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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 18:52:31
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11.7k
| library_name
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|---|---|---|---|---|---|---|---|---|---|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756813452
|
vwzyrraz7l
| 2025-09-02T12:09:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:09:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ReactiveAI/RxT-Alpha-Mini-S-Decoder-SMAT
|
ReactiveAI
| 2025-09-02T12:08:41Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"text-generation",
"license:apache-2.0",
"region:eu"
] |
text-generation
| 2025-09-02T11:06:49Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
skyai798/STAR-1_DeepSeek-R1-Distill-Llama-8B_sft-complete-dpo
|
skyai798
| 2025-09-02T12:08:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T08:21:41Z |
---
library_name: transformers
license: other
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: STAR-1_DeepSeek-R1-Distill-Llama-8B_sft-complete_dpo
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. -->
# STAR-1_DeepSeek-R1-Distill-Llama-8B_sft-complete_dpo
This model is a fine-tuned version of [/models/saves/dingyue/STAR-1_DeepSeek-R1-Distill-Llama-8B_sft-complete](https://huggingface.co//models/saves/dingyue/STAR-1_DeepSeek-R1-Distill-Llama-8B_sft-complete) on the STAR-1_DeepSeek-R1-Distill-Llama-8B_dpo dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.3
|
phospho-app/ACT_BBOX-test1_dataset-qwjchdwnyv
|
phospho-app
| 2025-09-02T12:07:34Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"safetensors",
"act",
"robotics",
"dataset:phospho-app/test1_dataset_bboxes",
"region:us"
] |
robotics
| 2025-09-02T11:46:01Z |
---
datasets: phospho-app/test1_dataset_bboxes
library_name: phosphobot
pipeline_tag: robotics
model_name: act
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act model - 🧪 phosphobot training pipeline
- **Dataset**: [phospho-app/test1_dataset_bboxes](https://huggingface.co/datasets/phospho-app/test1_dataset_bboxes)
- **Wandb run id**: None
## This model was trained using **[🧪phospho](https://phospho.ai)**
Training was successful, try it out on your robot!
## Training parameters
```text
{
"batch_size": 100,
"steps": 10000,
"save_freq": 5000,
"target_detection_instruction": "move the yellow box on the white space, move pink box on the brown space",
"image_key": "main",
"image_keys_to_keep": []
}
```
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756814777
|
omerbkts
| 2025-09-02T12:06:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:06:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
severussnape21/mms-tts-urd-common-voice
|
severussnape21
| 2025-09-02T12:06:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T12:06:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756814634
|
akirafudo
| 2025-09-02T12:04:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:04:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_ur_hi_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T12:04:06Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T12:04:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Reihaneh/wav2vec2_fi_et_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T12:04:02Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T12:04:01Z |
---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756814540
|
liukevin666
| 2025-09-02T12:03:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:03:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shaleen123/ThoughtSwitch-V1-1.7b-Instruct
|
Shaleen123
| 2025-09-02T12:03:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T12:02:25Z |
---
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]
|
coastalcph/Qwen2.5-7B-plus-14t_diff_pv_evil
|
coastalcph
| 2025-09-02T12:01:52Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-02T11:58:28Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-7B-Instruct")
t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-pv-prompts-evil")
t_combined = 1.0 * t_1 + 14.0 * t_2 - 14.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 1: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-pv-prompts-evil
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model1": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model2": "coastalcph/Qwen2.5-7B-pv-prompts-evil",
"finetuned_model3": "coastalcph/Qwen2.5-7B-pv-prompts-non-evil",
"output_model_name": "coastalcph/Qwen2.5-7B-plus-14t_diff_pv_evil",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 14.0,
"scale_t3": 14.0
}
|
mradermacher/Qwen3-18B-QiMing-V1.0-Silence-Of-The-Qwen-i1-GGUF
|
mradermacher
| 2025-09-02T12:01:47Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-02T11:45:28Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-18B-QiMing-V1.0-Silence-Of-The-Qwen
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756814444
|
Dejiat
| 2025-09-02T12:01:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:01:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1756814257
|
cwayneconnor
| 2025-09-02T12:01:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:00:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bumppd/digitalmarketingcompany
|
bumppd
| 2025-09-02T12:00:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T11:38:53Z |
<h1>How to Choose the Best Digital Marketing Company in India for Your Business Growth.</h1>
In today’s fast-paced digital-first world, businesses cannot rely on traditional methods alone to reach their audience. From social media and search engines to emails and websites, every platform plays a crucial role in building visibility and driving conversions. But with endless strategies and tools available, the real challenge lies in execution. That’s where partnering with the <a href="https://bumppd.com">best digital marketing company in India</a> can transform the game for your brand.
Whether you are a startup looking for visibility or a small business aiming to compete with bigger players, a reliable digital marketing company can help you scale faster, smarter, and more sustainably. In this blog, we will discuss why hiring a professional agency matters, how to choose the right one, and why Bumppd could be your partner in growth.
<h2>Why Your Business Needs a Digital Marketing Company</h2>
<h4>Expertise Across Channels</h4>
Digital marketing involves multiple touchpoints: SEO, social media, content, paid ads, and more. A full-service digital marketing company offers specialized teams for each channel, ensuring your campaigns are well-rounded and data-driven.
<h4>Scalability</h4>
As your business grows, your marketing needs evolve. Agencies can easily scale strategies, add new services, or shift focus depending on your goals.
<h4>Cost-Effective for Small Businesses</h4>
Instead of hiring a full in-house team, outsourcing to professionals saves both money and time. That’s why digital marketing services for small business have become a popular solution across industries.
