modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Emartur/Emartur_25
|
Emartur
| 2025-08-06T06:41:36Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-06T05:57:32Z |
---
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
---
|
Hooooooooooon/A.X-4.0-Light
|
Hooooooooooon
| 2025-08-06T06:41:05Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T06:31:00Z |
---
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 ๐ค 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]
|
nobrand/KULLM-R
|
nobrand
| 2025-08-06T06:39:15Z | 44 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"reasoning",
"LLMs",
"Korean",
"conversational",
"ko",
"en",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T05:24:12Z |
---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B
language:
- ko
- en
tags:
- reasoning
- LLMs
- Korean
---
# KULLM-R
Introducing KULLM-R, a large language model specialized for high-level reasoning queries in Korean, with a particular focus on complex mathematical problems. The model is designed to provide both the correct reasoning paths and answers for such queries, offering enhanced reasoning efficiency and language transferability to Korean compared to general-purpose reasoning models. Reinforcement learning strategy is employed for efficient reasoning path exploration and Korean-specific generation.
## Model Details
- **Model Name**: KULLM-R
- **Developer**: Seungyoon Lee, Minhyuk Kim, Dongjun Kim, Gyuho Shim and Chanjun Park, supported by [NLP&AI Lab in Korea University](https://nlp.korea.ac.kr/)
- **Languages**: Korean, English
- **Objective**: Producing efficient and interpretable reasoning paths and answers for high-level Korean reasoning queries
- **Training Framework**: verl, PyTorch, Transformers
- **Parameter Size**: 8B
### Model Description
KULLM-R is distinguished from standard reasoning LLMs based on Qwen3-8B by its focus on reinforcement learning-based reasoning path exploration and its strong proficiency in Korean language use. It is trained to generate efficient reasoning paths for both English and Korean problems and provides well-structured, readable answers in Korean, delivering strong interpretability and an outstanding user experience for Korean speakers.
### Key Features
- **Reasoning Efficiency Aware Reinforcement Learning**: Introduces RL techniques considering both reasoning path efficiency and answer correctness, reducing unnecessary steps while maintaining answer quality.
- **Reasoning Path Pruning**: Specialized for high-difficulty reasoning problems by pruning ineffective paths and emphasizing transparency and readability in generated answers.
- **Support High Readability in Korean System**: Enhanced both logical reasoning and natural Korean expression ability in answer.
- **Adaptive Length Penalty**: Adaptive penalties optimize the reasoning process according to the questionโs complexity and difficulty, ensuring efficient solutions for various math problems.
## Data & Training Process
- **Data Sources**: ko-limo (only 817 rows)
- **Training Strategy**: Uses reasoning problem difficulty-aware adaptive reward systems, implementing reinforcement learning with dynamic length penalty for optimal performance.
- **Iteration**: The model repeatedly trains on high-difficulty examples to optimize reasoning path generation.
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nobrand/KULLM-R"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
system_prompt = "You are a helpful assistant.\nPlease reason step by step, and put your final answer within \\boxed{{}}" # Recommend to use given system prompt
user_promt = "1๋ถํฐ 1008๊น์ง์ ์์ฐ์ ์ค 1008๊ณผ ์๋ก์์ธ ์์ฐ์์ ๊ฐฏ์๋ฅผ ๊ตฌํ์์ค."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_promt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
> [!NOTE]
> As mentioned in Qwen3, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
## Evaluation
- Shows superior reasoning efficiency, shorter reasoning steps, higher readability in Korean, and better explanation quality compared to models of similar scale when evaluated on HRM-8K.
| Task | Score | Think Step Length | Korean Response Ratio |
|------------|:-----:|:------------------:|:---------------------:|
| GSM8k | 91.9 | 896 | 94.47 |
| KSM | 70.9 | 7979 | 80.6 |
| MATH | 95.1 | 2668 | 96.12 |
| OMNI Math | 61.9 | 7987 | 73.91 |
<img src="KULLM_R_result.png" width="1000"/>
## Intended Use
- Solving complex Korean mathematical and logical reasoning problems
- Improved explainability for Korean logical reasoning
