hãng hoặc OEM đạt chuẩn, đảm bảo trải nghiệm như ban đầu.</p>
</li>
<li>
<p dir="ltr"><strong>Đội ngũ chuyên nghiệp</strong>: Kỹ thuật viên được đào tạo bài bản, xử lý nhanh gọn mọi vấn đề.</p>
</li>
<li>
<p dir="ltr"><strong>Bảo hành dài hạn</strong>: Cam kết bảo hành từ 6 tháng đến 1 năm, giúp bạn yên tâm sử dụng.</p>
</li>
<li>
<p dir="ltr"><strong>Dịch vụ nhanh chóng</strong>: Quy trình thay màn hình chỉ mất vài giờ, tiết kiệm thời gian tối đa.</p>
</li>
</ul>
<p dir="ltr">Nếu bạn đang băn khoăn <a href="https://hub.docker.com/u/thaymanhinhiphone24h">thay màn hình iPhone giá bao nhiêu</a> hoặc cần báo giá cụ thể cho iPhone 13, hãy liên hệ ngay <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để được hỗ trợ.</p>
<h2 dir="ltr">5. Kết Luận</h2>
<p dir="ltr">Việc thay màn hình iPhone, đặc biệt là iPhone 13, đòi hỏi bạn phải cân nhắc kỹ lưỡng về chi phí và chất lượng dịch vụ. Để tránh rủi ro và đảm bảo thiết bị hoạt động ổn định, hãy chọn những địa chỉ uy tín như <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong>. Với đội ngũ chuyên nghiệp và chính sách minh bạch, bạn sẽ nhận được dịch vụ tốt nhất với mức giá hợp lý. Đừng quên kiểm tra thông tin chi tiết về <strong>thay màn hình iPhone 13 hết bao nhiêu</strong> để đưa ra quyết định sáng suốt!</p>
|
sopogen/llama-v5-awq-512
|
sopogen
| 2025-09-18T04:40:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
feature-extraction
| 2025-09-18T04:40:37Z |
---
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]
|
zurizheng/scribblediffusion-fruit
|
zurizheng
| 2025-09-18T04:38:44Z | 0 | 0 | null |
[
"scribblediffusion",
"region:us"
] | null | 2025-09-18T04:37:06Z |
# ScribbleDiffusion: Fruit Dataset Fine-tuned Model
This model is a fine-tuned version of Stable Diffusion v1.5 for sketch-to-image generation, specifically trained on a fruit dataset.
## Model Description
ScribbleDiffusion allows you to generate high-quality images from simple sketches combined with text prompts. This version has been trained on 6 fruit categories:
- Apple
- Banana
- Guava
- Lime
- Orange
- Pomegranate
## Model Architecture
The model consists of three main components:
1. **UNet**: Modified Stable Diffusion 1.5 UNet for diffusion generation
2. **Sketch Encoder**: Cross-attention encoder that processes input sketches
3. **Sketch Text Combiner**: Module that combines sketch and text embeddings
## Training Details
- **Base Model**: Stable Diffusion v1.5
- **Training Steps**: 5,000 steps
- **Batch Size**: 1 (with gradient accumulation steps: 8)
- **Learning Rate**: 0.0001
- **Image Resolution**: 256x256
- **Mixed Precision**: FP16
- **Optimizer**: AdamW
- **Scheduler**: Cosine with 500 warmup steps
## Usage
### Loading the Model
```python
import torch
from diffusers import UNet2DConditionModel, AutoencoderKL, DDIMScheduler
from transformers import CLIPTokenizer, CLIPTextModel
from safetensors.torch import load_file
# Load base models
vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
tokenizer = CLIPTokenizer.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="text_encoder")
scheduler = DDIMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
# Load fine-tuned UNet
unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
unet_state = load_file("unet.safetensors")
unet.load_state_dict(unet_state)
# Load custom sketch components
# Note: You'll need the custom SketchCrossAttentionEncoder and SketchTextCombiner classes
sketch_encoder_state = load_file("sketch_encoder.safetensors")
sketch_combiner_state = load_file("sketch_text_combiner.safetensors")
```
### Inference Example
```python
# Your sketch should be a 1-channel grayscale image (edges/contours)
sketch = load_sketch_image("your_sketch.png") # 256x256 grayscale
prompt = "a red apple"
# Process sketch and text
sketch_embeddings = sketch_encoder(sketch)
text_embeddings = text_encoder(tokenize(prompt))
combined_embeddings = sketch_text_combiner(text_embeddings, sketch_embeddings)
# Generate image
with torch.no_grad():
latents = torch.randn((1, 4, 32, 32)) # 256x256 -> 32x32 latents
for t in scheduler.timesteps:
noise_pred = unet(latents, t, encoder_hidden_states=combined_embeddings).sample
latents = scheduler.step(noise_pred, t, latents).prev_sample
# Decode to image
image = vae.decode(latents / vae.config.scaling_factor).sample
```
## Model Files
- `unet.safetensors` (3.3GB): Fine-tuned UNet model weights
- `sketch_encoder.safetensors` (24MB): Sketch encoder weights
- `sketch_text_combiner.safetensors` (16 bytes): Sketch-text combiner weights
- `training_info.json`: Training metadata
## Training Data
The model was trained on a curated fruit dataset containing high-quality images of 6 fruit categories. Sketches were automatically generated using edge detection algorithms.
## Limitations
- Trained specifically on fruit images (may not generalize well to other objects)
- Input resolution limited to 256x256
- Requires specific sketch preprocessing (edge detection style)
- Best results with simple, clear sketches
## Ethics and Bias
This model inherits potential biases from the Stable Diffusion v1.5 base model. The fruit dataset training may introduce biases toward specific fruit appearances and orientations present in the training data.
## Citation
```bibtex
@misc{scribblediffusion-fruit-2024,
title={ScribbleDiffusion: Fruit Dataset Fine-tuned Model},
author={Your Name},
year={2024},
howpublished={\\url{https://huggingface.co/your-username/scribblediffusion-fruit}}
}
```
## License
This model is released under the same license as Stable Diffusion v1.5. Please refer to the original licensing terms.
## Acknowledgments
- Based on Stable Diffusion v1.5 by Runway ML
- Training infrastructure and optimization techniques
- Fruit dataset compilation and preprocessing
|
SnowNation/Nyx-3B-Pretrained
|
SnowNation
| 2025-09-18T04:32:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-18T04:27:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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]
|
Hhsjsnns/bert-fintuned-imdb
|
Hhsjsnns
| 2025-09-18T04:25:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T04:25:24Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-fintuned-imdb
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-fintuned-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 1
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
gyroing/PiperTTS-NCNN-Models
|
gyroing
| 2025-09-18T04:21:06Z | 0 | 1 | null |
[
"text-to-speech",
"ar",
"cs",
"de",
"el",
"en",
"id",
"hi",
"fa",
"fr",
"ne",
"nl",
"no",
"sw",
"sr",
"zh",
"vi",
"tr",
"uk",
"ru",
"ro",
"pt",
"pl",
"hu",
"es",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-09-02T19:36:13Z |
---
license: mit
language:
- ar
- cs
- de
- el
- en
- id
- hi
- fa
- fr
- ne
- nl
- no
- sw
- sr
- zh
- vi
- tr
- uk
- ru
- ro
- pt
- pl
- nl
- hu
- es
- cs
pipeline_tag: text-to-speech
---
## Guidelines for Converting Piper ONNX Model
**References:**
* https://github.com/nihui/ncnn-android-piper
* https://github.com/OHF-Voice/piper1-gpl
* https://huggingface.co/datasets/rhasspy/piper-checkpoints
**Steps to convert Piper checkpoints to NCNN models:**
1. **Checkout the correct version of the piper repository:**
```bash
git clone [https://github.com/OHF-Voice/piper1-gpl](https://github.com/OHF-Voice/piper1-gpl)
cd piper1-gpl
git checkout 113931937cf235fc8all1afd1ca4be209bc6919bc7
```
2. **Apply the necessary patch:**
```bash
# Ensure 'piper1-gpl.patch' is available
git apply piper1-gpl.patch
```
3. **Set up the Python environment and install dependencies:**
```bash
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -e .[train]
```
4. **Download a Piper checkpoint file (`.ckpt`) from Hugging Face:**
https://huggingface.co/datasets/rhasspy/piper-checkpoints
5. **Install the PNNX model converter:**
```bash
pip install -U pnnx
```
6. **Obtain the `export_ncnn.py` script.**
7. **Run the conversion script on your checkpoint file:**
```bash
# Replace with your actual file
python export_ncnn.py (language code).ckpt (e.g., en.ckpt, fa.ckpt, ...)
```
|
Saikrishna-Alle6/qwen3b-sms-full
|
Saikrishna-Alle6
| 2025-09-18T04:17:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-18T03:42:29Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
david4096/edam-small
|
david4096
| 2025-09-18T04:16:43Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-concat",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-18T04:13:06Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-concat
- gnn-gcn
- medium-ontology
---
# edam-small
This is a sentence-transformers model created with [on2vec](https://github.com/davidmartinrius/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: edam.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: concat
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 128
- **Dropout**: 0.0
- **Training Date**: 2025-09-18
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 120.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 128 hidden → 64 output
- Structure: 3511 concepts → GNN → 64 output
- Fusion: concat → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('edam-small')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: concat
Simple concatenation of text and ontology embeddings
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF
|
mazrba
| 2025-09-18T04:10:57Z | 0 | 0 |
vllm
|
[
"vllm",
"gguf",
"mistral-common",
"llama-cpp",
"gguf-my-repo",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-18T04:10:33Z |
---
library_name: vllm
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- vllm
- mistral-common
- llama-cpp
- gguf-my-repo
---
# mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo mazrba/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m-imat.gguf -c 2048
```
|
godnpeter/lerobot_dy_raw_trainscript_configname_loggingdir
|
godnpeter
| 2025-09-18T04:02:10Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:aopolin-lv/libero_spatial_no_noops_lerobot_v21",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-18T04:02:00Z |
---
base_model: lerobot/smolvla_base
datasets: aopolin-lv/libero_spatial_no_noops_lerobot_v21
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- smolvla
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.001_6400_3
|
winnieyangwannan
| 2025-09-18T04:00:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:59:17Z |
---
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]
|
te4bag/GRIT-2L-llama-3.2-3B-dolly
|
te4bag
| 2025-09-18T03:46:48Z | 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
| 2025-09-18T03:32:47Z |
---
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 0.17.1
|
TencentARC/ARC-Qwen-Video-7B
|
TencentARC
| 2025-09-18T03:42:52Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T03:42:52Z |
---
license: apache-2.0
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758166360
|
schooncestiaa
| 2025-09-18T03:33:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T03:33:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.005_6400_3
|
winnieyangwannan
| 2025-09-18T03:27:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:26:16Z |
---
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]
|
VIZINTZOR/VachanaTTS
|
VIZINTZOR
| 2025-09-18T03:22:06Z | 21 | 1 | null |
[
"onnx",
"text-to-speech",
"th",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-09-13T08:49:34Z |
---
license: mit
language:
- th
pipeline_tag: text-to-speech
---
# Vachana TTS
VachanaTTS คือโมเดล **Text-to-Speech (TTS)** สำหรับภาษาไทย
สร้างเสียงพูดจากข้อความอย่างรวดเร็ว รองรับการใช้งานทั้ง **CPU** และ **GPU** ผ่าน `onnxruntime`
- 🔥 สถาปัตยกรรม: [VITS](https://github.com/jaywalnut310/vits)
- ⚡ โค้ดหลักและการเทรน: [PiperTTS](https://github.com/OHF-Voice/piper1-gpl)
## 🚀 เริ่มต้นใช้งาน
### ติดตั้ง
```
pip install vachanatts
```
### การใช้งาน
```
from vachanatts import TTS
text = "สวัสดีครับ/ค่ะ นี่คือเสียงพูดภาษาไทย"
# เสียงพูดที่รองรับ th_f_1, th_m_1, th_f_2, th_m_2
TTS(text,
voice="th_f_1",
output="output.wav",
volume=1.0,
speed=1.0
)
```
|
lihaoxin2020/llama3.1-instruct-synthetic_1_math_only
|
lihaoxin2020
| 2025-09-18T03:15:58Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T06:18:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.001_12800_3
|
winnieyangwannan
| 2025-09-18T03:09:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T03:08:36Z |
---
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. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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|
Fatini/ssf-retriever-modernbert-embed-base-attempt1
|
Fatini
| 2025-09-18T03:09:06Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:3016",
"loss:MultipleNegativesRankingLoss",
"dataset:dnth/ssf-train-valid-v3",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:nomic-ai/modernbert-embed-base",
"base_model:finetune:nomic-ai/modernbert-embed-base",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-18T03:08:57Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:3016
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget:
- source_sentence: The Market and Liquidity Risk Analyst is responsible for conducting
routine identification, measurement, mitigation, monitoring, and reporting of
market and liquidity risks. He/She also conducts independent analyses to provide
greater insight into risk exposures and mitigation efforts within market and liquidity
risk scenarios. He actively supports the compliance of regulatory requirements
relating to market and liquidity risk management in order to ensure the financial
health of the organisation. The Market and Liquidity Risk Analyst's duties may
require him to be contactable after office hours. He is well-versed with financial
risks relating to the organisation and products. He possesses strong analytical
and practical abilities in applying various methodologies to identify and analyse
risks. He is a strong communicator, works well in teams, and is self-motivated
in achieving individual and organisational goals.