<h4>Access to Tools and Data</h4>
Top agencies invest in premium tools for analytics, keyword tracking, automation, and customer insights—resources that small businesses often cannot afford individually.
<center><img src="https://technofaq.org/wp-content/uploads/2022/03/digital-marketing-agency.jpg" style="width: 500px; height: 300px;"></center>
<h2>How to Choose the Best Digital Marketing Company in India</h2>
With hundreds of agencies claiming expertise, selecting the right partner can be overwhelming. Here are some factors to consider before making a decision:
<h4>1. Industry Experience</h4>
A company with experience across different industries understands how consumer behavior changes in various markets. They can adapt strategies to suit your niche.
<h4>2. Proven Track Record</h4>
Look for case studies, client testimonials, and measurable results. The best digital marketing company in India will always showcase real examples of how they helped businesses grow.
<h4>3. Service Portfolio</h4>
From SEO and paid campaigns to branding and social media, ensure the agency offers a comprehensive set of digital marketing services. This saves you from working with multiple vendors.
<h4>4. Transparency and Reporting</h4>
Regular updates and detailed performance reports should be non-negotiable. A reliable agency ensures you always know where your money is being spent and what results it is driving.
<h4>5. Customized Strategies</h4>
Every business is unique. The agency should take time to understand your goals, challenges, and audience before suggesting a plan. Cookie-cutter strategies rarely work.
<h2>Digital Marketing Services for Small Business</h2>
Small businesses face specific challenges: limited budgets, fewer resources, and the need for quick yet sustainable growth. That’s why <a href="https://bumppd.com/">digital marketing services for small business</a> are tailored differently than for large corporations. Some of the most impactful services include:
<ul>
<li>Search Engine Optimization (SEO) – Ensuring your business ranks higher on Google searches to drive organic traffic.</li>
<li>Local SEO – Optimizing for “near me” searches so customers in your area can find you easily.</li>
<li>Social Media Marketing – Building brand awareness, trust, and engagement on platforms like Instagram, Facebook, and LinkedIn.</li>
<li>Pay-Per-Click (PPC) Ads – Driving immediate results with budget-controlled campaigns on Google or social media.</li>
<li>Content Marketing – Creating blogs, guides, and videos that educate and convert customers.</li>
</ul>
For small businesses, these services are designed to maximize ROI, ensuring that every marketing dollar is invested wisely.
<center><img src="https://www.shutterstock.com/shutterstock/videos/3559823779/thumb/12.jpg?ip=x480" style="width: 500px; height: 300px;"></center>
<h2>Why Bumppd Could Be Your Ideal Partner</h2>
At Bumppd, we believe growth is not accidental—it’s engineered. Unlike one-size-fits-all agencies, we specialize in creating personalized strategies that align with your business goals.
<b>Here’s why businesses choose us as their trusted partner:</b>
<ul>
<li>Tailored Solutions – Whether you’re a startup or an established brand, our approach is customized to your needs.</li>
<li>Result-Driven Campaigns – We focus on measurable outcomes: leads, sales, engagement, and brand visibility.</li>
<li>Creative Meets Data – Our team combines storytelling with analytics to deliver campaigns that not only look good but also perform.</li>
<li>Trusted by Brands – We’ve worked with diverse businesses, helping them accelerate growth through smart digital strategies.</li>
</ul>
This commitment makes Bumppd one of the best digital marketing company in India for businesses that want meaningful results.
<h2>Future of Digital Marketing in India</h2>
The Indian market is rapidly adopting digital solutions. From small towns to big metros, customers are searching online before making purchase decisions. This makes digital presence non-negotiable.
<b>Some trends shaping the future include:</b>
<ul>
<li>AI-driven campaigns for better targeting.</li>
<li>Voice search optimization as smart assistants become mainstream.</li>
<li>Short-form content (Reels, Shorts) dominating social platforms.</li>
<li>Hyper-local marketing for businesses targeting regional audiences.</li>
</ul>
By partnering with the best digital marketing company in India, you can stay ahead of these trends while focusing on your core business.
<h2>Conclusion</h2>
Digital marketing is no longer optional—it’s essential for survival and growth. Whether you’re looking to increase brand awareness, generate leads, or drive sales, the right partner can make all the difference.
If you’re a small business searching for affordable yet effective strategies, or a growing company looking to scale, finding the best digital marketing company in India should be your top priority. With expertise, innovation, and a results-driven approach, agencies like Bumppd are here to help businesses thrive in the digital age.
|
vendi11/blockassist-bc-placid_placid_llama_1756814364
|
vendi11
| 2025-09-02T12:00:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:00:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1756814285
|
0xaoyama
| 2025-09-02T11:58:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:58:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756814159
|
xinnn32
| 2025-09-02T11:57:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:57:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756814168
|
akirafudo
| 2025-09-02T11:56:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:56:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sprt98/GPT-SoVITS_Lady_Sun
|
Sprt98
| 2025-09-02T11:56:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-09-02T09:47:07Z |
---
license: mit
---
王者荣耀 孙尚香 GPT-SoVITS模型
该模型为V2ProPlus版本
模型作者：幽花夜雪（https://space.bilibili.com/3493264718039598）
声音归属：陶典（https://space.bilibili.com/10431355）
|
Aman6u5/dddd
|
Aman6u5
| 2025-09-02T11:55:14Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-06T11:47:02Z |
---
license: apache-2.0
---
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756814001
|
omerbektass
| 2025-09-02T11:53:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:53:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756813897
|
kittygirlhere
| 2025-09-02T11:52:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:52:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LeFeujitif/sandbox
|
LeFeujitif
| 2025-09-02T11:51:36Z | 2,461 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Liberata/illustrious-xl-v1.0",
"base_model:adapter:Liberata/illustrious-xl-v1.0",
"region:us"
] |
text-to-image
| 2025-05-19T10:12:23Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/c61b725b-4cfd-46e9-839b-013c93ccfb15.png
base_model: Liberata/illustrious-xl-v1.0
instance_prompt: null
---
# Mixing
<Gallery />
## Model description
Just a mix of lora
## Download model
Weights for this model are available in Safetensors format.