- Tutoring and educational support in reasoning fields
## Citation
```
@misc{KULLM-R2025,
title = {KULLM-R: Korea University Large Language Model for Reasoning},
author = {Korea University NLP&AI Lab},
year = {2025},
}
```
|
eiknarf/Qwen3-0.6B-Gensyn-Swarm-amphibious_lumbering_beaver
|
eiknarf
| 2025-08-06T06:36:28Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am amphibious_lumbering_beaver",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-28T16:13:29Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am amphibious_lumbering_beaver
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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|
SIGTIR/Qwen3-0.6B-Gensyn-Swarm-hulking_sharp_rhino
|
SIGTIR
| 2025-08-06T06:33:32Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am hulking_sharp_rhino",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-02T12:42:57Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am hulking_sharp_rhino
---
# 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 ๐ค 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]
|
minimimtoy25/guilherme1
|
minimimtoy25
| 2025-08-06T06:30:27Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-06T04:04:10Z |
---
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
---
|
Coaster41/patchtst-tsmixup-relu
|
Coaster41
| 2025-08-06T06:28:54Z | 74 | 0 |
transformers
|
[
"transformers",
"safetensors",
"patchtst",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T03:45:54Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: patchtst-tsmixup-relu
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. -->
# patchtst-tsmixup-relu
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Mse: 229.0122
- Mae: 0.6126
- Rmse: 15.1332
- Smape: 83.2157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 448
- eval_batch_size: 896
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 896
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Rmse | Smape |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:-------:|:--------:|
| 0.1785 | 0.1666 | 1000 | 0.1739 | 433.0006 | 0.7291 | 20.8087 | 100.7767 |
| 0.1655 | 0.3333 | 2000 | 0.1654 | 424.8635 | 0.7099 | 20.6122 | 74.2051 |
| 0.1663 | 0.4999 | 3000 | 0.1624 | 381.5935 | 0.6823 | 19.5344 | 142.9640 |
| 0.1632 | 0.6666 | 4000 | 0.1599 | 330.2523 | 0.6648 | 18.1728 | 78.8419 |
| 0.162 | 0.8332 | 5000 | 0.1592 | 322.0357 | 0.6595 | 17.9454 | 78.0993 |
| 0.1615 | 0.9998 | 6000 | 0.1581 | 294.6714 | 0.6549 | 17.1660 | 79.6810 |
| 0.162 | 1.1665 | 7000 | 0.1573 | 333.3479 | 0.6496 | 18.2578 | 81.2097 |
| 0.1587 | 1.3331 | 8000 | 0.1570 | 263.9417 | 0.6429 | 16.2463 | 90.2972 |
| 0.154 | 1.4998 | 9000 | 0.1565 | 268.0528 | 0.6515 | 16.3723 | 83.8961 |
| 0.1562 | 1.6664 | 10000 | 0.1561 | 287.6087 | 0.6475 | 16.9590 | 108.2025 |
| 0.1603 | 1.8330 | 11000 | 0.1558 | 281.4109 | 0.6507 | 16.7753 | 77.4054 |
| 0.1557 | 1.9997 | 12000 | 0.1550 | 281.4917 | 0.6408 | 16.7777 | 81.8113 |
| 0.1568 | 2.1663 | 13000 | 0.1546 | 258.6138 | 0.6406 | 16.0815 | 89.9780 |
| 0.1556 | 2.3329 | 14000 | 0.1545 | 268.3961 | 0.6425 | 16.3828 | 79.0450 |
| 0.1561 | 2.4996 | 15000 | 0.1538 | 249.9753 | 0.6366 | 15.8106 | 88.3079 |
| 0.1546 | 2.6662 | 16000 | 0.1535 | 239.0104 | 0.6313 | 15.4600 | 96.2489 |
| 0.1536 | 2.8329 | 17000 | 0.1534 | 232.7196 | 0.6318 | 15.2552 | 73.1808 |
| 0.1531 | 2.9995 | 18000 | 0.1537 | 224.2394 | 0.6249 | 14.9746 | 99.7205 |
| 0.1535 | 3.1661 | 19000 | 0.1530 | 253.5844 | 0.6296 | 15.9243 | 80.0392 |
| 0.1532 | 3.3328 | 20000 | 0.1529 | 256.6078 | 0.6314 | 16.0190 | 184.8716 |
| 0.1566 | 3.4994 | 21000 | 0.1531 | 228.1704 | 0.6266 | 15.1053 | 90.5678 |
| 0.1547 | 3.6661 | 22000 | 0.1527 | 216.8113 | 0.6265 | 14.7245 | 88.1824 |
| 0.1537 | 3.8327 | 23000 | 0.1522 | 241.5133 | 0.6282 | 15.5407 | 73.5045 |
| 0.1531 | 3.9993 | 24000 | 0.1521 | 232.2086 | 0.6302 | 15.2384 | 87.4450 |
| 0.1525 | 4.1660 | 25000 | 0.1523 | 253.6224 | 0.6328 | 15.9255 | 88.9352 |
| 0.1525 | 4.3326 | 26000 | 0.1517 | 254.2605 | 0.6304 | 15.9455 | 77.5196 |
| 0.1548 | 4.4993 | 27000 | 0.1519 | 225.7644 | 0.6212 | 15.0255 | 82.3784 |
| 0.1527 | 4.6659 | 28000 | 0.1519 | 220.0219 | 0.6254 | 14.8331 | 86.0485 |
| 0.153 | 4.8325 | 29000 | 0.1515 | 258.0009 | 0.6347 | 16.0624 | 145.4315 |
| 0.1521 | 4.9992 | 30000 | 0.1516 | 227.8417 | 0.6227 | 15.0944 | 76.3474 |
| 0.151 | 5.1658 | 31000 | 0.1514 | 213.8730 | 0.6185 | 14.6244 | 157.4075 |
| 0.1527 | 5.3324 | 32000 | 0.1510 | 238.2835 | 0.6189 | 15.4364 | 571.1568 |
| 0.1529 | 5.4991 | 33000 | 0.1510 | 270.1301 | 0.6278 | 16.4356 | 83.5608 |