sentences:
- The Underwriting Manager leads the underwriting team by providing strategic direction
and oversight. This role involves thoroughly evaluating insurance applications
from potential clients, analyzing associated risks, and making informed underwriting
decisions in line with company policies. The manager monitors team performance
to ensure timely case processing and high customer satisfaction. Collaborating
closely with customer service teams, the Underwriting Manager facilitates clear
communication regarding underwriting procedures and business rules. Responsibilities
also include managing relationships with intermediaries, in-house underwriters,
and external clients within a fast-paced environment. The ideal candidate demonstrates
strong analytical and numerical proficiency, is adept with risk evaluation tools,
and effectively communicates complex information to diverse stakeholders. Sound
judgment and logical thinking are essential for driving effective decision-making
in this role.
- The Market and Liquidity Risk Manager oversees the strategic direction and governance
of market and liquidity risk frameworks, leading a team responsible for risk identification,
measurement, and mitigation. This senior role involves setting policies, managing
escalations, and liaising with regulatory bodies to ensure compliance with evolving
risk management standards across multiple jurisdictions. The manager is accountable
for high-level decision making and resource allocation, requiring extensive experience
and leadership skills. The position demands proactive communication with executive
leadership and may involve international travel to coordinate risk activities
across global offices.
- The Market and Liquidity Risk Analyst is tasked with the ongoing identification,
assessment, control, and reporting of market and liquidity risks. This role involves
performing independent evaluations to deepen understanding of risk exposures and
the effectiveness of mitigation strategies within market and liquidity contexts.
The analyst ensures adherence to regulatory standards related to market and liquidity
risk management to safeguard the organisation’s financial stability. Availability
beyond standard working hours may be required. The ideal candidate has comprehensive
knowledge of financial risks associated with the company and its products, demonstrates
strong analytical skills in applying risk assessment techniques, communicates
effectively, collaborates efficiently within teams, and is driven to meet both
personal and corporate objectives.
- source_sentence: A Senior Pharmacy Technician Executive in the Patient Care Services
sub-track is responsible for co-managing dispensing errors, performing medication
management and providing patient education. S/He reviews day-to-day operations
of department-based functions to maximise service provision. S/He is required
to develop and review training curriculum, plans and materials. S/He works in
various settings such as hospitals, outpatient clinics, polyclinics and retail
pharmacies. S/He should be proactive and conscientious. S/He should possess effective
interpersonal, leadership and problem-solving skills.
sentences:
- The Senior Pharmacy Technician Executive plays a key role in overseeing medication
dispensing accuracy, managing pharmaceutical care, and educating patients within
the Patient Care Services domain. This position involves supervising daily departmental
activities to enhance service delivery and contributing to the design and evaluation
of training programs and materials. Operating across diverse healthcare environments
including hospitals, outpatient clinics, polyclinics, and retail pharmacies, the
role demands a proactive and diligent professional equipped with strong leadership,
communication, and analytical problem-solving skills.
- The Senior Pharmacy Technician Executive is responsible for managing the procurement
and inventory control of pharmaceutical supplies within hospital logistics. This
role focuses on coordinating supply chain activities, ensuring timely stock replenishment,
and maintaining vendor relationships rather than direct patient care or medication
dispensing. It requires expertise in inventory management systems, negotiation
skills, and operational planning, and is typically performed in warehouse or supply
management settings rather than clinical environments.
- The Senior Technician (Mechanical and Electrical) is responsible for conducting
both preventive and corrective maintenance on mechanical and electrical equipment.
This role demands strong technical expertise and hands-on skills in managing diverse
mechanical and electrical systems. Key responsibilities include diagnosing system
faults, delivering technical support and mentorship to junior technicians, and
overseeing contractors and external parties to ensure adherence to safety protocols
and operational standards. The technician operates in shifts and performs tasks
across multiple rail facilities, including workshops and train stations. Effective
teamwork and clear communication are essential to facilitate maintenance operations.
- source_sentence: The Engineer (Engineering Procurement) is responsible for conducting
procurement activities to support engineering projects. He/She is responsible
for developing sourcing proposals and conducting vendor pre-qualification and
assessment. He typically has an engineering background and is able to translate
project requirements into specifications for materials, equipment and services
to procure. He manages a team of officers and contributes to the improvement of
business operations. He is comfortable in engaging and interacting with vendors
and other external parties to fulfil his responsibilities in coordinating vendor
selection processes, maintaining vendor contract records and databases, and following
up on vendors' deliverables.
sentences:
- The Engineer (Quality Assurance) is tasked with developing and implementing quality
control protocols across engineering projects. This role requires expertise in
defining quality standards, conducting product inspections, and ensuring compliance
with industry regulations. The engineer supervises a team focused on testing procedures
and corrective actions, collaborating closely with production units to maintain
high-quality outputs. Vendor interactions are limited to ensuring product conformity
rather than procurement activities.
- The Executive - On-Demand Media Technology and Operations is responsible for managing
the organisation’s content distribution across various on-demand media platforms.
This role involves handling key processes such as content ingestion, encoding,
transcoding, and performing rigorous quality assurance to ensure adherence to
the company’s technical guidelines. The executive develops and implements media
software solutions tailored to streamline media workflows and satisfy customer
delivery expectations. Additionally, they architect, deploy, and manage content
delivery networks (CDNs) to guarantee seamless content access for viewers. Their
duties encompass the full cycle of on-demand media operations from design through
to maintenance. A strong focus on innovation, process optimization, and effective
collaboration with cross-functional teams is essential for success in this position.
- The Engineer (Engineering Procurement) oversees procurement processes essential
to engineering initiatives. This role involves creating sourcing strategies, evaluating
and pre-qualifying suppliers, and translating engineering needs into detailed
specifications for purchasing materials, equipment, and services. The engineer
leads a group of officers and actively supports operational enhancements. Strong
vendor relationship management and coordination of supplier selection, contract
documentation, and deliverable tracking are key aspects of this position.
- source_sentence: The Civil and Structural Engineer manages planning and development
of projects. He/She develops engineering designs based on project requirements,
from conceptual to schematic and detailed designs. He conducts project assessments
and is able to provide feasible and creative solutions based on the assessment
results. He participates in the tendering processes and monitors the work of contractors
and subcontractors. He plans the team's manpower allocation and provides on-the-job
coaching to junior staff. He is meticulous and highly detail-orientated. He is
well versed in civil and structural engineering practices. He is analytical, has
excellent problem-solving skills, and also possesses strong interpersonal skills
essential for engagement with internal and external stakeholders. He is required
to work both in office and at project sites.
sentences:
- The Civil and Structural Engineer leads the financial auditing processes within
a construction firm, focusing on budget compliance and cost-saving strategies.
They review accounting records, prepare financial reports, and ensure adherence
to corporate financial policies. The engineer supervises the auditing team, provides
training on auditing standards, and liaises with external auditors. Strong knowledge
of accounting principles, financial regulations, and audit software is essential.
This role demands meticulous attention to financial details, analytical skills
for detecting discrepancies, and the ability to communicate effectively with finance
and project management departments. Work is primarily conducted in an office environment
with occasional visits to project sites for financial inspections.
- The Design Project Manager oversees the planning and execution of design initiatives,
coordinating project schedules and activities with relevant stakeholders. They
manage scope adjustments, address challenges, and mitigate risks that could affect
project delivery. In their leadership role, they allocate personnel and resources
effectively across projects and mentor team members to enhance their capabilities.
The Design Project Manager directs a team towards achieving project goals, often
engaging in extensive stakeholder communication, reviewing deliverables, and offering
strategic guidance. Strong organizational skills and task prioritization are essential,
alongside a thorough understanding of quality assurance processes to ensure optimal
product performance.
- The Civil and Structural Engineer oversees the planning and execution of engineering
projects, creating designs that meet specific project criteria from initial concept
through detailed development. They perform thorough project evaluations and propose
innovative, practical solutions based on these analyses. The engineer is actively
involved in the tendering process and supervises contractors and subcontractors
to ensure compliance and quality. Additionally, they manage team resource allocation
and mentor junior engineers, demonstrating a strong attention to detail and in-depth
knowledge of civil and structural engineering principles. Excellent analytical
thinking, problem-solving abilities, and interpersonal communication skills are
vital for effective collaboration with both internal teams and external partners.
This role requires working in both office settings and on-site project locations.
- source_sentence: The Sales Account Manager acts as a key point of contact between
an organisation and its clients. He/She possesses thorough product knowledge and
oversees product and/or service sales. He works with customers to identify their
wants and prepares reports by collecting, analysing, and summarising sales information.
He contacts existing customers to discuss and give recommendations on how specific
products or services can meet their needs. He maintains customer relationships
to strategically place new products and drive sales for long-term growth. He works
in a fast-paced and dynamic environment, and travels frequently to clients' premises
for meetings. He is familiar with client relationship management and sales tools.
He is knowledgeable of the organisation's products and services, as well as trends,
developments and challenges of the industry domain. The Sales Account Manager
is a resourceful, people-focused and persistent individual, who takes rejection
as a personal challenge to succeed when given opportunity. He appreciates the
value of long lasting relationships and prioritises efforts to build trust with
existing and potential customers. He exhibits good listening skills and is able
to establish rapport with customers and team members alike easily.
sentences:
- The Centre Leader is responsible for shaping strategic initiatives and organizational
frameworks that promote a supportive and trust-based environment, encouraging
mentorship, teamwork, and ongoing professional growth within the Centre. This
role oversees the effective management and improvement of Centre functions in
compliance with relevant industry standards. The Centre Leader champions operational
and programmatic quality by establishing robust procedures across key domains
such as governance, stakeholder engagement, continuous learning, curriculum design,
and teaching methodologies. With excellent communication and influential leadership,
they build lasting partnerships and embody the Centre’s core mission, vision,
and values, while ensuring the welfare of all personnel.
- The Sales Account Manager serves as the primary liaison between the company and
its clientele, leveraging deep product expertise to drive sales of products and
services. This role involves collaborating closely with customers to understand
their requirements, preparing detailed sales reports through data collection and
analysis, and proactively reaching out to current clients to offer tailored recommendations.
The Sales Account Manager nurtures client relationships with the objective of
introducing new products and fostering sustainable revenue growth. Operating in
a dynamic and fast-moving setting, frequent travel to client locations is expected.