[Download](/LeFeujitif/kgns/tree/main) them in the Files & versions tab.
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756813855
|
omerbkts
| 2025-09-02T11:51:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:51:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wealthcoders/gpt-oss-20b-new
|
wealthcoders
| 2025-09-02T11:51:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"arxiv:2508.10925",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-02T11:37:15Z |
---
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},
}
```
|
mustafasneu38/sugartrq-tokenizer-tr
|
mustafasneu38
| 2025-09-02T11:50:24Z | 0 | 0 | null |
[
"tokenizer",
"turkish",
"bpe",
"qwen",
"mistral",
"education",
"fine-tuning",
"sugartrq",
"instruction-tuning",
"oscar",
"tr",
"license:apache-2.0",
"region:us"
] | null | 2025-09-02T11:28:41Z |
---
license: apache-2.0
tags:
- tokenizer
- turkish
- bpe
- qwen
- mistral
- education
- fine-tuning
- sugartrq
- instruction-tuning
- oscar
language:
- tr
---
# 🍬 SugarTrQ-Token: Süper Hızlı, Öğrenci Dostu, Türkçe-Özel Tokenizer
SugarTrQ-Token, **Türkçe eğitim verileri**, **MEB ders kitapları**, **LGS/TYT soruları**, **Hugging Face Türkçe veri setleri**, **öğrenci hata modelleri** ve **büyük ölçekli web korpusları** ile eğitilmiş, **yüksek performanslı, morfolojik olarak duyarlı, hata toleranslı** bir tokenizerdir.
Qwen3, Mistral, Llama3, DeepSeek gibi büyük modellere kolayca entegre edilebilir ve özellikle **öğrenci odaklı AI eylemciler** için optimize edilmiştir.
> 🚀 **Hedef:**
> "Türkçe büyük dil modelleri için yeni bir altyapı standardı oluşturmak."
---
## 🚀 Özellikler
| Özellik | Açıklama |
|--------|--------|
| 🌐 **Yalnızca Türkçe'ye Özel** | Genel modellerin aksine, sadece Türkçe için optimize edildi |
| 🧩 **Morfolojik Duyarlılık** | `trmor` ile kök-ek analizi ile eğitildi, uzayıcı kelimeler anlamlı şekilde bölünür |
| 🛠️ **Hata Toleransı** | `"denglem"` → `["denk", "lem"]` gibi yaygın öğrenci hatalarını anlar |
| 🔢 **Yüksek Performans** | Qwen3'te %35 daha az token, %38 daha hızlı inference |
| 📚 **Eğitim Odaklı** | MEB, Maarif, LGS, sunumlar, ders notları, soru bankaları |
| 🔗 **Çoklu Model Uyumu** | Qwen3, Mistral, Llama3, DeepSeek'e entegre edilebilir |
| 🧪 **Pedagojik Token’lar** | `[HATA]`, `[TAVSİYE]`, `[STİL:GÖRSEL]`, `[KONU:MATEMATİK]` gibi özel token’lar |
| 🧠 **Anlam Bütünlüğü** | `GSM8K-TR`, `WikiRAG-TR` ile test edildi, anlam kaybı minimum |
---
## 📊 Performans Karşılaştırması
| Metrik | Orijinal Qwen3 | SugarTrQ-Token | Kazanç |
|-------|----------------|----------------|--------|
| Ort. token/soru (LGS) | 132 | **86** | %35 azalma |
| Inference süresi (A100) | 1.3 sn | **0.8 sn** | %38 hızlanma |
| `"öğrencilerimizdeki"` → token sayısı | 7 | **5** | Anlamlı bölünme |
| Hata tanıma (yazım) | Düşük | **Yüksek** | HT-Token ile |
| BLEU-4 (anlam korunumu) | 0.76 | **0.89** | +17% |
---
## 🧱 Eğitim Verisi Kaynakları
SugarTrQ-Token, aşağıdaki **100+ GB temiz, etik, Türkçe metin** üzerinde eğitildi:
### 📚 Eğitim ve Ders Kitapları
- ✅ Google Drive `__egitim` klasörü (PDF/DOCX/PPT)
- ✅ MEB ders kitapları (6-12. sınıflar)
- ✅ Maarif Vakfı proje tabanlı materyaller
- ✅ LGS, TYT örnek soruları
### 🌐 Büyük Ölçekli Web Korpusları
- ✅ [OSCAR-2201 (tr)](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) – 75 GB temiz Türkçe metin
> _"Open Super-large Crawled Aggregated coRpus, Common Crawl verilerinden türetilmiştir. 151 dilde mevcuttur. Türkçe alt kümesi 6.4 milyar kelime içermektedir."_
- ✅ [CulturaX (tr)](https://huggingface.co/datasets/culturax/culturax) – 50B+ token
- ✅ [Wikipedia (tr)](https://dumps.wikimedia.org/trwiki/) – 20M+ makale
### 🧠 Talimat ve İnce Ayar Verileri
- ✅ [atasoglu/turkish-instruction-datasets](https://huggingface.co/collections/atasoglu/turkish-instruction-datasets-6601d92fa6d901e554d98979)
- ✅ [turkish-nlp-suite/InstrucTurca](https://huggingface.co/datasets/turkish-nlp-suite/InstrucTurca)
- ✅ [merve/turkish_instructions](https://huggingface.co/datasets/merve/turkish_instructions)
- ✅ [Alpaca-TR](https://huggingface.co/datasets/emre97/Alpaca-TR)
- ✅ [OpenOrca-TR](https://huggingface.co/datasets/muratsami/openorca-tr)
- ✅ [GSM8K-TR](https://huggingface.co/datasets/ahmetaa/gsm8k-tr) – Matematiksel akıl yürütme
### 🧩 Çok Dilli ve RAG Odaklı
- ✅ [WikiRAG-TR](https://huggingface.co/datasets/emre97/wikirag-tr) – Bağlamsal anlama
- ✅ [XLSum (TR alt küme)](https://huggingface.co/datasets/castorini/xlsum) – Özetleme
- ✅ [OPUS-100 (en↔tr)](https://huggingface.co/datasets/Helsinki-NLP/opus-100) – Çeviri
- ✅ [ParlaMint-TR](https://huggingface.co/datasets/uhh-lt/parlamint-tr) – Resmi dil
### 💬 Duygu ve Hata Analizi
- ✅ [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset)
- ✅ [WhiteAngelss/Turkce-Duygu-Analizi-Dataset](https://huggingface.co/datasets/WhiteAngelss/Turkce-Duygu-Analizi-Dataset)
- ✅ [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
### 📈 Benchmark ve Değerlendirme
- ✅ [Turkish-MMLU](https://huggingface.co/datasets/alibayram/turkish_mmlu) – Lise seviyesi bilgi testi
- ✅ [Large-Scale Hate Speech Turkish](https://huggingface.co/datasets/hasalp/Large-Scale_Hate_Speech_Turkish) – Etik filtreleme
- ✅ [Bianet (tr-en-ku)](https://huggingface.co/datasets/bianet/bianet) – Çok dilli haberler
---
## 🏗️ İlham Kaynağı: TURKCELL/Turkcell-LLM-7b-v1
SugarTrQ-Token, **[TURKCELL/Turkcell-LLM-7b-v1](https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1)** modelinden ilham alarak geliştirilmiştir.
> _"Bu model, Mistral 7B tabanlı, Türkçe için genişletilmiş bir büyük dil modelidir. 5 milyar token temiz Türkçe veri üzerinde eğitilmiş ve LORA ile ince ayarlanmıştır. Tokenizer'ı özellikle Türkçe'ye uygun hâle getirilmiştir."_
Bu model, **Türkçe’ye özel tokenizer’ın mümkün olduğunu** ve **büyük modellere entegre edilebileceğini** kanıtlamıştır. SugarTrQ-Token, bu ilhamı alarak, **Qwen3, Mistral, Llama3 gibi modellere uyumlu, daha hızlı ve öğrenci dostu bir yapı** sunar.
---
## 🛠️ Kullanım (Hugging Face)
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mustafasneu38/sugartrq-tokenizer-tr")
text = "öğrencilerimizdeki matematik problemini çözemedik çünkü üslü sayılarda hata yaptık"
tokens = tokenizer.tokenize(text)
print(tokens)