| 0.1505 | 5.6657 | 34000 | 0.1511 | 241.0177 | 0.6271 | 15.5247 | 76.5107 |
| 0.1521 | 5.8324 | 35000 | 0.1516 | 255.7361 | 0.6331 | 15.9918 | 108.9967 |
| 0.1513 | 5.9990 | 36000 | 0.1507 | 253.1635 | 0.6233 | 15.9111 | 85.8247 |
| 0.1502 | 6.1656 | 37000 | 0.1509 | 255.3432 | 0.6230 | 15.9795 | 118.9721 |
| 0.1517 | 6.3323 | 38000 | 0.1504 | 238.2068 | 0.6213 | 15.4339 | 79.4896 |
| 0.151 | 6.4989 | 39000 | 0.1508 | 244.4908 | 0.6243 | 15.6362 | 98.8420 |
| 0.1516 | 6.6656 | 40000 | 0.1504 | 229.2746 | 0.6231 | 15.1418 | 71.1164 |
| 0.1506 | 6.8322 | 41000 | 0.1501 | 237.0237 | 0.6217 | 15.3956 | 74.7138 |
| 0.1503 | 6.9988 | 42000 | 0.1500 | 240.7731 | 0.6206 | 15.5169 | 85.3629 |
| 0.1493 | 7.1655 | 43000 | 0.1501 | 265.2171 | 0.6242 | 16.2855 | 157.2147 |
| 0.1501 | 7.3321 | 44000 | 0.1499 | 247.8091 | 0.6219 | 15.7420 | 98.6004 |
| 0.1508 | 7.4988 | 45000 | 0.1497 | 265.8900 | 0.6227 | 16.3061 | 72.4383 |
| 0.1518 | 7.6654 | 46000 | 0.1497 | 249.7165 | 0.6216 | 15.8024 | 110.3652 |
| 0.1502 | 7.8320 | 47000 | 0.1496 | 248.1616 | 0.6200 | 15.7531 | 77.3612 |
| 0.1503 | 7.9987 | 48000 | 0.1493 | 237.9707 | 0.6190 | 15.4263 | 71.8934 |
| 0.1502 | 8.1653 | 49000 | 0.1494 | 225.7567 | 0.6149 | 15.0252 | 78.6202 |
| 0.1492 | 8.3319 | 50000 | 0.1492 | 258.1519 | 0.6185 | 16.0671 | 73.3061 |
| 0.1513 | 8.4986 | 51000 | 0.1491 | 226.3746 | 0.6162 | 15.0458 | 118.5835 |
| 0.1508 | 8.6652 | 52000 | 0.1491 | 236.9618 | 0.6171 | 15.3936 | 80.4855 |
| 0.1517 | 8.8319 | 53000 | 0.1490 | 242.2040 | 0.6186 | 15.5629 | 144.8560 |
| 0.1494 | 8.9985 | 54000 | 0.1490 | 237.0488 | 0.6174 | 15.3964 | 78.5948 |
| 0.1477 | 9.1651 | 55000 | 0.1488 | 232.7170 | 0.6157 | 15.2551 | 82.3074 |
| 0.1499 | 9.3318 | 56000 | 0.1488 | 236.9111 | 0.6168 | 15.3919 | 77.6623 |
| 0.1524 | 9.4984 | 57000 | 0.1487 | 231.8599 | 0.6148 | 15.2269 | 102.9215 |
| 0.1505 | 9.6651 | 58000 | 0.1486 | 230.8095 | 0.6139 | 15.1924 | 67.3176 |
| 0.1507 | 9.8317 | 59000 | 0.1485 | 231.5027 | 0.6137 | 15.2152 | 84.0686 |
| 0.1461 | 9.9983 | 60000 | 0.1485 | 229.0122 | 0.6126 | 15.1332 | 83.2157 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 2.17.1
- Tokenizers 0.21.1
|
ScatterRaven/klue-ner-koelectra
|
ScatterRaven
| 2025-08-06T06:23:46Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T06:06:57Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 35
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
crystalline7/494948
|
crystalline7
| 2025-08-06T06:23:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T06:23:40Z |
[View on Civ Archive](https://civitaiarchive.com/models/520634?modelVersionId=578468)
|
crystalline7/1185908
|
crystalline7
| 2025-08-06T06:23:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T06:23:28Z |
[View on Civ Archive](https://civitaiarchive.com/models/520634?modelVersionId=1281331)
|
crystalline7/1373977
|
crystalline7
| 2025-08-06T06:23:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T06:23:18Z |
[View on Civ Archive](https://civitaiarchive.com/models/1290328?modelVersionId=1456032)
|
tiantiaf/voxlect-english-dialect-whisper-large-v3
|
tiantiaf
| 2025-08-06T06:23:03Z | 266 | 1 |
transformers
|
[
"transformers",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"speaker_dialect_classification",
"audio-classification",
"en",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:ajd12342/paraspeechcaps",
"arxiv:2508.01691",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-08-02T00:50:17Z |
---
base_model:
- openai/whisper-large-v3
datasets:
- mozilla-foundation/common_voice_11_0
- ajd12342/paraspeechcaps
language:
- en
license: openrail
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- speaker_dialect_classification
library_name: transformers
---
# Whisper-Large v3 for English Dialect Classification
# Model Description
This model includes the implementation of English dialect classification described in <a href="https://arxiv.org/abs/2508.01691"><strong>**Voxlect: A Speech Foundation Model Benchmark for Modeling Dialect and Regional Languages Around the Globe**</strong></a>
Github repository: https://github.com/tiantiaf0627/voxlect
The included English dialects are:
```
[
'East Asia',
'English',
'Germanic',
'Irish',
'North America',
'Northern Irish',
'Oceania',
'Other',
'Romance',
'Scottish',
'Semitic',
'Slavic',
'South African',
'Southeast Asia',
'South Asia',
'Welsh'
]
```
Compared to Vox-Profile English accent/dialect models, we trained with additional speech data from TIMIT and ParaSpeechCaps.