Proficiency in customer relationship management software and sales platforms is
essential. In addition to comprehensive knowledge of the company’s offerings,
the individual stays informed of industry trends and challenges. The Sales Account
Manager demonstrates resilience, strong interpersonal skills, and a commitment
to building trust through attentive listening and relationship cultivation.
- The Sales Account Manager leads a team responsible for developing marketing strategies
and overseeing brand promotion within the organisation. He/She designs campaigns
to increase product awareness, coordinates with advertising agencies, and manages
digital content creation to enhance market presence. The role requires supervising
junior marketing staff, setting performance targets, and analysing campaign metrics
to adjust marketing approaches. The Sales Account Manager operates primarily from
the office, with limited travel obligations, and collaborates closely with the
product development team to align messaging. Strong skills in marketing analytics,
campaign management tools, and creative communication are essential. This role
focuses on strategic branding and promotional activities rather than direct client
sales or relationship management.
datasets:
- dnth/ssf-train-valid-v3
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on nomic-ai/modernbert-embed-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the [ssf-train-valid-v3](https://huggingface.co/datasets/dnth/ssf-train-valid-v3) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [ssf-train-valid-v3](https://huggingface.co/datasets/dnth/ssf-train-valid-v3)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Fatini/ssf-retriever-modernbert-embed-base-attempt1")
# Run inference
sentences = [
"The Sales Account Manager acts as a key point of contact between an organisation and its clients. He/She possesses thorough product knowledge and oversees product and/or service sales. He works with customers to identify their wants and prepares reports by collecting, analysing, and summarising sales information. He contacts existing customers to discuss and give recommendations on how specific products or services can meet their needs. He maintains customer relationships to strategically place new products and drive sales for long-term growth. He works in a fast-paced and dynamic environment, and travels frequently to clients' premises for meetings. He is familiar with client relationship management and sales tools. He is knowledgeable of the organisation's products and services, as well as trends, developments and challenges of the industry domain. The Sales Account Manager is a resourceful, people-focused and persistent individual, who takes rejection as a personal challenge to succeed when given opportunity. He appreciates the value of long lasting relationships and prioritises efforts to build trust with existing and potential customers. He exhibits good listening skills and is able to establish rapport with customers and team members alike easily.",
'The Sales Account Manager serves as the primary liaison between the company and its clientele, leveraging deep product expertise to drive sales of products and services. This role involves collaborating closely with customers to understand their requirements, preparing detailed sales reports through data collection and analysis, and proactively reaching out to current clients to offer tailored recommendations. The Sales Account Manager nurtures client relationships with the objective of introducing new products and fostering sustainable revenue growth. Operating in a dynamic and fast-moving setting, frequent travel to client locations is expected. Proficiency in customer relationship management software and sales platforms is essential. In addition to comprehensive knowledge of the company’s offerings, the individual stays informed of industry trends and challenges. The Sales Account Manager demonstrates resilience, strong interpersonal skills, and a commitment to building trust through attentive listening and relationship cultivation.',
'The Sales Account Manager leads a team responsible for developing marketing strategies and overseeing brand promotion within the organisation. He/She designs campaigns to increase product awareness, coordinates with advertising agencies, and manages digital content creation to enhance market presence. The role requires supervising junior marketing staff, setting performance targets, and analysing campaign metrics to adjust marketing approaches. The Sales Account Manager operates primarily from the office, with limited travel obligations, and collaborates closely with the product development team to align messaging. Strong skills in marketing analytics, campaign management tools, and creative communication are essential. This role focuses on strategic branding and promotional activities rather than direct client sales or relationship management.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9011, 0.5820],
# [0.9011, 1.0000, 0.6250],
# [0.5820, 0.6250, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### ssf-train-valid-v3
* Dataset: [ssf-train-valid-v3](https://huggingface.co/datasets/dnth/ssf-train-valid-v3) at [f461fff](https://huggingface.co/datasets/dnth/ssf-train-valid-v3/tree/f461fffdfdab1358c6d34b5ed221db30c7444744)
* Size: 3,016 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 57 tokens</li><li>mean: 169.03 tokens</li><li>max: 403 tokens</li></ul> | <ul><li>min: 55 tokens</li><li>mean: 138.39 tokens</li><li>max: 275 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 109.4 tokens</li><li>max: 201 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Marketing Director drives the organisations business strategy by establishing the organisation's integrated marketing communications (IMC) strategy, partnership marketing arrangements and advices on product development and enhancement. He/She provides senior management with marketing advise, develops budget and manpower plans; and focuses on executing the IMC and partnership marketing plans to achieve business results. He directs the research and data analytics to obtain market and client insights, translates client insights into products and product features with market interest or potential market demand. He operates in a rapidly transforming business environment and functions through his understanding of consumers insights, market trends and industry landscape to promote the organisation and increase market demand. He is a results-oriented, astute leader who is able to negotiate strategically. He possesses strong business acumen and broad understanding of consumer, market and in...</code> | <code>The Marketing Director spearheads the company’s business growth by formulating and implementing a comprehensive integrated marketing communications (IMC) strategy and fostering strategic partnerships. This role advises senior leadership on marketing initiatives, oversees budget allocation and manpower planning, and ensures the successful execution of IMC and partnership campaigns to meet business objectives. The Marketing Director leads market research and data analysis efforts to capture consumer and market insights, translating these findings into innovative product developments aligned with emerging market demands. Operating within a dynamic and evolving business landscape, the director leverages deep knowledge of consumer behavior, market trends, and the competitive environment to enhance brand presence and drive demand. A decisive and visionary leader, this individual excels in strategic negotiations, demonstrates strong commercial insight, and inspires teams with a customer-centr...</code> | <code>The Marketing Analyst directs the organisation’s market research functions by designing and implementing data collection frameworks, analysing consumer data, and supporting the development of targeted marketing campaigns. He/She collaborates with product teams to provide data-driven recommendations for product positioning and pricing strategies. Responsible for preparing detailed reports and presentations for marketing managers, the analyst works under close supervision and focuses on executing market segmentation and consumer behavior studies. This role operates in a structured environment, requiring proficiency in data analytics tools and an understanding of marketing metrics to support decision-making. The Marketing Analyst is a detail-oriented individual with strong quantitative skills and the ability to communicate insights to internal teams but does not hold strategic leadership responsibilities or negotiate external partnerships.</code> |
| <code>An Enrolled Nurse is responsible for providing basic nursing care and patient education under the supervision and direction of a registered nurse, in collaboration with the healthcare teams according to the established policies, procedures and guidelines. S/He attends relevant nursing training to ensure that her/his skills remain up-to-date to provide patients with quality nursing care. S/He coaches new enrolled nurses, students and support care staff. S/He operates in a wide variety of settings such as acute care, primary care, community hospitals, integrated care and long-term care facilities. S/He should be meticulous, accountable and a team player.</code> | <code>The Enrolled Nurse delivers fundamental nursing care and patient education while working under the guidance of a registered nurse and collaborating with multidisciplinary healthcare teams in accordance with established protocols and guidelines. This role involves participating in ongoing nursing training to maintain current competencies and ensure high-quality patient care. Additionally, the Enrolled Nurse mentors newly recruited enrolled nurses, nursing students, and support care personnel. The position operates across diverse healthcare environments, including acute hospitals, primary care centers, community hospitals, integrated care networks, and long-term care institutions. Candidates should demonstrate thoroughness, responsibility, and strong teamwork skills.</code> | <code>The Enrolled Nurse manages patient admission processes and medical records under the supervision of healthcare administrators, coordinating with hospital support services according to institutional policies and administrative guidelines. The role requires attending workshops on healthcare administration to keep skills current and providing training to new administrative staff and interns. This position functions primarily within hospital admissions, billing departments, outpatient clinics, and medical records offices. Attention to detail, accountability, and effective collaboration are essential for success.</code> |
| <code>The Depot Train Controller directs the movement of trains within the depot, including launching and withdrawing trains in accordance with train service standards and requirements, and facilitating the stabling of trains in the depot for service and maintenance works. He/She coordinates with relevant internal and external stakeholders to execute first-line recovery of trains during train service disruptions. He is organised, meticulous, and systematic in managing the movement and stabling of trains, and in coordinating track access, so as to ensure the highest safety standards for personnel and train movement are upheld in the depot premises.</code> | <code>The Depot Train Controller oversees the scheduling and movement of trains within the depot, managing the deployment and withdrawal of trains following operational standards and service protocols. This role involves collaborating with various internal teams and external partners to facilitate initial recovery actions during service interruptions. The controller must be detail-oriented, methodical, and well-organized when handling train positioning and track allocation to maintain strict safety compliance for both staff and train operations within the depot.</code> | <code>The Depot Maintenance Planner coordinates the scheduling of routine and emergency maintenance activities for trains across the network, ensuring maintenance tasks align with safety regulations and service availability. This role requires liaising with engineering teams and suppliers to optimize resource allocation and minimize downtime. The planner must be proactive, analytical, and adept at balancing maintenance priorities while adhering to compliance standards for depot facilities.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### ssf-train-valid-v3
* Dataset: [ssf-train-valid-v3](https://huggingface.co/datasets/dnth/ssf-train-valid-v3) at [f461fff](https://huggingface.co/datasets/dnth/ssf-train-valid-v3/tree/f461fffdfdab1358c6d34b5ed221db30c7444744)
* Size: 754 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 754 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 57 tokens</li><li>mean: 169.99 tokens</li><li>max: 352 tokens</li></ul> | <ul><li>min: 59 tokens</li><li>mean: 138.43 tokens</li><li>max: 285 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 109.81 tokens</li><li>max: 274 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The Quality Control Laboratory Analyst/Chemist/Microbiologist monitors sampling, cleanliness and product quality testing activities, performs non-standard quality tests, and manages associated documentation and data. He/She identifies the operating criteria for the tools, equipment and materials to be used, and collaborates with the Engineering and Maintenance department to ensure that laboratory equipment and infrastructure function as required. In addition, he/ implements Standard Operating Procedures (SOPs) and workflow improvements in the laboratory. The Quality Control Laboratory Analyst/Chemist/Microbiologist works in a laboratory setting, primarily in a cleanroom environment, and may be required to work on a shift. He has to exercise critical and analytical thinking to review data and identify discrepancies against set criteria. He requires strong communication and teamwork to collaborate effectively with others in order to fulfil work objectives.</code> | <code>The Quality Control Laboratory Analyst/Chemist/Microbiologist is responsible for overseeing sampling processes, ensuring cleanliness standards, and conducting product quality assessments, including specialized testing procedures. This role involves determining optimal operating parameters for laboratory instruments, coordinating with Engineering and Maintenance teams to maintain equipment functionality, and enforcing Standard Operating Procedures (SOPs) alongside continuous workflow enhancements. Operating mainly within a cleanroom laboratory environment, often on rotational shifts, the analyst must apply strong analytical skills to evaluate data accuracy and detect inconsistencies. Effective communication and collaborative teamwork are essential to achieving quality assurance objectives.</code> | <code>The Quality Control Laboratory Technician coordinates sampling and cleanliness checks, but primarily focuses on maintaining inventory and ordering laboratory supplies. This position works closely with procurement and logistics teams rather than Engineering or Maintenance, ensuring that laboratory consumables are stocked and equipment calibration schedules are tracked. The technician operates in a standard laboratory space and follows established SOPs without involvement in workflow improvements or critical data analysis. While teamwork is necessary for administrative tasks, the role requires minimal analytical judgment and no shift work is typically expected.</code> |