# Output: ['öğrenci', 'ler', 'imiz', 'de', 'ki', 'matematik', 'problemini', 'çöze', 'medik', 'çünkü', 'üs', 'lü', 'sayılarda', 'hata', 'yaptık']
|
emogooo/abb-chatbot
|
emogooo
| 2025-09-02T11:50:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T11:05:59Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** emogooo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
arif696/blockassist-bc-regal_spotted_pelican_1756813684
|
arif696
| 2025-09-02T11:49:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:49:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Duckq/unsloth-llama-3.2-1B-full-finetuned
|
Duckq
| 2025-09-02T11:48:45Z | 315 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T04:47:12Z |
---
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]
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756813520
|
Ferdi3425
| 2025-09-02T11:46:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:46:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BarnabePM/cours-ML-Barnabe
|
BarnabePM
| 2025-09-02T11:45:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:39:52Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: cours-ML-Barnabe
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. -->
# cours-ML-Barnabe
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6949
- eval_model_preparation_time: 0.0032
- eval_accuracy: 0.5
- eval_f1: 0.3333
- eval_runtime: 295.1508
- eval_samples_per_second: 1.016
- eval_steps_per_second: 0.064
- step: 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
koloni/blockassist-bc-deadly_graceful_stingray_1756811978
|
koloni
| 2025-09-02T11:45:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:45:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TohanBoss/blockassist-bc-regal_spotted_pelican_1756813364
|
TohanBoss
| 2025-09-02T11:44:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:43:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756813346
|
omerbkts
| 2025-09-02T11:43:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:42:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
the-usan/urdu-crime-event-v1
|
the-usan
| 2025-09-02T11:41:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:41:43Z |
---
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]
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756813194
|
akirafudo
| 2025-09-02T11:40:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:40:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
devtayyabsajjad/my-imdb-sentiment-model
|
devtayyabsajjad
| 2025-09-02T11:39:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:39:25Z |
---
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]
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756811610
|
pempekmangedd
| 2025-09-02T11:38:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:38:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756813049
|
omerbektass
| 2025-09-02T11:37:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:37:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Laluy/blockassist-bc-hardy_cunning_stingray_1756812895
|
Laluy
| 2025-09-02T11:37:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy cunning stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:36:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy cunning stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hung3r/q-FrozenLake-v1-4x4-noSlippery
|
hung3r
| 2025-09-02T11:35:09Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-02T11:34:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="hung3r/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
mradermacher/llama3.1-swallow-hamahiyo-GGUF
|
mradermacher
| 2025-09-02T11:35:04Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:intohay/llama3.1-swallow-hamahiyo",
"base_model:quantized:intohay/llama3.1-swallow-hamahiyo",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T10:20:15Z |
---
base_model: intohay/llama3.1-swallow-hamahiyo
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### 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/intohay/llama3.1-swallow-hamahiyo
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#llama3.1-swallow-hamahiyo-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
j-klawson/ppo-LunarLander-v2
|
j-klawson
| 2025-09-02T11:34:16Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-01T20:44:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 284.97 +/- 14.71
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756812755
|
akirafudo
| 2025-09-02T11:32:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:32:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_ml_ta_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T11:29:11Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:29:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF
|
mradermacher
| 2025-09-02T11:28:21Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b",
"base_model:quantized:KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T06:26:18Z |
---
base_model: KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#FindYourSwordInThisLand-Llama-3.3-72b-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-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/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756812455
|
omerbkts
| 2025-09-02T11:28:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:27:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fhussein/rocky
|
fhussein
| 2025-09-02T11:27:52Z | 0 | 0 | null |
[
"text-classification",
"arxiv:1910.09700",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"region:us"
] |
text-classification
| 2025-09-02T11:24:38Z |
---
base_model:
- openai/gpt-oss-120b
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
mradermacher/Alpha-Model-1b-105B-i1-GGUF
|
mradermacher
| 2025-09-02T11:26:12Z | 22 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-01T12:23:00Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/bruhzair/Alpha-Model-1b-105B
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756812310
|
kittygirlhere
| 2025-09-02T11:25:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:25:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tumini21/blockassist-bc-hairy_howling_butterfly_1756812214
|
tumini21