# How to use this model
## Download repo
```bash
git clone [email protected]:tiantiaf0627/voxlect
```
## Install the package
```bash
conda create -n voxlect python=3.8
cd voxlect
pip install -e .
```
## Load the model
```python
# Load libraries
import torch
import torch.nn.functional as F
from src.model.dialect.whisper_dialect import WhisperWrapper
# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
# Load model from Huggingface
model = WhisperWrapper.from_pretrained("tiantiaf/voxlect-english-dialect-whisper-large-v3").to(device)
model.eval()
```
## Prediction
```python
# Label List
dialect_list = [
'East Asia',
'English',
'Germanic',
'Irish',
'North America',
'Northern Irish',
'Oceania',
'Other',
'Romance',
'Scottish',
'Semitic',
'Slavic',
'South African',
'Southeast Asia',
'South Asia',
'Welsh'
]
# Load data, here just zeros as the example
# Our training data filters output audio shorter than 3 seconds (unreliable predictions) and longer than 15 seconds (computation limitation)
# So you need to prepare your audio to a maximum of 15 seconds, 16kHz and mono channel
max_audio_length = 15 * 16000
data = torch.zeros([1, 16000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)
# Probability and output
dialect_prob = F.softmax(logits, dim=1)
print(dialect_list[torch.argmax(dialect_prob).detach().cpu().item()])
```
Responsible Use: Users should respect the privacy and consent of the data subjects, and adhere to the relevant laws and regulations in their jurisdictions when using Voxlect.
## If you have any questions, please contact: Tiantian Feng ([email protected])
โ **Out-of-Scope Use**
- Clinical or diagnostic applications
- Surveillance
- Privacy-invasive applications
|
ashun989/GlimpsePrune_Qwen2.5-VL-7B-Instruct
|
ashun989
| 2025-08-06T06:22:44Z | 12 | 2 |
transformers
|
[
"transformers",
"qwen2_5_vl_gp",
"image-to-text",
"image-text-to-text",
"arxiv:2508.01548",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-02T01:47:57Z |
---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---
# GlimpsePrune: A Dynamic Visual Token Pruning for Large Vision-Language Models
**GlimpsePrune** is a dynamic visual token pruning framework designed for Large Vision-Language Models (LVLMs). This model was presented in the paper [A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models](https://huggingface.co/papers/2508.01548).
Existing methods for visual token compression typically adopt fixed compression ratios, which cannot adapt to scenes of varying complexity, often causing imprecise pruning that discards informative visual tokens and results in degraded model performance. Inspired by human cognition, GlimpsePrune addresses this issue by taking a data-driven "glimpse" and pruning irrelevant visual tokens in a single forward pass before answer generation. This approach prunes 92.6% of visual tokens while on average fully retaining the baseline performance on free-form VQA tasks. The reduced computational cost also enables more effective fine-tuning: an enhanced GlimpsePrune+ achieves 110% of the baseline performance while maintaining a similarly high pruning rate. Our work paves a new way for building more powerful and efficient LVLMs.
For the official code and more details, please refer to the [GitHub repository](https://github.com/HVision-NKU/GlimpsePrune).
<div align="center">
<img src="https://github.com/HVision-NKU/GlimpsePrune/raw/main/assets/case1.png" width="80%">
<img src="https://github.com/HVision-NKU/GlimpsePrune/raw/main/assets/case2.png" width="80%">
<br>
<em>GlimpsePrune dynamically prunes a large number of irrelevant visual tokens before answering questions, reducing the model's inference overhead.</em>
</div>
## โจ Key Features
- **High Pruning Rate**: Prunes over **90%** of visual tokens on average with almost no performance loss, effectively reducing computational and memory overhead.
- **Robust Performance**: Stable performance when processing high-resolution images and handling complex **free-form VQA** tasks.
- **Lightweight Training**: Only a few extra parameters (Glimpse token and VIP) need to be trained, completed in less than 1 hour on a single A100 GPU.
- **Broad Compatibility**: Supports single and multi-image inputs, is compatible with KV-Cache and Flash Attention 2, and provides a fair comparison benchmark with other mainstream visual compression methods.
## ๐ผ๏ธ Framework Overview
The core idea of GlimpsePrune is to introduce a **glimpse token** and a lightweight **Visual tokens Important Predictor (VIP)** that can quickly identify and retain the visual regions most relevant to the text prompt, pruning the remaining redundant information.
<div align="center">
<img src="https://github.com/HVision-NKU/GlimpsePrune/raw/main/assets/framework.png" width="70%">
</div>
## ๐ Performance Results
We evaluated GlimpsePrune on multiple VQA benchmarks. The results show that it achieves a high pruning rate while maintaining performance on par with the original model, outperforming other visual compression methods.