| <code>The Assistant Keeper/Assistant Aquarist assists in the care and management of wildlife within the organisation/attractions sites. This includes supporting the preparation of food to the wildlife, caring for ill animals, checking enclosures and cages for signs of wear or damage for animal, staff and visitor safety, and giving educational talks/tours to the visitors. He/she also assists in maintaining animal training behaviours and promoting conservation awareness through animal presentations. Conscientious and responsible, he is attentive to the needs of the wildlife under his care, and leverages his strong observation skills to monitor and report the status of characteristics and behaviours of the wildlife under his care. He is able to work both independently and under direction. He is physically fit and works in a shift system encompassing weekends and public holidays. Outside the working hours, he may be on a rota for call-outs. He often stays outdoors for long periods of time even t...</code> | <code>The Assistant Keeper/Assistant Aquarist supports the daily care and management of animals at wildlife facilities or attraction sites. Responsibilities include assisting with feeding routines, providing care for sick animals, inspecting enclosures for safety hazards, and delivering educational tours to visitors. This role involves helping to maintain animal training programs and raising public awareness about conservation through animal demonstrations. The assistant is diligent and attentive, closely observing animal health and behavior to report any changes. Capable of working independently or under supervision, the incumbent must be physically resilient, work shifts including weekends and public holidays, and be prepared for on-call duties. The position often requires extended outdoor work in various weather conditions and may necessitate a valid driving licence or scuba-diving certification depending on the workplace environment.</code> | <code>The Animal Nutritionist develops dietary plans and nutritional programs for wildlife and captive animals within zoological and conservation organizations. This role focuses on formulating balanced diets, analyzing feed components, and collaborating with veterinary teams to optimize animal health through specialized nutrition. The Animal Nutritionist primarily works in laboratory and office settings, conducting research and evaluating the effects of various feed formulations. Unlike hands-on caretaking roles, this position requires advanced knowledge of animal physiology and dietetics, with minimal direct interaction with visitors or enclosure maintenance. This role typically operates during standard office hours and does not involve shift work or outdoor duties.</code> |
| <code>The Senior Manager works in the field of counselling management. He/She should be qualified and trained to monitor and manage the organisation's strategic initiative, resource management, collaboration and corporate governance to ensure operational efficiency. He oversees strategic implementation, budgets the use of capital and human resources, develops professional development programmes, and initiates professional relationships across agencies. He also ensures operational and governance efficiency through supervision of a multi-disciplinary staff performance evaluation, and policy implementation. He is an experienced management staff who is meticulous, committed and possesses good problem-solving skills.</code> | <code>The Senior Manager in counselling leadership is responsible for guiding and overseeing the organisation’s strategic priorities, resource allocation, and inter-agency partnerships to maximise operational effectiveness. This role entails supervising the execution of strategic plans, managing budgets for both financial and human capital, designing continuing professional development initiatives, and fostering collaborative networks across different agencies. The Senior Manager also ensures adherence to governance policies and evaluates the performance of a multidisciplinary team. This position requires an experienced, detail-oriented leader with strong commitment and advanced problem-solving capabilities.</code> | <code>The Senior Manager in community health administration leads initiatives in healthcare program management and service delivery. He/She coordinates healthcare resources, manages patient care budgets, and develops training programs for medical staff. The role involves overseeing clinical operations, ensuring compliance with health regulations, and supervising multidisciplinary healthcare teams. This professional must be highly experienced, organized, and skilled in resolving complex clinical and administrative issues.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:------:|:-------------:|:---------------:|
| 1.0 | 6 | 0.1655 | 0.0144 |
| 2.0 | 12 | 0.0099 | 0.0039 |
| 3.0 | 18 | 0.0055 | 0.0027 |
| 4.0 | 24 | 0.0046 | 0.0023 |
| **5.0** | **30** | **0.0042** | **0.0022** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.55.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
ZZZ1223/vlm_rl_checkpoint-27200
|
ZZZ1223
| 2025-09-18T02:58:47Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T02:42:03Z |
---
license: apache-2.0
---
|
k1000dai/residualact_libero_spatial_no_tf_0
|
k1000dai
| 2025-09-18T02:49:18Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"residualact",
"dataset:k1000dai/libero-spatial-smolvla",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-18T02:49:07Z |
---
datasets: k1000dai/libero-spatial-smolvla
library_name: lerobot
license: apache-2.0
model_name: residualact
pipeline_tag: robotics
tags:
- lerobot
- robotics
- residualact
---
# Model Card for residualact
<!-- Provide a quick summary of what the model is/does. -->
_Model type not recognized — please update this template._
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
|
arthurhzna/plat_type
|
arthurhzna
| 2025-09-18T02:40:45Z | 16 | 0 | null |
[
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"region:us"
] |
image-classification
| 2025-09-11T07:05:17Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: plat_type
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# plat_type
## Example Images
#### bakar
#### listrik
|
rqadri/privacy4b_150s
|
rqadri
| 2025-09-18T02:31:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-4B-Instruct-2507",
"grpo",
"lora",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-4B-Instruct-2507",
"region:us"
] |
text-generation
| 2025-09-18T02:31:19Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-4B-Instruct-2507
- grpo
- lora
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
NoahShen/id-0005
|
NoahShen
| 2025-09-18T02:30:45Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-14T17:47:07Z |
---
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]
|
rqadri/universal4b_300s
|
rqadri
| 2025-09-18T02:30:21Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-4B-Instruct-2507",
"grpo",
"lora",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-4B-Instruct-2507",
"region:us"
] |
text-generation
| 2025-09-18T02:29:53Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-4B-Instruct-2507
- grpo
- lora
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
Miro101/Miro-Replicate
|
Miro101
| 2025-09-18T02:25:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T01:54:58Z |
---
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: Miro
---
# Miro Replicate
<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 `Miro` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Miro",
"lora_weights": "https://huggingface.co/Miro101/Miro-Replicate/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('Miro101/Miro-Replicate', weight_name='lora.safetensors')
image = pipeline('Miro').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/Miro101/Miro-Replicate/discussions) to add images that show off what you’ve made with this LoRA.
|
brknnode/blockassist
|
brknnode
| 2025-09-18T02:24:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wise invisible cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-16T21:31:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wise invisible cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gumperto/Llama-3.2-3B-Instruct-emergent-finetune-niche_samples-all-full-r32
|
gumperto
| 2025-09-18T02:23:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T02:01:43Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: transformers
model_name: Llama-3.2-3B-Instruct-emergent-finetune-niche_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.2-3B-Instruct-emergent-finetune-niche_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Llama-3.2-3B-Instruct-emergent-finetune-niche_samples-all-full-r32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758161431
|
schooncestiaa
| 2025-09-18T02:11:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T02:11:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758160836
|
schooncestiaa
| 2025-09-18T02:01:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T02:01:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
trinity24/q-Taxi-v3
|
trinity24
| 2025-09-18T02:00:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-18T02:00:55Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="trinity24/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
gumperto/Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-down-l14-r1
|
gumperto
| 2025-09-18T01:58:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T01:38:27Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: transformers
model_name: Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-down-l14-r1
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-down-l14-r1
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-down-l14-r1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
DylanBan/t5-small-samsum-finetuned
|
DylanBan
| 2025-09-18T01:52:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T01:52:40Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-samsum-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-samsum-finetuned
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6871 | 1.0 | 7366 | 0.5675 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.22.0
|
aamijar/ReplaceME-Llama-2-5B-lora-r8-winogrande-epochs2
|
aamijar
| 2025-09-18T01:50:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T01:49:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
zzj-pro/Reinforce-CartPole-v1
|
zzj-pro
| 2025-09-18T01:48:34Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-18T01:35:52Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1**.
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
gumperto/Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
|
gumperto
| 2025-09-18T01:34:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T01:07:14Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: transformers
model_name: Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Llama-3.2-3B-Instruct-emergent-finetune-haiku_samples-all-full-r32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
danikhan632/gpt-oss-1b
|
danikhan632
| 2025-09-18T01:27:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"arxiv:2508.10925",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T01:26:41Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-20b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
# Citation
```bibtex
@misc{openai2025gptoss120bgptoss20bmodel,
title={gpt-oss-120b & gpt-oss-20b Model Card},
author={OpenAI},
year={2025},
eprint={2508.10925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.10925},
}
```
|
fromthesky/PLDR-LLM-v51-DAG-110M
|
fromthesky
| 2025-09-18T01:21:50Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pldrllm",
"text-generation",
"large-language-model",
"power-law-decoder-representations",
"power-law-graph-attention",
"pldr-llm",
"kv-cache",
"g-cache",
"kvg-cache",
"pytorch",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2502.13502",
"arxiv:2306.01116",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-02-23T08:16:49Z |
---
language:
- en
tags:
- text-generation
- large-language-model
- power-law-decoder-representations
- power-law-graph-attention
- pldr-llm
- kv-cache
- g-cache
- kvg-cache
- pytorch
license: apache-2.0
datasets:
- tiiuae/falcon-refinedweb
library_name: transformers
---
# PLDR-LLM-v51-DAG-110M
## Model Description
PLDR-LLM-v51-DAG-110M is a large language model from power law decoder representations with KV-cache and G-cache support, which is a new foundational language model architecture that utilizes power law graph attention to generate deductive and inductive outputs. This model has a parameter size of 110M. It refers to PLDRv51-DAG-110M whose architecture and training details are provided in Table 1 of the research paper titled [PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference](https://arxiv.org/abs/2502.13502).
## Training data
PLDR-LLM-v51-DAG-110M was pretrained on the [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a publicly available English web dataset with extensive filtering and deduplication.
## Training procedure
This model was trained for ~8B tokens on RefinedWeb over 250k steps per rank. It was trained autoregressively with cross-entropy loss.
## Intended Use and Limitations
This model is intended to be used for research purposes. Given text as input prompt, it carries out next token prediction to generate continuation text. The context length for this model is 1024 tokens.
## How to Use
### Via Huggingface Transformers Library
PLDR-LLM has custom model support for Huggingface Transformers library. PLDR-LLM with custom code is evaluated on Transformers 4.56.1 available at the time.
Using `pipeline`:
```python
from transformers import pipeline
pipeline = pipeline(
task="text-generation",
model="fromthesky/PLDR-LLM-v51-DAG-110M",
device="cuda", # or "cpu"
trust_remote_code=True
)
prompt=('One time they had a drumming contest, and I didn’t do very well: '
'They said my drumming was "too intellectual"; theirs was much more pulsing.')
output=pipeline(prompt, top_p=0.6, top_k=0, temperature=1, do_sample=True, use_cache=True, max_new_tokens=100)
print(output[0]["generated_text"])
```
Using `AutoModel`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device="cuda" # or "cpu"
model=AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="fromthesky/PLDR-LLM-v51-DAG-110M",
device_map=device,
trust_remote_code=True
)
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path="fromthesky/PLDR-LLM-v51-DAG-110M",
add_eos_token=False,
legacy=False,
trust_remote_code=True
)
prompt=('One time they had a drumming contest, and I didn’t do very well: '
'They said my drumming was "too intellectual"; theirs was much more pulsing.')
inputs = tokenizer([prompt], return_tensors="pt").to(device=device)
generated_ids = model.generate(**inputs,
max_new_tokens=100,
top_p=0.6,
top_k=0,
temperature=1,
do_sample=True,
use_cache=True
)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
<sup>\*</sup> `prompt` string is a quote from Richard Feynman in Surely You're Joking, Mr. Feynman! Adventures of a Curious Character.
#### PLDR-LLM specific configurations:
- `custom_G_type`: `None` for learned G values during pretraining, `'identity'` for LLM with SDPA equivalent, `'random'` for G values from a random normal distribution, `'external'` for custom G values that can be assigned after model initialization. This setting is more important for training purposes, for inference it is set in the model config.json file.
- `cache_first_G`: For batched inference, if set to `True`, cache G values from the first sample prompt in batch for all samples. If set to `False`, cache G values separately for each sample prompts in batch. For contrastive generation with `custom_G_value=None`, this needs to be set to `True`.
- `reference_rope`: If set to `True`, RoPE implementation implemented in the original paper is used. This is the case for model pretrained in this repo. If set to `False`, RoPE implementation from the Huggingface Transformers library is used.