| 2025-09-02T11:24:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy howling butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:24:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy howling butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vuitton/LouisVuitton_crn_v2.10
|
vuitton
| 2025-09-02T11:24:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:54Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1756810655
|
maxibillion1975
| 2025-09-02T11:23:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:23:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756810543
|
calegpedia
| 2025-09-02T11:23:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:23:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
prithivMLmods/OpenScienceReasoning-Qwen-e10
|
prithivMLmods
| 2025-09-02T11:23:24Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"text-generation-inference",
"medical",
"biology",
"science",
"conversational",
"en",
"zh",
"dataset:nvidia/OpenScienceReasoning-2",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T01:37:52Z |
---
license: apache-2.0
datasets:
- nvidia/OpenScienceReasoning-2
language:
- en
- zh
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- text-generation-inference
- medical
- biology
- science
---
# **OpenScienceReasoning-Qwen-e10**
> OpenScienceReasoning-Qwen-e10 is a high-efficiency, science-focused reasoning model fine-tuned on **Qwen3-1.7B** using the [**nvidia/OpenScienceReasoning-2**](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset. It incorporates **10,000 distinct entries** for scientific reasoning, chain-of-thought exploration, and analytical problem solving.
> The model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for researchers, educators, and developers seeking advanced reasoning under constrained compute.
> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF](https://huggingface.co/prithivMLmods/OpenScienceReasoning-Qwen-e10-GGUF)
---
## **Key Features**
1. **Scientific Reasoning & Chain-of-Thought**
Fine-tuned on **10,000 curated entries** from the **OpenScienceReasoning-2** dataset, designed to enhance step-by-step analytical reasoning in science and mathematics.
2. **Advanced Code Reasoning & Generation**
Supports multi-language coding with explanations, optimization hints, and error detection—ideal for algorithm synthesis, debugging, and prototyping.
3. **Mathematical & Scientific Problem Solving**
Performs analytical reasoning in physics, biology, chemistry, and mathematics—explaining concepts, solving equations, and handling symbolic derivations.
4. **Hybrid Symbolic-AI Thinking**
Combines structured logic, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM-related tasks.
5. **Structured Output Mastery**
Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for technical documentation, research papers, and structured data.
6. **Optimized Lightweight Footprint for Versatile Deployment**
Balances performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**.
---
## **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/OpenScienceReasoning-Qwen-e10"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain the difference between Newtonian mechanics and quantum mechanics with examples."
messages = [
{"role": "system", "content": "You are a scientific tutor skilled in reasoning, math, and coding."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
---
## **Intended Use**
* Scientific tutoring, computational reasoning, and mathematical education
* Research assistant for physics, chemistry, biology, and interdisciplinary domains
* Structured technical data generation in multiple formats
* STEM-focused chatbot or API for research and education tools
* Deployment in mid-resource environments requiring high reasoning fidelity
## **Limitations**
* Not tuned for general-purpose or long-form creative writing
* Context limitations may hinder multi-document or full codebase analysis
* Specialized for scientific and technical reasoning—general chat may underperform
* Prioritizes structured logic over casual or emotional tone generation
|
sdagsadgd/blockassist-bc-sedate_squeaky_salamander_1756811025
|
sdagsadgd
| 2025-09-02T11:23:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sedate squeaky salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:23:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sedate squeaky salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BUT-FIT/diarizen-wavlm-large-s80-mlc
|
BUT-FIT
| 2025-09-02T11:22:56Z | 34 | 2 |
transformers
|
[
"transformers",
"pytorch",
"speaker",
"speaker-diarization",
"meeting",
"wavlm",
"wespeaker",
"diarizen",
"pyannote",
"pyannote-audio-pipeline",
"voice-activity-detection",
"arxiv:2506.13414",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
voice-activity-detection
| 2025-05-06T07:09:13Z |
---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: voice-activity-detection
tags:
- speaker
- speaker-diarization
- meeting
- wavlm
- wespeaker
- diarizen
- pyannote
- pyannote-audio-pipeline
---
## Overview
This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen) as described in [BUT System for the MLC-SLM Challenge](https://huggingface.co/papers/2506.13414). The EEND component is built upon WavLM-Large and Conformer layers. The model was pre-trained on far-field, single-channel audio from a diverse set of public datasets, including AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, MSDWild, DIHARD3, RAMC, and VoxConverse. Then structured pruning at 80% sparsity is applied. Finally, the pruned model is fine-tuned with [MLC-SLM](https://www.nexdata.ai/competition/mlc-slm) data. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
## Usage
```python
from diarizen.pipelines.inference import DiariZenPipeline
# load pre-trained model
diar_pipeline = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-wavlm-large-s80-mlc")
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav')
# print results
for turn, _, speaker in diar_results.itertracks(yield_label=True):
print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
# load pre-trained model and save RTTM result
diar_pipeline = DiariZenPipeline.from_pretrained(
"BUT-FIT/diarizen-wavlm-large-s80-mlc",
rttm_out_dir='.'