<p align="center">
<b>Free-form VQA Benchmarks</b><br>
<img src="https://github.com/HVision-NKU/GlimpsePrune/raw/main/assets/freeform_results.png" width="90%">
</p>
<p align="center">
<b>Short-form VQA Benchmarks</b><br>
<img src="https://github.com/HVision-NKU/GlimpsePrune/raw/main/assets/shortform_results.png" width="90%">
</p>
## ๐ฆ Models and Data
### Model Download
All models can be automatically downloaded from the Hugging Face Hub. `<new_module>` are the weights of the extra glimpse token and VIP modules we trained.
|`<base_model>`| `<new_module>` |
|:---:|:---:|
|[Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)|[ashun989/GlimpsePrune_Qwen2.5-VL-3B-Instruct](https://huggingface.co/ashun989/GlimpsePrune_Qwen2.5-VL-3B-Instruct)|
|[Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)|[ashun989/GlimpsePrune_Qwen2.5-VL-7B-Instruct](https://huggingface.co/ashun989/GlimpsePrune_Qwen2.5-VL-3B-Instruct)|
|[liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b)|[ashun989/GlimpsePrune_LLaVA-1.5-7B](https://huggingface.co/ashun989/GlimpsePrune_LLaVA-1.5-7B)|
|[liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b)|[ashun989/GlimpsePrune_LLaVA-1.5-13B](https://huggingface.co/ashun989/GlimpsePrune_LLaVA-1.5-13B)|
## โถ๏ธ How to Use
You can use `GlimpsePrune` with the `transformers_gp`, which is located in the [GitHub repository](https://github.com/HVision-NKU/GlimpsePrune).
```python
from transformers_gp.models.qwen2_5_vl import (
Qwen2_5_VL_GP_ForConditionalGeneration,
Qwen2_5_VL_GP_Processor
)
from qwen_vl_utils import process_vision_info
from PIL import Image
import torch
# Load the model and processor
base_model_name = "Qwen/Qwen2.5-VL-7B-Instruct"
new_model_name = "ashun989/GlimpsePrune_Qwen2.5-VL-7B-Instruct"
model = Qwen2_5_VL_GP_ForConditionalGeneration.from_pretrained(
base_model,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map={"": "cuda:0"},
)
processor = Qwen2_5_VL_GP_Processor.from_pretrained(base_model)
model.load_new_modules(new_modules_dir)
model.eval()
# Prepare messages (image and text input)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "../examples/people.png", # Placeholder: replace with your image path
},
{"type": "text", "text": "What kind of a tie is the groom wearing?"},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
# Generate output
model.reset_image_tokens_cache() # NOTE: reset the cache before inference
with torch.inference_mode():
generated_ids = model.generate(**inputs, max_new_tokens=1024, do_selection=True) # Enable glimpse prune by do_selection=True
# Decode and print the response
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
print(f"User: {question}
Assistant: {output_text[0]}")
```
## ๐๏ธ Citation
If you find our work helpful, please consider citing our paper:
```bibtex
@misc{zeng2025glimpseprune,
title={A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models},
author={Quan-Sheng Zeng and Yunheng Li and Qilong Wang and Peng-Tao Jiang and Zuxuan Wu and Ming-Ming Cheng and Qibin Hou},
year={2025},
eprint={2508.01548},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.01548},
}
```
|
crystalline7/1291290
|
crystalline7
| 2025-08-06T06:22:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T06:22:35Z |
[View on Civ Archive](https://civitaiarchive.com/models/1232462?modelVersionId=1388769)
|
tiantiaf/voxlect-thai-dialect-whisper-large-v3
|
tiantiaf
| 2025-08-06T06:22:16Z | 63 | 0 |
transformers
|
[
"transformers",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"speaker_dialect_classification",
"audio-classification",
"th",
"arxiv:2508.01691",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-07-29T10:10:55Z |
---
base_model:
- openai/whisper-large-v3
language:
- th
license: openrail
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- speaker_dialect_classification
library_name: transformers
---
# Whisper-Large v3 for Thai Dialect Classification
# Model Description
This model includes the implementation of Thai dialect classification described in <a href="https://arxiv.org/abs/2508.01691"><strong>**Voxlect: A Speech Foundation Model Benchmark for Modeling Dialect and Regional Languages Around the Globe**</strong></a>
Github repository: https://github.com/tiantiaf0627/voxlect
The included Thai dialects are:
```
[
"Khummuang",
"Korat",
"Pattani",
"Thai Central"
]
```
# How to use this model
## Download repo
```bash
git clone [email protected]:tiantiaf0627/voxlect
```
## Install the package
```bash
conda create -n voxlect python=3.8
cd voxlect
pip install -e .