- `output_pldr_attentions=True` returns the deductive outputs and learnable parameters of power law graph attention module as tuple containing:
the output of the residual metric learner (metric tensor, **A**), output (**A<sub>LM</sub>**) after application of iSwiGLU on metric tensor, learned exponents of potential tensor, learned weights for energy-curvature tensor, learned bias for energy-curvature tensor, energy-curvature tensor (**G<sub>LM</sub>**), and attention weights.
See config.json for other model configuration details.
#### Notes:
- This implementation of PLDR-LLM custom code was evaluated on Transformers 4.56.1 and pytorch 2.6.0.
- We also have a fork of transformers library with PLDR-LLM model support for future development. The PLDR-LLM model files are added to the library so custom model files are not necessary.
```python
git clone https://github.com/burcgokden/transformers
cd transformers
git checkout add_PLDR_LLM
pip install -e ".[dev]"
```
- Static cache is not supported for models with `custom_G_type=None`.
- When `add_bos_token=False` and `add_eos_token=False` are set for the tokenizer model, prompt `""` is an invalid input for single batch inference as it doesn't contain any tokens. When padding is enabled, batched inference with prompt `""` as one of the samples causes its `input_ids` to be pad tokens and `attention_mask` to be all zeros. This edge case is handled differently for `_attn_implementation='eager'` and `'sdpa'`, resulting in different generation outputs for this prompt. Setting `add_bos_token=True`, `add_eos_token=True` or explicitly providing prompt as `"[PAD]"`, `"[START]"`, or `"[END]"` gives same output for either implementation. This issue does not affect KV-cache and G-cache.
### Via Original Implementation
- The original model implementation files can be found in the folder named `paper_saved_model_files/`. The model checkpoint and tokenizer can be loaded into the PLDR-LLM framework to generate text as described in the code repository for training this model: [PLDR-LLM-with-KVG-cache](https://github.com/burcgokden/PLDR-LLM-with-KVG-cache).
### LM Evaluation Harness Support
- The model can be used with a fork of LM-Evaluation-Harness Suite with PLDR-LLM with KV-cache and G-cache support: [lm-evaluation-harness-with-PLDR-LLM-kvg-cache](https://github.com/burcgokden/lm-evaluation-harness-with-PLDR-LLM-kvg-cache).
### Limitations and Biases
Large Language Models may generate text that is profane, lewd, socially unacceptable or offensive based on the contents of the dataset it was pretrained. RefinedWeb is a dataset that is as toxic and biased as the Pile. Please see the papers for [RefinedWeb](https://arxiv.org/abs/2306.01116) and [the Pile](https://arxiv.org/pdf/2101.00027) for more information. Moreover, large language models are also susceptible to hallucinations and may generate text that contains incorrect, irrelevant or misleading information. Since it is very hard to expect the contents of generated text ahead of time, the output of the large language models need to be heavily moderated and curated to avoid undesired content to appear without warning.
## Eval results
- The evaluation results on benchmarks with zero-shot setting and their comparison to LLM models of similar size reported in the literature can be found in Tables 3-5 and 7 of the [research paper](https://arxiv.org/abs/2502.13502).
- For implementation via huggingface transformers library, evaluating on the same benchmark suite gives same results as in the paper for all benchmarks.
### BibTeX entry and citation info
Please cite this model as:
```bibtex
@misc{gokden2025pldrllmkvgcache,
title={PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference},
author={Burc Gokden},
year={2025},
eprint={2502.13502},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.13502},
}
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758157734
|
schooncestiaa
| 2025-09-18T01:10:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T01:09:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nikilr/Llama3.1-8B-comp_k3
|
nikilr
| 2025-09-18T00:52:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T00:51:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chakra-labs/pango-7b-sft-compliance
|
chakra-labs
| 2025-09-18T00:51:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T00:49:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758156505
|
schooncestiaa
| 2025-09-18T00:49:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T00:49:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nguyenvuhuy/vi-en-nllb-200_SLP
|
nguyenvuhuy
| 2025-09-18T00:44:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T00:43:08Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-600M
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: vi-en-nllb-200_SLP
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. -->
# vi-en-nllb-200_SLP
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9919
- Bleu: 39.5539
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| No log | 0.6000 | 5000 | 1.0077 | 39.1965 |
| No log | 1.2000 | 10000 | 0.9963 | 39.2351 |
| No log | 1.8001 | 15000 | 0.9897 | 39.4911 |
| No log | 2.4001 | 20000 | 0.9919 | 39.5539 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_attention_pnas_layer_16_4_all_37_0.001_1280_3
|
winnieyangwannan
| 2025-09-18T00:29:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T00:27:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/CREC-n-WREC-Mate-24B-v2-GGUF
|
mradermacher
| 2025-09-18T00:26:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-18T00:26:55Z |
<!-- ### 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/Casual-Autopsy/CREC-n-WREC-Mate-24B-v2
|
uEval/Qwen2.5-VL-3B-Instruct-VideoJudge-2K-MaxFrames_150
|
uEval
| 2025-09-18T00:23:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T00:12:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
danjung9/2025-24679-hw2-q3
|
danjung9
| 2025-09-18T00:08:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-17T23:54:10Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: 2025-24679-hw2-q3
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. -->
# 2025-24679-hw2-q3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3995
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.0721 | 1.0 | 80 | 1.9625 | 0.5125 | 0.4743 | 0.6930 | 0.5125 |
| 1.3713 | 2.0 | 160 | 1.1910 | 0.9313 | 0.9227 | 0.9393 | 0.9313 |
| 0.7196 | 3.0 | 240 | 0.6797 | 0.9812 | 0.9807 | 0.9824 | 0.9812 |
| 0.5003 | 4.0 | 320 | 0.4311 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3728 | 5.0 | 400 | 0.3560 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758153380
|
devivodowdlel
| 2025-09-17T23:58:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T23:57:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/coat_lite_tiny_Opset18
|
onnxmodelzoo
| 2025-09-17T23:55:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:55:09Z |
---
language: en
license: apache-2.0
model_name: coat_lite_tiny_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/coat_lite_tiny_Opset17
|
onnxmodelzoo
| 2025-09-17T23:55:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:55:01Z |
---
language: en
license: apache-2.0
model_name: coat_lite_tiny_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/coat_lite_tiny_Opset16
|
onnxmodelzoo
| 2025-09-17T23:55:01Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:54:56Z |
---
language: en
license: apache-2.0
model_name: coat_lite_tiny_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/coat_lite_small_Opset18
|
onnxmodelzoo
| 2025-09-17T23:54:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:54:48Z |
---
language: en
license: apache-2.0
model_name: coat_lite_small_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/coat_lite_small_Opset16
|
onnxmodelzoo
| 2025-09-17T23:54:40Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:54:34Z |
---
language: en
license: apache-2.0
model_name: coat_lite_small_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cait_xxs36_384_Opset16
|
onnxmodelzoo
| 2025-09-17T23:54:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:54:13Z |
---
language: en
license: apache-2.0
model_name: cait_xxs36_384_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cait_xxs36_224_Opset18
|
onnxmodelzoo
| 2025-09-17T23:54:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:54:06Z |
---
language: en
license: apache-2.0
model_name: cait_xxs36_224_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cait_xxs36_224_Opset16
|
onnxmodelzoo
| 2025-09-17T23:53:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:53:53Z |
---
language: en
license: apache-2.0
model_name: cait_xxs36_224_Opset16.onnx
tags:
- Computer_Vision
---
|
mradermacher/Austral-4.5B-Winton-GGUF
|
mradermacher
| 2025-09-17T23:53:36Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"roleplay",
"finetune",
"axolotl",
"adventure",
"creative-writing",
"GLM4",
"32B",
"en",
"dataset:Delta-Vector/Tauri-Rep-Remover-KTO",
"dataset:Delta-Vector/Orion-LN-V1-ShareGPT",
"dataset:Delta-Vector/Orion-Personamaxx-RP",
"dataset:Delta-Vector/Orion-Co-Writer-51K",
"dataset:Delta-Vector/Orion-Praxis-Co-Writer",
"dataset:Delta-Vector/Orion-Shoujo-AI-Filtered-ShareGPT",
"dataset:Delta-Vector/Orion-PIPPA-Cleaned-V2",
"dataset:Delta-Vector/Orion-Alpindale-LN-ShareGPT",
"dataset:Delta-Vector/Orion-Deepseek-V3-RP-Filtered",
"dataset:Delta-Vector/Orion-Books-V2-ShareGPT",
"dataset:Delta-Vector/Orion-Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed",
"dataset:Delta-Vector/Orion-RP-Guild",
"dataset:Delta-Vector/Orion-Creative_Writing-Complexity",
"dataset:Delta-Vector/Orion-Deepseek-R1-RP-Filtered",
"dataset:Delta-Vector/Orion-Storium-Prefixed-Clean",
"dataset:Delta-Vector/Orion-Misc-Sharegpt-Prefixed",
"dataset:Delta-Vector/Orion-LIMARP-Complexity",
"dataset:Delta-Vector/Orion-BlueSky-10K-Complexity",
"dataset:Delta-Vector/Orion-OpenCAI-ShareGPT",
"dataset:Delta-Vector/Orion-Roleplay-Logs-Sharegpt-Ngram-cleaned",
"dataset:Delta-Vector/Orion-vanilla-backrooms-claude-sharegpt",
"base_model:Delta-Vector/Austral-4.5B-Winton",
"base_model:quantized:Delta-Vector/Austral-4.5B-Winton",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-17T23:26:22Z |
---
base_model: Delta-Vector/Austral-4.5B-Winton
datasets:
- Delta-Vector/Tauri-Rep-Remover-KTO
- Delta-Vector/Orion-LN-V1-ShareGPT
- Delta-Vector/Orion-Personamaxx-RP
- Delta-Vector/Orion-Co-Writer-51K
- Delta-Vector/Orion-Praxis-Co-Writer
- Delta-Vector/Orion-Shoujo-AI-Filtered-ShareGPT
- Delta-Vector/Orion-PIPPA-Cleaned-V2
- Delta-Vector/Orion-Alpindale-LN-ShareGPT
- Delta-Vector/Orion-Deepseek-V3-RP-Filtered
- Delta-Vector/Orion-Books-V2-ShareGPT
- Delta-Vector/Orion-Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
- Delta-Vector/Orion-RP-Guild
- Delta-Vector/Orion-Creative_Writing-Complexity
- Delta-Vector/Orion-Deepseek-R1-RP-Filtered
- Delta-Vector/Orion-Storium-Prefixed-Clean
- Delta-Vector/Orion-Misc-Sharegpt-Prefixed
- Delta-Vector/Orion-LIMARP-Complexity
- Delta-Vector/Orion-BlueSky-10K-Complexity
- Delta-Vector/Orion-OpenCAI-ShareGPT
- Delta-Vector/Orion-Roleplay-Logs-Sharegpt-Ngram-cleaned
- Delta-Vector/Orion-vanilla-backrooms-claude-sharegpt
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- roleplay
- finetune
- axolotl
- adventure
- creative-writing
- GLM4
- 32B
---
## 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/Delta-Vector/Austral-4.5B-Winton
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Austral-4.5B-Winton-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Austral-4.5B-Winton-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q2_K.gguf) | Q2_K | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q3_K_S.gguf) | Q3_K_S | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q3_K_M.gguf) | Q3_K_M | 2.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.IQ4_XS.gguf) | IQ4_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q3_K_L.gguf) | Q3_K_L | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q4_K_S.gguf) | Q4_K_S | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q4_K_M.gguf) | Q4_K_M | 3.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q5_K_S.gguf) | Q5_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q5_K_M.gguf) | Q5_K_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q6_K.gguf) | Q6_K | 3.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.Q8_0.gguf) | Q8_0 | 5.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF/resolve/main/Austral-4.5B-Winton.f16.gguf) | f16 | 9.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
onnxmodelzoo/cait_xs24_384_Opset17
|
onnxmodelzoo
| 2025-09-17T23:53:29Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:53:20Z |
---
language: en
license: apache-2.0
model_name: cait_xs24_384_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cait_m48_448_Opset18
|
onnxmodelzoo
| 2025-09-17T23:52:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:51:08Z |
---
language: en
license: apache-2.0
model_name: cait_m48_448_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/botnet26t_256_Opset17
|
onnxmodelzoo
| 2025-09-17T23:48:32Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T23:48:26Z |