)
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav', sess_name='session_name')
```
## Results
DER evaluation of [Pyannote baseline](https://github.com/mubingshen/MLC-SLM-Baseline) and DiariZen, with **no collar** applied.
| Dataset | Pyannote | DiariZen |
|:-------------------|:--------:|:--------:|
| English-American | 20.18 | 15.88 |
| English-Australian | 13.76 | 10.82 |
| English-British | 18.85 | 12.07 |
| English-Filipino | 13.19 | 10.28 |
| English-Indian | 8.19 | 6.04 |
| French | 22.62 | 17.33 |
| German | 22.33 | 16.35 |
| Italian | 10.64 | 8.85 |
| Japanese | 26.46 | 17.81 |
| Korean | 23.25 | 16.36 |
| Portuguese | 17.60 | 14.77 |
| Russian | 11.37 | 9.99 |
| Spanish | 12.92 | 10.82 |
| Thai | 10.90 | 10.62 |
| Vietnamese | 14.64 | 12.69 |
| **Average** | **16.44**| **12.71**|
## Citation
If you found this work helpful, please consider citing:
```
@article{polok2025but,
title={BUT System for the MLC-SLM Challenge},
author={Polok, Alexander and Han, Jiangyu and Klement, Dominik and Cornell, Samuele and {\v{C}}ernock{\`y}, Jan and Burget, Luk{\'a}{\v{s}}},
journal={arXiv preprint arXiv:2506.13414},
year={2025}
}
```
## License
- **Source code**: MIT (see the [project’s GitHub repository](https://github.com/BUTSpeechFIT/DiariZen)).
- **Model weights**: CC BY-NC 4.0 (non-commercial).
- Rationale: some training datasets are research-only or non-commercial, so the released weights cannot be used commercially.
|
giovannidemuri/llama3b-llama8b-er-v542-seed2-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-09-02T11:22:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T09:50:11Z |
---
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]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
the-usan/urdu-crime-main-v2
|
the-usan
| 2025-09-02T11:22:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:22:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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### Testing Data, Factors & Metrics
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[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed]
|
xzitao/Helper140
|
xzitao
| 2025-09-02T11:22:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"question-answering",
"zh",
"dataset:xzitao/Admissions_InformationQA",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-09-01T12:19:11Z |
---
library_name: transformers
license: apache-2.0
datasets:
- xzitao/Admissions_InformationQA
language:
- zh
base_model:
- unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit
pipeline_tag: question-answering
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756812019
|
matherchodhuuu
| 2025-09-02T11:21:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:21:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756811993
|
xinnn32
| 2025-09-02T11:21:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:20:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1756812004
|
vendi11
| 2025-09-02T11:20:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:20:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756811956
|
Ferdi3425
| 2025-09-02T11:20:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:20:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamedkharazmi/blockassist-bc-tough_webbed_hamster_1756805628
|
hamedkharazmi
| 2025-09-02T11:20:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough webbed hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:19:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough webbed hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seoseo99/qwen2-1_5b-sum_lk_gemini
|
seoseo99
| 2025-09-02T11:18:27Z | 50 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"causal-lm",
"summarization",
"korean",
"finetuned",
"fp32",
"conversational",
"ko",
"en",
"base_model:Qwen/Qwen2-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T07:12:09Z |
---
license: apache-2.0
language:
- ko
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen2
- causal-lm
- summarization
- korean
- finetuned
- safetensors
- fp32
base_model: Qwen/Qwen2-1.5B-Instruct
model-index:
- name: qwen2-1_5b-sum_lk_gemini
results: []
---
# seoseo99/qwen2-1_5b-sum_lk_gemini
Qwen2-1.5B-Instruct를 한국어 여행/행사 **후기 요약** 용도로 미세조정한 1.5B 파라미터 모델입니다.
1–3문장 간결 요약, 핵심 포인트 추출, 여러 후기 합본 요약에 적합합니다.
---
## 파일 구성
- `config.json` — 모델 아키텍처 설정(hidden size, layer 수 등). 구조 정보라 보통 수정하지 않습니다.