```
## Load the model
```python
# Load libraries
import torch
import torch.nn.functional as F
from src.model.dialect.whisper_dialect import WhisperWrapper
# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
# Load model from Huggingface
model = WhisperWrapper.from_pretrained("tiantiaf/voxlect-thai-dialect-whisper-large-v3").to(device)
model.eval()
```
## Prediction
```python
# Label List
dialect_list = [
"Khummuang",
"Korat",
"Pattani",
"Thai Central"
]
# Load data, here just zeros as an example
# Our training data filters output audio shorter than 3 seconds (unreliable predictions) and longer than 15 seconds (computation limitation)
# So you need to prepare your audio to a maximum of 15 seconds, 16kHz, and mono channel
max_audio_length = 15 * 16000
data = torch.zeros([1, 16000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)
# Probability and output
dialect_prob = F.softmax(logits, dim=1)
print(dialect_list[torch.argmax(dialect_prob).detach().cpu().item()])
```
Responsible Use: Users should respect the privacy and consent of the data subjects, and adhere to the relevant laws and regulations in their jurisdictions when using Voxlect.
## If you have any questions, please contact: Tiantian Feng ([email protected])
โ **Out-of-Scope Use**
- Clinical or diagnostic applications
- Surveillance
- Privacy-invasive applications
|
jerseyjerry/task-13-Qwen-Qwen2.5-3B-Instruct
|
jerseyjerry
| 2025-08-06T06:21:13Z | 67 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:adapter:microsoft/Phi-4-mini-instruct",
"license:other",
"region:us"
] | null | 2025-08-05T12:54:20Z |
---
library_name: peft
license: other
base_model: microsoft/Phi-4-mini-instruct
---
<!-- 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. -->
# lora
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Framework versions
- PEFT 0.15.2
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
crystalline7/507012
|
crystalline7
| 2025-08-06T06:18:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T06:18:06Z |
[View on Civ Archive](https://civitaiarchive.com/models/532272?modelVersionId=591565)
|
GetSoloTech/Physical_AI
|
GetSoloTech
| 2025-08-06T06:18:11Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lfm2",
"text-generation",
"text-generation-inference",
"conversational",
"en",
"dataset:GetSoloTech/Physical_AI",
"base_model:LiquidAI/LFM2-700M",
"base_model:finetune:LiquidAI/LFM2-700M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T05:52:51Z |
---
base_model:
- LiquidAI/LFM2-700M
tags:
- text-generation-inference
- transformers
- lfm2
language:
- en
datasets:
- GetSoloTech/Physical_AI
library_name: transformers
---
# Uploaded finetuned model
- **Developed by:** GetSoloTech
This lfm2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
Comfy-Org/Qwen-Image_ComfyUI
|
Comfy-Org
| 2025-08-06T06:18:04Z | 121,788 | 88 |
diffusion-single-file
|
[
"diffusion-single-file",
"comfyui",
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T02:44:50Z |
---
license: apache-2.0
tags:
- diffusion-single-file
- comfyui
---
See: https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/
|
LLCC506/InternVL-X-8B
|
LLCC506
| 2025-08-06T06:08:29Z | 8 | 0 | null |
[
"safetensors",
"internvl_chat",
"custom_code",
"region:us"
] | null | 2025-08-05T02:21:23Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# InternVL-X-8B
## How to Get Started with the Model
```
import numpy as np
import time
import math
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
import os
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
block_h = math.ceil(orig_height / image_size)
block_w = math.ceil(orig_width / image_size)
max_num_new = block_h * block_w
if max_num_new > max_num:
max_num_new = max_num
max_num = max_num_new
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
path = 'InternVL-X-8B'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attention_2=False,
trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
generation_config = dict(max_new_tokens=1024, do_sample=False)
pixel_values = load_image('examples/image1.jpg', max_num=1).to(torch.bfloat16).cuda()
# single-image single-round conversation (ๅๅพๅ่ฝฎๅฏน่ฏ)
question = '<image>\nDescribe this image in datail'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')
# single-image multi-round conversation (ๅๅพๅค่ฝฎๅฏน่ฏ)
question = '<image>\nPlease describe the image in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Please write a story according to the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
```
|
Alic-Li/RWKV-v7-LibChara-lora-3b.st
|
Alic-Li
| 2025-08-06T06:08:11Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T07:44:37Z |
---
license: apache-2.0
---
|
sourled/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shaggy_wild_alpaca
|
sourled
| 2025-08-06T06:05:30Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shaggy wild alpaca",
"unsloth",
"trl",
"genrl-swarm",
"I am shaggy_wild_alpaca",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T13:08:53Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shaggy_wild_alpaca
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shaggy wild alpaca
- unsloth
- trl
- genrl-swarm
- I am shaggy_wild_alpaca
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shaggy_wild_alpaca
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sourled/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shaggy_wild_alpaca", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ggmancer/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silent_dormant_peacock
|
ggmancer
| 2025-08-06T06:05:20Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am silent_dormant_peacock",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-02T18:44:07Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am silent_dormant_peacock
---
# 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 ๐ค 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]
|
Uzaki12/Qwen3-0.6B-Gensyn-Swarm-bellowing_sleek_clam
|
Uzaki12
| 2025-08-06T06:05:20Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am bellowing_sleek_clam",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T06:03:35Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am bellowing_sleek_clam
---
# 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 ๐ค 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]
|
sourled/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scaly_aquatic_clam
|
sourled
| 2025-08-06T06:05:19Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scaly aquatic clam",
"unsloth",
"trl",
"genrl-swarm",
"I am scaly_aquatic_clam",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:42:34Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scaly_aquatic_clam
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scaly aquatic clam
- unsloth
- trl
- genrl-swarm
- I am scaly_aquatic_clam