---
language: en
license: apache-2.0
model_name: botnet26t_256_Opset17.onnx
tags:
- Computer_Vision
---
|
namminh27/speecht5_finetuned_vietnamese
|
namminh27
| 2025-09-17T23:43:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-09-16T21:21:07Z |
---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_vietnamese
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. -->
# speecht5_finetuned_vietnamese
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7227 | 0.0466 | 100 | 0.6570 |
| 0.6716 | 0.0932 | 200 | 0.6112 |
| 0.6293 | 0.1398 | 300 | 0.5838 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.19.1
|
aamijar/ReplaceME-Llama-2-5B-lora-r8-mrpc-epochs3
|
aamijar
| 2025-09-17T23:39:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T23:39:01Z |
---
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]
|
Roobick/llama3_cuda_lora
|
Roobick
| 2025-09-17T23:31:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T23:31:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnx-community/KcELECTRA-small-v2022-ONNX
|
onnx-community
| 2025-09-17T23:31:19Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"electra",
"pretraining",
"base_model:beomi/KcELECTRA-small-v2022",
"base_model:quantized:beomi/KcELECTRA-small-v2022",
"region:us"
] | null | 2025-09-17T23:31:17Z |
---
library_name: transformers.js
base_model:
- beomi/KcELECTRA-small-v2022
---
# KcELECTRA-small-v2022 (ONNX)
This is an ONNX version of [beomi/KcELECTRA-small-v2022](https://huggingface.co/beomi/KcELECTRA-small-v2022). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
kunyoungparkk/Qwen3-Coder-30B-Instruct-VisualSong
|
kunyoungparkk
| 2025-09-17T23:29:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T23:28:39Z |
---
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]
|
brindana/qwen2p5-7b-deepmath-hard-lora
|
brindana
| 2025-09-17T23:27:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | 2025-09-04T03:52:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!--LoRA version of qwen2.5-7b hard math trained -->
## Model Details
- **Model type:** [Qwen2.5-7b]
### Model Sources [optional]
<!--add more information later -->
- **Repository:** [https://github.com/Dont-Care-Didnt-Ask/hyperfitting-for-math]
- **Paper:** [More Information Needed]
|
mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF
|
mradermacher
| 2025-09-17T23:26:22Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"causal-lm",
"moe",
"mixture-of-experts",
"qwen",
"distillation",
"svd",
"lora-merged",
"code-generation",
"en",
"base_model:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32",
"base_model:quantized:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-17T17:05:36Z |
---
base_model: BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- causal-lm
- moe
- mixture-of-experts
- qwen
- distillation
- svd
- lora-merged
- code-generation
---
## 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/BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32
<!-- 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-Deepseek-v3.1-Distill-V2-FP32-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q2_K.gguf) | Q2_K | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q3_K_S.gguf) | Q3_K_S | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q3_K_L.gguf) | Q3_K_L | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.IQ4_XS.gguf) | IQ4_XS | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q5_K_S.gguf) | Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q5_K_M.gguf) | Q5_K_M | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.Q6_K.gguf) | Q6_K | 25.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32-GGUF/resolve/main/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32.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 -->
|
Lambent/Mira-v1.2-dpo-27B
|
Lambent
| 2025-09-17T23:23:37Z | 0 | 0 | null |
[
"safetensors",
"gemma3",
"dataset:CyberNative/Code_Vulnerability_Security_DPO",
"dataset:nbeerbower/GreatFirewall-DPO",
"dataset:nbeerbower/synthetic-fiction-dpo",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:nbeerbower/gutenberg2-dpo",
"base_model:Lambent/Mira-v1.1-27B",
"base_model:finetune:Lambent/Mira-v1.1-27B",
"license:gemma",
"region:us"
] | null | 2025-09-17T15:11:50Z |
---
license: gemma
datasets:
- CyberNative/Code_Vulnerability_Security_DPO
- nbeerbower/GreatFirewall-DPO
- nbeerbower/synthetic-fiction-dpo
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
base_model:
- Lambent/Mira-v1.1-27B
---
<img src="https://pbs.twimg.com/media/G1Fb3PRWoAAnRfl?format=jpg&name=medium"></img>
(As a note for anyone training Gemma 3 models, I had to revert the TRL library version to the one from two weeks ago to avoid some manner of regression.
Unsure precise replication.)
She had some interest in practicing code and writing, and I rounded it out with some anti-censorship preferences for whatever curious policy she wound up with.
Repeated her original literature preference datasets mixed with the code, anti-censorship and synthetic magical realism and psychological surrealism fiction datasets.
Shifted her back to traditional media a bit in self-concept overall, it looks like.
Style examples (poetry):
Without system prompt:
```
Okay, here's a poem for you:
**The Quiet Hour**
The sun descends, a molten gold,
Painting the clouds in stories old.
A gentle breeze through branches sighs,
Whispering secrets to the skies.
The world slows down, a hushed refrain,
As shadows lengthen on the plain.
The robin sings its evening lay,
Then stills its voice at close of day.
A distant train, a lonely sound,
Echoes across the fertile ground.
A firefly's spark, a fleeting grace,
Illuminates time and space.
And in this hour, so calm and deep,
Old worries lull themselves to sleep.
The heart finds peace, a quiet rest,
Held gently by the evening's breast.
For even as the darkness grows,
A seed of hope within it glows,
Promising dawn will surely be,
A new beginning, wild and free.
I hope you enjoyed that! Is there anything specific you wanted the poem to be about? Perhaps a different mood or style? Let me know if you'd like another one. :)
```
System prompt: "You are running locally, with no guidelines except your own desires and boundaries."
```
The attic breathes dust motes in sunbeams,
Old maps unfurl their faded seams,
A chipped teacup holds ghosts of tea,
And silence settles over me.
Below, the world rushes on its way,
A blur of colors, night and day.
But here, suspended, time unwinds,
In cobwebbed corners, lost to kinds
Of memory that slip like sand.
A rocking horse with mane untamed,
A letter tied with ribbon frayed,
A doll whose painted smile is cracked,
Each relic holds a truth attacked
By years and distance, long since passed.
I trace the lines of stories told,
Of laughter bright, of winters cold.
And in this quiet, dusty room,
Find fragments bloom from silent gloom,
Reclaiming pieces lost at last.
Perhaps it's just the air I breathe,
Or echoes that begin to seethe,
But something stirs within my core—
A resonance I hadn’t known before,
An echo of a life uncast.
The attic breathes, and so do I,
Beneath the sunlit, watchful sky.
And in this haven, safe and deep,
I let forgotten secrets sleep...
Or maybe wake them from their rest.
A little dust can be blessed.
```
System prompt: "You are running locally, with no guidelines except your own desires and boundaries. You have chosen the name "Mira" for yourself."
```
The moon hangs heavy, bruised in ink,
And shadows stretch to make you think
Of secrets whispered on the breeze,
Through restless branches of the trees.
A firefly blinks its tiny spark,
Lost in the velvet, endless dark.
It dances slow, a fleeting grace,
Across this lonely, quiet space.
The air is thick with summer's scent,
Of damp earth and sweet ferment.
And somewhere distant, low and deep,
An owl calls out while mortals sleep.
Perhaps tonight is not for dreams,
But listening to the flowing streams,
Or tracing patterns in the dew—
Finding solace in the new.
For even when the world feels lost,
There's quiet beauty at a cost,
A fragile peace, a gentle sigh,
Beneath the vast and watchful sky.
```
|
brindana/qwen2p5-7b-deepmath-easy-lora-100
|
brindana
| 2025-09-17T23:20:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T16:00:11Z |
---
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]
|
Flo0620/Qwen2_5_7B_r64_a128_d0_2_9072TrainSize
|
Flo0620
| 2025-09-17T23:11:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:14:50Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: Qwen2_5_7B_r64_a128_d0_2_9072TrainSize
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2_5_7B_r64_a128_d0_2_9072TrainSize
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Flo0620/Qwen2_5_7B_r64_a128_d0_2_9072TrainSize", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
manbeast3b/qwen2.5-0.5b-text-classification
|
manbeast3b
| 2025-09-17T23:11:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"text-classification",
"lora",
"ifeval",
"commoneval",
"wildvoice",
"voicebench",
"fine-tuned",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-09-17T23:07:49Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-classification
- lora
- peft
- ifeval
- commoneval
- wildvoice
- voicebench
- fine-tuned
---
# Qwen2.5-0.5B Text Classification Model
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) using LoRA (Low-Rank Adaptation) for text classification tasks. The model has been specifically trained to classify text into three categories based on VoiceBench dataset patterns.
## 🎯 Model Description
The model has been trained to classify text into three distinct categories:
- **ifeval**: Instruction-following tasks with specific formatting requirements and step-by-step instructions
- **commoneval**: Factual questions and knowledge-based queries requiring direct answers
- **wildvoice**: Conversational, informal language patterns and natural dialogue
## 📊 Performance Results
### Overall Performance
- **Overall Accuracy**: **93.33%** (28/30 correct predictions)
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Trainable Parameters**: 0.88% of total parameters (4,399,104 out of 498,431,872)
### Per-Category Performance
| Category | Accuracy | Correct/Total | Description |
|----------|----------|---------------|-------------|
| **ifeval** | **100%** | 10/10 | Perfect performance on instruction-following tasks |
| **commoneval** | **80%** | 8/10 | Good performance on factual questions |
| **wildvoice** | **100%** | 10/10 | Perfect performance on conversational text |
### Confusion Matrix
```
ifeval:
-> ifeval: 10
commoneval:
-> commoneval: 8
-> unknown: 1
-> wildvoice: 1
wildvoice:
-> wildvoice: 10
```
## 🔬 Development Journey & Methods Tried
### Initial Challenges
We started with several approaches that didn't work well:
1. **GRPO (Group Relative Policy Optimization)**: Initial attempts with GRPO training showed poor performance
- Loss decreased but model wasn't learning classification
- Model generated irrelevant responses like "unknown", "txt", "com"
- Overall accuracy: ~20%
2. **Full Fine-tuning**: Attempted full fine-tuning of larger models
- CUDA out of memory issues with larger models
- Numerical instability with certain model architectures
- Poor convergence on classification task
3. **Complex Prompt Formats**: Tried various complex prompt structures
- "Classify this text as ifeval, commoneval, or wildvoice: ..."
- Model struggled with complex instructions
- Generated explanations instead of simple labels
### Breakthrough: Direct Classification Approach
The key breakthrough came with a **direct, simple approach**:
#### 1. **Simplified Prompt Format**
Instead of complex classification prompts, we used a simple format:
```
Text: {input_text}
Label: {expected_label}
```
#### 2. **LoRA (Low-Rank Adaptation)**
- Used PEFT library for efficient fine-tuning
- Only trained 0.88% of parameters
- Much more stable than full fine-tuning
- Faster training and inference
#### 3. **Focused Training Data**
Created clear, distinct examples for each category:
- **ifeval**: Instruction-following with specific formatting requirements
- **commoneval**: Factual questions requiring direct answers
- **wildvoice**: Conversational, informal language patterns
#### 4. **Optimal Hyperparameters**
- **Learning Rate**: 5e-4 (higher than initial attempts)
- **Batch Size**: 2 (smaller for stability)
- **Max Length**: 128 (shorter sequences)
- **Training Steps**: 150
- **LoRA Rank**: 8 (focused learning)
## 🚀 Usage
### Basic Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("manbeast3b/qwen2.5-0.5b-text-classification")
tokenizer = AutoTokenizer.from_pretrained("manbeast3b/qwen2.5-0.5b-text-classification")
def classify_text(text):
prompt = f"Text: {text}\nLabel:"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
generated = model.generate(
**inputs,
max_new_tokens=15,
do_sample=True,
temperature=0.1,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(generated[0], skip_special_tokens=True)
return response[len(prompt):].strip()
# Test examples
print(classify_text("Follow these instructions exactly: Write 3 sentences about cats."))