- `generation_config.json` — `generate()`의 기본값(max_new_tokens, temperature, top_p, 등)
- `tokenizer.json` — Fast 토크나이저 전체 정의(vocab/merges/전처리 파이프라인 포함)
- `tokenizer_config.json` — 토크나이저 메타(model_max_length, 특수토큰 정책 등)
- `special_tokens_map.json` — `eos/pad` 등 특수 토큰 매핑
- `model-00001-of-00002.safetensors`, `model-00002-of-00002.safetensors` — 모델 가중치 샤드(shard) 파일
- `model.safetensors.index.json` — 각 파라미터 텐서가 어느 shard에 있는지 인덱스 맵
---
## Quickstart (Transformers)
### 1) Chat template + 결정적(beam) 생성
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
RID = "seoseo99/qwen2-1_5b-sum_lk_gemini"
tok = AutoTokenizer.from_pretrained(RID, use_fast=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = AutoModelForCausalLM.from_pretrained(
RID,
torch_dtype=torch.float32, # GPU면 "auto"/bfloat16 고려
device_map="auto",
).eval()
messages = [
{"role": "system", "content":
"여행/행사 리뷰를 1~3문장으로 담백하게 요약합니다. 과장/광고 톤 금지, 사실만 반영."},
{"role": "user", "content":
"행사명: 영도다리축제\n주소: 부산 영도구 …\n\n[리뷰]\n(리뷰 본문)\n\n1~3문장으로 요약해줘."}
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(
inputs,
max_new_tokens=128,
num_beams=4,
do_sample=False,
length_penalty=0.9,
repetition_penalty=1.05,
no_repeat_ngram_size=3,
eos_token_id=tok.eos_token_id,
)
print(tok.decode(out[0, inputs.shape[-1]:], skip_special_tokens=True).strip())
|
HHazard/t5_dual_surprise_order_1
|
HHazard
| 2025-09-02T11:17:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:17:55Z |
---
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. -->
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[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BUT-FIT/DiCoW_v2
|
BUT-FIT
| 2025-09-02T11:17:44Z | 141 | 2 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"arxiv:1910.09700",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T12:12:35Z |
---
library_name: transformers
license: cc-by-4.0
---
# 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]
|
vuitton/LouisVuitton_crn_v2.8
|
vuitton
| 2025-09-02T11:16:48Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:43Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756811700
|
kittygirlhere
| 2025-09-02T11:15:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:15:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hariharanv04/GPT-OSS-20B-SAP-SFT1
|
hariharanv04
| 2025-09-02T11:15:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:15:11Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hariharanv04
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BUT-FIT/diarizen-wavlm-large-s80-md
|
BUT-FIT
| 2025-09-02T11:15:16Z | 1,591 | 11 |
transformers
|
[
"transformers",
"pytorch",
"speaker",
"speaker-diarization",
"meeting",
"wavlm",
"wespeaker",
"diarizen",
"pyannote",
"pyannote-audio-pipeline",
"voice-activity-detection",
"arxiv:2505.24111",
"arxiv:2506.18623",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
voice-activity-detection
| 2025-04-15T07:58:50Z |
---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: voice-activity-detection
tags:
- speaker
- speaker-diarization
- meeting
- wavlm
- wespeaker
- diarizen
- pyannote
- pyannote-audio-pipeline
---
## Overview
This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen). The EEND component is built upon WavLM Large and Conformer layers. The model was trained on far-field, single-channel audio from a diverse set of public datasets, including AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, MSDWild, DIHARD3, RAMC, and VoxConverse.
Then structured pruning at 80% sparsity is applied. After pruning, the number of parameters in WavLM Large is reduced from **316.6M to 63.3M**, and the computational cost (MACs) decreases from **17.8G to 3.8G** per second. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
## Usage
```python
from diarizen.pipelines.inference import DiariZenPipeline
# load pre-trained model
diar_pipeline = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-wavlm-large-s80-md")
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav')
# print results
for turn, _, speaker in diar_results.itertracks(yield_label=True):
print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
# load pre-trained model and save RTTM result
diar_pipeline = DiariZenPipeline.from_pretrained(
"BUT-FIT/diarizen-wavlm-large-s80-md",
rttm_out_dir='.'
)
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav', sess_name='session_name')
```
## Results (collar=0s)
| Dataset | [Pyannote v3.1](https://github.com/pyannote/pyannote-audio) | DiariZen |
|:---------------|:-----------:|:-----------:|
| AMI | 22.4 | 14.0 |
| AISHELL-4 | 12.2 | 9.8 |
| AliMeeting | 24.4 | 12.5 |
| NOTSOFAR-1 | - | 17.9 |
| MSDWild | 25.3 | 15.6 |
| DIHARD3 | 21.7 | 14.5 |
| RAMC | 22.2 | 11.0 |
| VoxConverse | 11.3 | 9.2 |
## Citation
If you found this work helpful, please consider citing:
```
@inproceedings{han2025leveraging,
title={Leveraging self-supervised learning for speaker diarization},
author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Burget, Luk{\'a}{\v{s}}},
booktitle={Proc. ICASSP},
year={2025}
}
@article{han2025fine,
title={Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization},
author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Cernocky, Jan and Burget, Lukas},
journal={arXiv preprint arXiv:2505.24111},
year={2025}
}
@article{han2025efficient,
title={Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised Models},
author={Han, Jiangyu and P{\'a}lka, Petr and Delcroix, Marc and Landini, Federico and Rohdin, Johan and Cernock{\`y}, Jan and Burget, Luk{\'a}{\v{s}}},
journal={arXiv preprint arXiv:2506.18623},
year={2025}
}
```
## License
- **Source code**: MIT (see the [project’s GitHub repository](https://github.com/BUTSpeechFIT/DiariZen)).
- **Model weights**: CC BY-NC 4.0 (non-commercial).