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scaly_aquatic_clam
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sourled/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scaly_aquatic_clam", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AminuPeril/Qwen3-0.6B-Gensyn-Swarm-reptilian_moist_badger
|
AminuPeril
| 2025-08-06T06:04:49Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am reptilian_moist_badger",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-09T11:23:02Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am reptilian_moist_badger
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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|
hangangdam/klue-ner-koelectra
|
hangangdam
| 2025-08-06T06:04:36Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T06:04:23Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
notsatoshi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-shy_amphibious_snake
|
notsatoshi
| 2025-08-06T06:04:15Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am shy_amphibious_snake",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-16T10:24:15Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am shy_amphibious_snake
---
# 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 ๐ค 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]
|
casperbenya/Qwen3-0.6B-Gensyn-Swarm-peaceful_sleek_bear
|
casperbenya
| 2025-08-06T06:03:34Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am peaceful_sleek_bear",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-19T21:37:20Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am peaceful_sleek_bear
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
pepppper/klue-ner-koelectra
|
pepppper
| 2025-08-06T06:03:24Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T06:03:02Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_large_caribou
|
razor534
| 2025-08-06T06:03:16Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mottled large caribou",
"trl",
"genrl-swarm",
"I am mottled_large_caribou",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-09T18:21:05Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_large_caribou
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mottled large caribou
- trl
- genrl-swarm
- I am mottled_large_caribou
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_large_caribou
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_large_caribou", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.1
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
aARSNT/Qwen3-0.6B-Gensyn-Swarm-patterned_alert_bear
|
aARSNT
| 2025-08-06T06:03:03Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am patterned_alert_bear",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-02T18:42:11Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am patterned_alert_bear
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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
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#### Hardware
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#### Software
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## 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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|
jjiaweiyang/l-DeTok
|
jjiaweiyang
| 2025-08-06T06:03:02Z | 0 | 1 |
pytorch
|
[
"pytorch",
"computer-vision",
"image-generation",
"tokenizer",
"autoencoder",
"denoising",
"visual-tokenizer",
"imagenet",
"generative-modeling",
"image-feature-extraction",
"arxiv:2507.15856",
"license:mit",
"region:us"
] |
image-feature-extraction
| 2025-07-21T03:30:55Z |
---
library_name: pytorch
license: mit
pipeline_tag: image-feature-extraction
tags:
- computer-vision
- image-generation
- tokenizer
- autoencoder
- denoising
- visual-tokenizer
- imagenet
- generative-modeling
---
# DeTok: Latent Denoising Makes Good Visual Tokenizers
[](https://arxiv.org/abs/2507.15856)
[](https://github.com/Jiawei-Yang/DeTok)
## Model Description
**l-DeTok** (Latent Denoising Tokenizer) is a simple approach for training visual tokenizers by incorporating denoising objectives during tokenizer training. We observe that many modern generative models share a common training paradigm of reconstructing clean signals from corrupted inputs, and explore whether aligning tokenizer training with this principle might be beneficial for downstream generation tasks.
### Approach
We note that modern generative models often involve **reconstructing clean signals from corrupted inputs** (a form of denoising). This work investigates whether training tokenizers to reconstruct clean images from corrupted latent embeddings might produce representations that are better suited for downstream generative modeling.
### Architecture
- **Encoder-Decoder Architecture**: Based on Vision Transformers (ViT)
- **Denoising Strategies**:
- **Interpolative Latent Noise**: Corrupts latent embeddings through noise interpolation
- **Random Masking**: Masks random subsets of image patches during training
- **Training Losses**: Same as conventional image tokenizers.
## Model Variants
| Model | Type | Parameters | Description |
|-------|------|------------|-------------|
| **DeTok-BB** | Tokenizer | 172M | Base tokenizer with denoising training |
| **DeTok-BB-decoder_ft** | Tokenizer | 172M | Base tokenizer with additional decoder fine-tuning |
## Results
We evaluate our approach across six generative models on ImageNet 256ร256 and observe consistent improvements:
### With MAR Models (FID-50k with CFG)
| Model | FID-50K | Inception Score | Parameters |
|-------|---------|-----------------|------------|
| MAR-Base + MAR-VAE | 2.31 | 281.7 | 208M |
| MAR-Base + DeTok-BB | **1.61** | **289.7** | 208M |
| MAR-Base + DeTok-BB-decoder_ft | **1.55** | **291.0** | 208M |
| MAR-Large + MAR-VAE | 1.78 | 296.0 | 479M |
| MAR-Huge + MAR-VAE | 1.55 | 303.7 | 943M |
| MAR-Large + DeTok-BB | **1.43** | **303.5** | 479M |
| MAR-Large + DeTok-BB-decoder_ft | **1.32** | **304.1** | 479M |
### Observations
- **MAR-B**: FID improves from 2.31 (MAR-VAE) โ 1.55 (Ours)
- **MAR-L**: FID improves from 1.78 (MAR-VAE) โ 1.35 (Ours)
- The approach works across both non-autoregressive (DiT, SiT, LightningDiT) and autoregressive models (MAR, RasterAR, RandomAR)
## Usage
### Installation
To use DeTok for extracting latent embeddings from images, you need to:
1. **Clone the official DeTok repository**:
```bash
git clone https://github.com/Jiawei-Yang/DeTok.git
cd DeTok
pip install -r requirements.txt
```
2. **Download the pre-trained tokenizer weights**:
You can download the `DeTok-BB-decoder_ft` checkpoint (recommended) from [here](https://huggingface.co/jjiaweiyang/l-DeTok/resolve/main/detok-BB-gamm3.0-m0.7-decoder_tuned.pth) and place it in your working directory (e.g., `detok-BB-gamm3.0-m0.7-decoder_tuned.pth`).