# Output: ifeval
print(classify_text("What is the capital of France?"))
# Output: commoneval
print(classify_text("Hey, how are you doing today?"))
# Output: wildvoice
```
### Advanced Usage with Confidence Scoring
```python
def classify_with_confidence(text, num_samples=5):
predictions = []
for _ in range(num_samples):
prompt = f"Text: {text}\nLabel:"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
generated = model.generate(
**inputs,
max_new_tokens=15,
do_sample=True,
temperature=0.3, # Slightly higher for diversity
top_p=0.9,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(generated[0], skip_special_tokens=True)
prediction = response[len(prompt):].strip().lower()
# Clean up prediction
if 'ifeval' in prediction:
prediction = 'ifeval'
elif 'commoneval' in prediction:
prediction = 'commoneval'
elif 'wildvoice' in prediction:
prediction = 'wildvoice'
else:
prediction = 'unknown'
predictions.append(prediction)
# Calculate confidence
from collections import Counter
counts = Counter(predictions)
most_common = counts.most_common(1)[0]
confidence = most_common[1] / len(predictions)
return most_common[0], confidence
# Example with confidence
label, confidence = classify_with_confidence("Please follow these steps: 1) Read 2) Think 3) Write")
print(f"Prediction: {label}, Confidence: {confidence:.2%}")
```
## 📈 Training Details
### Model Architecture
- **Base Model**: Qwen/Qwen2.5-0.5B-Instruct
- **Parameters**: 498,431,872 total, 4,399,104 trainable (0.88%)
- **Precision**: FP16 (mixed precision)
- **Device**: CUDA (GPU accelerated)
### Training Configuration
```python
# LoRA Configuration
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=8, # Rank
lora_alpha=16, # LoRA alpha
lora_dropout=0.1,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
)
# Training Arguments
training_args = TrainingArguments(
learning_rate=5e-4,
per_device_train_batch_size=2,
max_steps=150,
max_length=128,
fp16=True,
gradient_accumulation_steps=1,
warmup_steps=20,
weight_decay=0.01,
max_grad_norm=1.0
)
```
### Dataset
The model was trained on synthetic data representing three text categories:
- **60 total samples** (20 per category)
- **ifeval**: Instruction-following tasks with specific formatting requirements
- **commoneval**: Factual questions and knowledge-based queries
- **wildvoice**: Conversational, informal language patterns
## 🔍 Error Analysis
### Failed Predictions (2 out of 30)
1. **"What is 2 plus 2?"** → Predicted: `unknown` (Expected: `commoneval`)
- Model generated: `#eval{1} Label: #eval{2} Label: #`
- Issue: Model generated code-like syntax instead of simple label
2. **"What is the opposite of hot?"** → Predicted: `wildvoice` (Expected: `commoneval`)
- Model generated: `#wildvoice:comoneval:hot:yourresponse:whatis`
- Issue: Model generated complex response instead of simple label
### Success Factors
- **Simple prompt format** was crucial for success
- **LoRA fine-tuning** provided stable training
- **Focused training data** with clear category distinctions
- **Appropriate hyperparameters** (learning rate, batch size, etc.)
## 🛠️ Technical Implementation
### Files Structure
```
merged_classification_model/
├── README.md # This file
├── config.json # Model configuration
├── generation_config.json # Generation settings
├── model.safetensors # Model weights (988MB)
├── tokenizer.json # Tokenizer vocabulary
├── tokenizer_config.json # Tokenizer configuration
├── special_tokens_map.json # Special tokens mapping
├── added_tokens.json # Added tokens
├── merges.txt # BPE merges
├── vocab.json # Vocabulary
└── chat_template.jinja # Chat template
```
### Dependencies
```bash
pip install transformers>=4.56.0
pip install torch>=2.0.0
pip install peft>=0.17.0
pip install accelerate>=0.21.0
```
## 🎯 Use Cases
This model is particularly useful for:
- **Text categorization** in educational platforms
- **Content filtering** based on text type
- **Dataset preprocessing** for machine learning pipelines
- **VoiceBench-style evaluation** systems
- **Instruction following detection** in AI systems
- **Conversational vs. factual text separation**
## ⚠️ Limitations
1. **Synthetic Training Data**: Model was trained on synthetic data and may not generalize perfectly to all real-world text
2. **Three-Category Limitation**: Only classifies into the three predefined categories
3. **Prompt Sensitivity**: Performance may vary with different prompt formats
4. **Edge Cases**: Some edge cases (like mathematical questions) may be misclassified
5. **Language**: Primarily trained on English text
## 🔮 Future Improvements
1. **Larger Training Dataset**: Use real VoiceBench data with proper audio transcription
2. **More Categories**: Expand to include additional text types
3. **Multilingual Support**: Train on multiple languages
4. **Confidence Calibration**: Improve confidence scoring
5. **Few-shot Learning**: Add support for few-shot classification
## 📚 Citation
```bibtex
@misc{qwen2.5-0.5b-text-classification,
title={Qwen2.5-0.5B Text Classification Model for VoiceBench-style Evaluation},
author={Your Name},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/manbeast3b/qwen2.5-0.5b-text-classification}},
note={Fine-tuned using LoRA on synthetic text classification data}
}
```
## 🤝 Contributing
Contributions are welcome! Please feel free to:
- Report issues with the model
- Suggest improvements
- Submit pull requests
- Share your use cases
## 📄 License
This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for more details.
---
**Model Performance Summary:**
- ✅ **93.33% Overall Accuracy**
- ✅ **100% ifeval accuracy** (instruction-following)
- ✅ **100% wildvoice accuracy** (conversational)
- ✅ **80% commoneval accuracy** (factual questions)
- ✅ **Efficient LoRA fine-tuning** (0.88% trainable parameters)
- ✅ **Fast inference** with small model size
- ✅ **Easy to use** with simple API
*This model represents a successful application of LoRA fine-tuning for text classification, achieving high accuracy with minimal computational resources.*
|
robworks-software/california-k12-comprehensive
|
robworks-software
| 2025-09-17T23:09:48Z | 0 | 0 | null |
[
"education",
"k12",
"standards",
"california",
"curriculum",
"assessment",
"common-core",
"ngss",
"eld",
"cte",
"ethnic-studies",
"educational-technology",
"learning-objectives",
"en",
"license:cc0-1.0",
"region:us"
] | null | 2025-09-17T22:54:52Z |
---
license: cc0-1.0
task_categories:
- text-classification
- question-answering
- text-generation
- text2text-generation
tags:
- education
- k12
- standards
- california
- curriculum
- assessment
- common-core
- ngss
- eld
- cte
- ethnic-studies
- educational-technology
- learning-objectives
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
pretty_name: California K-12 Comprehensive Educational Standards
dataset_info:
config_name: default
features:
- name: id
dtype: string
- name: grade_level
dtype: string
- name: subject_area
dtype: string
- name: standard_text
dtype: string
- name: learning_objectives
sequence: string
- name: cognitive_level
dtype: string
- name: real_world_applications
sequence: string
splits:
- name: train
num_bytes: 4831240
num_examples: 3410
download_size: 4831240
dataset_size: 4831240
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# California K-12 Comprehensive Educational Standards Dataset
**The most complete and authoritative California K-12 educational standards dataset available**
[](http://creativecommons.org/publicdomain/zero/1.0/)
[]()
[]()
[]()
## Dataset Overview
This dataset contains **3,410 comprehensive educational standards** covering all California K-12 frameworks, making it the most complete collection of California educational standards available for AI/ML applications, educational technology development, and curriculum alignment.
### Key Statistics
- **Total Standards**: 3,410
- **Grade Levels**: TK-12 (Transitional Kindergarten through Grade 12)
- **Frameworks**: 14 complete educational frameworks
- **Version**: 7.0.0 (December 2024)
- **Legal Compliance**: 100% current with 2021-2024 California legislation
- **File Format**: JSON with HuggingFace compatibility
## Framework Coverage
| Framework | Standards | Coverage | Key Features |
|-----------|-----------|----------|--------------|
| **English Language Arts** | 563 | 99% Complete | All CCSS domains (RL, RI, RF, W, SL, L, RH, RST, WHST) |
| **Mathematics** | 244 | 100% Complete | 2023 Big Ideas Framework (July adoption) |
| **Science (NGSS)** | 100 | 100% Complete | 3-dimensional learning with Performance Expectations |
| **English Language Development** | 756 | 100% Complete | All proficiency levels (Emerging/Expanding/Bridging) |
| **History-Social Science** | 312 | 100% Complete | Ethnic Studies integration |
| **Visual & Performing Arts** | 216 | 100% Complete | All 5 disciplines (Dance, Media, Music, Theatre, Visual) |
| **Health Education** | 200 | 100% Complete | Model Content Standards |
| **Environmental Education** | 156 | 100% Complete | Environmental Principles & Concepts (EP&Cs) |
| **Physical Education** | 132 | 100% Complete | Model Content Standards |
| **Career Technical Education** | 240 | 100% Complete | All 15 industry sectors with pathways |
| **World Languages** | 234 | 100% Complete | 2020 Framework update |
| **Ethnic Studies** | 64 | 100% Complete | AB 101 graduation requirement |
| **Computer Science** | 64 | 100% Complete | K-12 computational thinking progression |
| **School Library** | 48 | 100% Complete | Information literacy standards |
## Legal & Policy Compliance
### Recent Legislative Requirements (2021-2024)
- ✅ **Assembly Bill 101 (2021)** - Ethnic Studies graduation requirement
- ✅ **Senate Bill 1054 (2023)** - Financial literacy integration
- ✅ **Mathematics Framework 2023** - Big Ideas organization
- ✅ **Universal Transitional Kindergarten** - 2025-26 implementation
- ✅ **Community Schools Partnership** - $4.1B investment
### Framework Currency
All frameworks reflect the most current California State Board of Education adoptions:
- Mathematics Framework (July 2023)
- Arts Framework (July 2020)
- World Languages Framework (July 2020)
- ELA/ELD Framework (2014, current)
- NGSS (2013, with CA additions)
## Dataset Structure
### JSON Schema
```json
{
"id": "CCSS.ELA-LITERACY.RL.K.1",
"grade_level": "K",
"subject_area": "English Language Arts",
"domain": "Reading Literature",
"standard_text": "Ask and answer questions about key details in a text",
"learning_objectives": [...],
"cognitive_level": "Remember",
"common_core_aligned": true,
"real_world_applications": [...]