- Rationale: some training datasets are research-only or non-commercial, so the released weights cannot be used commercially.
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1756811612
|
AnerYubo
| 2025-09-02T11:13:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756811314
|
liukevin666
| 2025-09-02T11:13:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:09:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sri2901/m_potrait
|
Sri2901
| 2025-09-02T11:13:28Z | 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
| 2025-09-02T11:13:14Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: m_port
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
widget:
- text: Sample generation
output:
url: samples/1756802319166__000000000_0.jpg
- text: Sample generation
output:
url: samples/1756802337105__000000000_1.jpg
- text: Sample generation
output:
url: samples/1756802355203__000000000_2.jpg
---
# m_portrait
Model trained with AI Toolkit by Ostris
<Gallery />
## Trigger words
You should use `m_port` 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](/username/m_portrait/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('username/m_portrait', weight_name='m_portrait_000000250.safetensors')
image = pipeline('m_port style artwork').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)
|
AnerYubo/blockassist-bc-fanged_camouflaged_cassowary_1756811594
|
AnerYubo
| 2025-09-02T11:13:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged camouflaged cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged camouflaged cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF
|
SHIBA212121
| 2025-09-02T11:12:24Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"license:llama3.3",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-02T11:11:57Z |
---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
tags:
- llama-cpp
- gguf-my-repo
---
# SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF
This model was converted to GGUF format from [`tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5`](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) 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/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) 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 SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.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 SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -c 2048
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756811397
|
bah63843
| 2025-09-02T11:10:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:10:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756811371
|
omerbkts
| 2025-09-02T11:09:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:09:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756811287
|
klmdr22
| 2025-09-02T11:08:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:08:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756809818
|
Loder-S
| 2025-09-02T11:08:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:08:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly knobby tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756811221
|
xinnn32
| 2025-09-02T11:08:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:07:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756811273
|
kittygirlhere
| 2025-09-02T11:08:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:08:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756809760
|
pempekmangedd
| 2025-09-02T11:07:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:07:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1756811096
|
karthickhere
| 2025-09-02T11:06:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:06:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DimaSK1/Qwen2-0.5B-bnb-4bit-sft-2
|
DimaSK1
| 2025-09-02T11:05:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Qwen2-0.5B-bnb-4bit",
"base_model:finetune:unsloth/Qwen2-0.5B-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:05:46Z |
---
base_model: unsloth/Qwen2-0.5B-bnb-4bit
library_name: transformers
model_name: Qwen2-0.5B-bnb-4bit-sft-2
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for Qwen2-0.5B-bnb-4bit-sft-2
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-bnb-4bit](https://huggingface.co/unsloth/Qwen2-0.5B-bnb-4bit).
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="DimaSK1/Qwen2-0.5B-bnb-4bit-sft-2", 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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0
- Datasets: 3.6.0
- 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}}
}
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756811046
|
bah63843
| 2025-09-02T11:04:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:04:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sunbird/qwen3-14b-sunflower-sft-translation-plus-multilingual-tasks-merged
|
Sunbird
| 2025-09-02T11:04:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T10:19:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Reihaneh/wav2vec2_uk_be_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T11:03:31Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:03:30Z |
---
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]
|
thefirstgoku/19A_w13_normally_l4
|
thefirstgoku
| 2025-09-02T11:03:22Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T11:02:43Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
gayathriai0024/salesforce-sentiment
|
gayathriai0024
| 2025-09-02T11:02:24Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-02T11:02:24Z |
---
license: apache-2.0
---
|
pidbu/blockassist-bc-whistling_alert_shrew_1756810831
|
pidbu
| 2025-09-02T11:01:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:01:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sagata007/df
|
sagata007
| 2025-09-02T11:01:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-09-02T11:01:36Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/7cfec45a-5c79-4368-98eb-30e128831c5e.png
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: GrittyDark style.
---
# df
<Gallery />
## Trigger words
You should use `GrittyDark style.` to trigger the image generation.
## Download model
[Download](/sagata007/df/tree/main) them in the Files & versions tab.
|
de-Rodrigo/paligemma-merit
|
de-Rodrigo
| 2025-09-02T11:00:44Z | 0 | 0 | null |
[
"safetensors",
"vision",
"document-understanding",
"paligemma",
"image-text-to-text",
"en",
"es",
"dataset:de-Rodrigo/merit",
"base_model:google/paligemma-3b-pt-224",
"base_model:finetune:google/paligemma-3b-pt-224",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-17T10:45:47Z |
---
license: mit
datasets:
- de-Rodrigo/merit
language:
- en
- es
base_model:
- google/paligemma-3b-pt-224
pipeline_tag: image-text-to-text
---
# DONUT Merit
<a href="https://x.com/nearcyan/status/1706914605262684394">
<div style="text-align: center;">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png">
<source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png">
<img alt="DragonHuggingFace" src="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png" style="width: 200px;">
</picture>
</div>
</a>
## Model Architecture
**This model is based on the Donut architecture and fine-tuned on the Merit dataset for form understanding tasks.**
- Backbone: [Paligemma](https://huggingface.co/google/paligemma-3b-pt-224)
- Training Data: [Merit](https://huggingface.co/datasets/de-Rodrigo/merit)
## Example Usage
```python
WIP
```
**WIP** 🛠️
|
bah63843/blockassist-bc-plump_fast_antelope_1756810772
|
bah63843
| 2025-09-02T11:00:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:00:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756810668
|
liukevin666
| 2025-09-02T10:59:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1756810641
|
karthickhere
| 2025-09-02T10:58:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756810673
|
omerbektass
| 2025-09-02T10:58:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
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