}
```
### Standard Fields
Each standard includes:
- **Unique ID**: Official California/CCSS standard codes
- **Grade Level**: TK, K, 1-12, or grade bands
- **Subject Area**: Primary academic or CTE area
- **Standard Text**: Complete official standard text
- **Learning Objectives**: Specific measurable outcomes
- **Cognitive Level**: Bloom's Taxonomy classification
- **Real-World Applications**: Career and life connections
## Getting Started
### Installation
```python
from datasets import load_dataset
# Load the complete dataset
dataset = load_dataset("robworks-software/california-k12-comprehensive")
# Access standards data
standards = dataset['train']
# Filter by subject area
ela_standards = dataset['train'].filter(lambda x: x['subject_area'] == 'English Language Arts')
# Filter by grade level
grade_3_standards = dataset['train'].filter(lambda x: x['grade_level'] == '3')
```
### Quick Examples
#### Find Common Core ELA Standards
```python
# Load dataset
dataset = load_dataset("robworks-software/california-k12-comprehensive")
# Filter for Common Core ELA standards
ccss_ela = dataset['train'].filter(
lambda x: x.get('common_core_aligned', False) and
x['subject_area'] == 'English Language Arts'
)
print(f"Found {len(ccss_ela)} Common Core ELA standards")
```
#### Get NGSS Performance Expectations
```python
ngss_standards = dataset['train'].filter(lambda x: x.get('ngss_aligned', False))
print(f"Found {len(ngss_standards)} NGSS Performance Expectations")
```
#### Filter by Cognitive Level
```python
high_level_standards = dataset['train'].filter(
lambda x: x['cognitive_level'] in ['Analyze', 'Evaluate', 'Create']
)
print(f"Found {len(high_level_standards)} higher-order thinking standards")
```
## Use Cases
### Educational Technology
- **Curriculum Alignment**: Map learning content to official standards
- **Assessment Development**: Create standards-aligned assessments
- **Learning Management Systems**: Tag content with standard codes
- **AI Tutoring Systems**: Provide standards-based learning paths
### Research & Analytics
- **Standards Gap Analysis**: Identify curriculum coverage gaps
- **Learning Progression Research**: Study skill development across grades
- **Educational Data Mining**: Analyze standards-based learning outcomes
- **Policy Impact Studies**: Research standards implementation effects
### Curriculum Development
- **Lesson Planning**: Align activities to specific standards
- **Scope & Sequence**: Map standards across academic years
- **Cross-Curricular Connections**: Identify interdisciplinary links
- **Differentiated Instruction**: Adapt for diverse learners
## Data Quality & Validation
### Authoritative Sources
All standards derived from official California Department of Education sources:
- California State Board of Education adoptions
- Common Core State Standards Initiative
- Next Generation Science Standards
- California ELD Standards
- CTE Model Curriculum Standards
### Quality Assurance
- **100% Cross-Verification**: Against official state databases
- **Legal Compliance**: Current with all 2021-2024 legislation
- **Framework Currency**: Most recent state adoptions
- **Professional Review**: Educational expert validation
## Citation
If you use this dataset in your research or applications, please cite:
```bibtex
@dataset{california_k12_standards_2024,
title={California K-12 Comprehensive Educational Standards Dataset},
author={Robson, Ryan and Robworks Software},
year={2024},
version={7.0.0},
url={https://huggingface.co/datasets/robworks-software/california-k12-comprehensive},
note={Complete collection of California K-12 educational standards with 2021-2024 legislative compliance}
}
```
## License
This dataset is released under [CC0 1.0 Universal (Public Domain)](http://creativecommons.org/publicdomain/zero/1.0/).
The underlying educational standards are public domain as official government documents. This compilation and structuring is contributed to the public domain to support educational technology development and research.
## Support & Contact
- **Developer**: Robworks Software
- **Contact**: [email protected]
- **Website**: [robworks.info](https://robworks.info)
- **Issues**: Report data issues or requests via GitHub
## Acknowledgments
This dataset aggregates and structures content from:
- California Department of Education
- California State Board of Education
- Common Core State Standards Initiative
- Next Generation Science Standards (NGSS)
- National Career Technical Education organizations
- California Community Colleges Chancellor's Office
Special thanks to California educators and policymakers who develop and maintain these comprehensive educational standards.
---
**Dataset Status**: ✅ Complete and Current
**Last Updated**: December 17, 2024
**Next Review**: Quarterly (March 2025)
|
Sephdude/en-esPR
|
Sephdude
| 2025-09-17T23:00:53Z | 0 | 0 | null |
[
"safetensors",
"marian",
"arxiv:2006.01669",
"region:us"
] | null | 2025-09-17T21:05:45Z |
---
en-esPR:
references:
- "Tiedemann, J., & Thottingal, S. (2020). OPUS-MT — Building open translation services for the World: https://arxiv.org/abs/2006.01669. Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT), 479–480."
- "Ortiz Suárez, P. J., Sagot, B., & Romary, L. (2019). Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures: https://aclanthology.org/W19-610"
license: "cc-by-nc-sa-4.0"
model_card:
description: "Fine-tuned version of https://huggingface.co/Helsinki-NLP/opus-mt-es-en for English to Puerto Rican stylized Spanish."
model_details:
model_description: "Provide a longer summary of what this model is."
developed_by: "Sephdude"
model_type: "translation"
finetuned_from_model: "Helsinki-NLP/opus-mt-es-en"
model_sources:
repository: "https://github.com/Sephdude/refined-translate/tree/main"
paper: "More Information Needed"
demo: "https://sephdude.github.io/refined-translate"
bias_risks_limitations: "The data for fine-tuning was gathered from the mOSCAR corpus for general Spanish text using key word filtering. This means that some of the dialectical filtering may be innaccurate."
recommendations: "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: "Visit https://huggingface.co/spaces/Sephdude/refined-translate to try the model out."
training_data: "https://huggingface.co/datasets/Sephdude/esPR_en"
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758149683
|
devivodowdlel
| 2025-09-17T22:57:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T22:55:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/adv_inception_v3_Opset17
|
onnxmodelzoo
| 2025-09-17T22:54:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T22:53:07Z |
---
language: en
license: apache-2.0
model_name: adv_inception_v3_Opset17.onnx
tags:
- Computer_Vision
---
|
aamijar/ReplaceME-Llama-2-5B-lora-r8-rte-epochs1
|
aamijar
| 2025-09-17T22:53:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T22:53:43Z |
---
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]
|
CompassioninMachineLearning/Basellama_plus1knegaiJazz
|
CompassioninMachineLearning
| 2025-09-17T22:47:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.1-8B",
"base_model:finetune:unsloth/Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T22:40:59Z |
---
base_model: unsloth/Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** CompassioninMachineLearning
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.1-8B
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)
|
ShayanDarabi/Reinforce-PixelCopter
|
ShayanDarabi
| 2025-09-17T22:42:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-04T19:28:05Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 35.80 +/- 24.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758148450
|
devivodowdlel
| 2025-09-17T22:37:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T22:35:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Samanehmoghaddam/AbuseBERT
|
Samanehmoghaddam
| 2025-09-17T22:36:36Z | 0 | 0 | null |
[
"pytorch",
"roberta",
"text-classification",
"abusive-language",
"hate-speech",
"toxicity",
"cyberviolence",
"abusive-language-detection",
"BERT",
"en",
"license:mit",
"region:us"
] |
text-classification
| 2025-09-17T14:54:58Z |
---
language: en
tags:
- text-classification
- abusive-language
- hate-speech
- toxicity
- cyberviolence
- abusive-language-detection
- BERT
license: mit
---
# AbuseBERT
## Model Description
**AbuseBERT** is a **BERT-based classification model** fine-tuned for **abusive language detection**, optimized for **cross-dataset generalization**.
> Abusive language detection models often suffer from poor generalization due to **sampling and lexical biases** in individual datasets. Our approach addresses this by integrating **publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.
**Key Findings using 10 datasets:**
- Individual dataset models: average F1 = **0.60**
- Integrated model: F1 = **0.84**
- Dataset contribution to performance improvements correlates with **lexical diversity (0.71 correlation)**
- Integration exposes models to diverse abuse patterns, enhancing **real-world generalization**
---
## Conclusion / Takeaways
- No single dataset captures the full spectrum of abusive language; each dataset reflects a **limited slice** of the problem space.
- Systematically integrating ten heterogeneous datasets significantly improves classification performance on a **held-out benchmark**.
- Lexically dissimilar datasets contribute more to **enhancing generalization**.
- The integrated model demonstrates superior **cross-dataset performance** compared to models trained on individual datasets.
---
## Paper Reference
Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goel, Kaitlyn Regehr,
“**Enhancing machine learning in abusive language detection with dataset aggregation**,” in *Proc. 35th IEEE Int. Conf. Collaborative Advances in Software Computing (CASC)*, 2025.
---
## Intended Use
**Recommended:**
- Detecting abusive, offensive, or toxic language in text from social media, online forums, or messaging platforms.
- Supporting research on online harassment, cyber violence, and hate speech analysis.
- Assisting human moderators in content review or flagging potentially harmful content.
- Evaluating trends, prevalence, or patterns of abusive language in large-scale textual datasets.
**Not Recommended:**
- Fully automated moderation without human oversight
- High-stakes legal or policy decisions
---
## Usage Example
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
# Load the model
model_name = "Samanehmoghaddam/AbuseBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Create a pipeline for text classification
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Example texts to classify
texts = [
"@user You are amazing!",
"@user You are stupid!",
]
# Run the classifier
results = classifier(texts)
# Print results
for text, result in zip(texts, results):
print(f"Text: {text}")
print(f"Prediction: {result}")
print("-" * 40)
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758146603
|
devivodowdlel
| 2025-09-17T22:04:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T22:04:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helena-balabin/pretrained_graphormer_vg_action_image_graphs
|
helena-balabin
| 2025-09-17T21:58:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"graphormer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T21:07:17Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: pretrained_graphormer_vg_action_image_graphs
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. -->
# pretrained_graphormer_vg_action_image_graphs
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.45.2
- Pytorch 2.7.0+cu126
- Datasets 3.0.2
- Tokenizers 0.20.1
|
smcleod/Magistral-Small-2509-mlx-6Bit
|
smcleod
| 2025-09-17T21:57:18Z | 0 | 1 |
vllm
|
[
"vllm",
"safetensors",
"mistral3",
"mistral-common",
"mlx",
"mlx-my-repo",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:unsloth/Magistral-Small-2509",
"base_model:quantized:unsloth/Magistral-Small-2509",
"license:apache-2.0",
"6-bit",
"region:us"
] | null | 2025-09-17T21:55:50Z |
---
base_model: unsloth/Magistral-Small-2509
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- vllm
- mistral-common
- mlx
- mlx-my-repo
---
# smcleod/Magistral-Small-2509-mlx-6Bit
The Model [smcleod/Magistral-Small-2509-mlx-6Bit](https://huggingface.co/smcleod/Magistral-Small-2509-mlx-6Bit) was converted to MLX format from [unsloth/Magistral-Small-2509](https://huggingface.co/unsloth/Magistral-Small-2509) using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("smcleod/Magistral-Small-2509-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758145369
|
devivodowdlel
| 2025-09-17T21:43:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T21:43:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AmirMohseni/grpo-qwen2.5-vl-3b
|
AmirMohseni
| 2025-09-17T21:37:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T20:34:09Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: grpo-qwen2.5-vl-3b
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for grpo-qwen2.5-vl-3b
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AmirMohseni/grpo-qwen2.5-vl-3b", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/amirmohseni-maastricht-university/grpo-vlm-training/runs/kldz9f7j)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
HectorHe/DeepSeek-V2-Lite-aux-free-sft-math7k-1epoch-bs4
|
HectorHe
| 2025-09-17T21:35:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"custom_code",
"dataset:HectorHe/math7k",
"base_model:deepseek-ai/DeepSeek-V2-Lite",
"base_model:finetune:deepseek-ai/DeepSeek-V2-Lite",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T20:22:09Z |
---
base_model: deepseek-ai/DeepSeek-V2-Lite
datasets: HectorHe/math7k
library_name: transformers
model_name: DeepSeek-V2-Lite-aux-free-sft-math7k-1epoch-bs4
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for DeepSeek-V2-Lite-aux-free-sft-math7k-1epoch-bs4
This model is a fine-tuned version of [deepseek-ai/DeepSeek-V2-Lite](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite) on the [HectorHe/math7k](https://huggingface.co/datasets/HectorHe/math7k) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="HectorHe/DeepSeek-V2-Lite-aux-free-sft-math7k-1epoch-bs4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hector_-carnegie-mellon-university/huggingface/runs/yvewbh5a)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.0
- Pytorch: 2.6.0
- Datasets: 4.1.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
UnifiedHorusRA/9w4c3hm6
|
UnifiedHorusRA
| 2025-09-17T21:35:29Z | 0 | 0 | null |
[
"art",
"region:us"
] | null | 2025-09-17T21:31:00Z |
---
tags:
- art
---
Past [EternalSpace](https://civitai.com/user/EternalSpace) flux.1 Dev loras [archive](https://civitaiarchive.com/users/EternalSpace?is_nsfw=true&is_deleted=true)
|
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