### Extract latent embeddings
Here's a sample Python code snippet for feature extraction using the `DeTok_BB` tokenizer:
```python
import torch
from PIL import Image
from torchvision.transforms import transforms
from models.detok import DeTok_BB # Import from the cloned DeTok repository
# --- Configuration (matching DeTok-BB-decoder_ft architecture from paper) ---
model_params = {
"img_size": 256,
"patch_size": 16,
"in_chans": 3,
"embed_dim": 768,
"depths": [2, 2, 8, 2],
"num_heads": [3, 6, 12, 24],
}
tokenizer_weights_path = "detok-BB-gamm3.0-m0.7-decoder_tuned.pth" # Path to your downloaded weights
# 1. Initialize and load the tokenizer
tokenizer = DeTok_BB(**model_params).eval()
if torch.cuda.is_available():
tokenizer = tokenizer.cuda()
# Load checkpoint state_dict
checkpoint = torch.load(tokenizer_weights_path, map_location='cpu')
tokenizer.load_state_dict(checkpoint['model'])
# 2. Prepare your image
transform = transforms.Compose([
transforms.Resize(model_params["img_size"]),
transforms.CenterCrop(model_params["img_size"]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# Replace 'path/to/your/image.jpg' with your actual image file
image = Image.new('RGB', (model_params["img_size"], model_params["img_size"]), color = 'red') # Example dummy image
# image = Image.open("path/to/your/image.jpg").convert("RGB")
pixel_values = transform(image).unsqueeze(0) # Add batch dimension
if torch.cuda.is_available():
pixel_values = pixel_values.cuda()
# 3. Extract latent embeddings
with torch.no_grad():
latent_embeddings = tokenizer.encode(pixel_values)
print(f"Shape of latent embeddings: {latent_embeddings.shape}")
# Expected output for a 256x256 input image with 16x16 patches is (1, 256, 768),
# representing 256 image patches with 768-dimensional embeddings.
```
## Training Details
### Tokenizer Training
- **Dataset**: ImageNet train set
- **Resolution**: 256ร256
- **Batch Size**: 1024 (global)
- **Epochs**: 200 (base) + 100 (decoder fine-tuning)
- **Denoising Parameters**:
- Gamma (noise strength): 3.0
- Mask ratio: 0.7
- Random masking ratio: sampled from max(0, U(-0.1, M))
### Key Training Components
- **Interpolative Noise**: x' = (1-ฯ)x + ฯฮต(ฮณ), where ฯ ~ U(0,1)
- **Random Masking**: Variable masking ratios during training
- **Multi-component Loss**: MSE + KL + Perceptual + Adversarial losses
## Technical Details
### Denoising Methodology
1. **Interpolative Latent Noise**: We use interpolative noise rather than additive noise, which allows for heavier corruption when the noise level ฯ is high
2. **Masking as Deconstruction**: We explore random masking as another form of latent deconstruction, inspired by masked autoencoders
3. **Downstream Alignment**: The denoising objective is designed to align with how modern generative models operate
### Potential Benefits
- **Task Alignment**: The training objective is designed to match downstream generative model objectives
- **Simplicity**: The approach works without requiring large-scale pretrained visual encoders or semantic distillation
- **Generality**: We observe improvements across different types of generative models
- **Robustness**: The learned representations appear to remain useful even under corruption
## Evaluation
### Datasets
- **Training**: ImageNet train set
- **Evaluation**: ImageNet validation set (50k images)
### Metrics
- **FID-50k**: Frรฉchet Inception Distance on 50,000 generated samples
- **Inception Score**: Standard generative model evaluation metric
- **Precision & Recall**: Using ImageNet validation precision-recall data
## Citation
```bibtex
@article{yang2025detok,
title={Latent Denoising Makes Good Visual Tokenizers},
author={Jiawei Yang and Tianhong Li and Lijie Fan and Yonglong Tian and Yue Wang},
journal={arXiv preprint arXiv:2507.15856},
year={2025}
}
```
## License
This project is licensed under the MIT License.
## Acknowledgments
This work builds upon many excellent open-source projects. We are particularly grateful to:
- [MAR](https://github.com/LTH14/mar) for masked autoregressive modeling
- [DiT](https://github.com/facebookresearch/DiT) for diffusion transformers
- [MAE](https://github.com/facebookresearch/mae) for masked autoencoder insights
- [1d-tokenizer](https://github.com/bytedance/1d-tokenizer) for tokenizer implementations
- The broader research community for foundational work in generative modeling
## Contact
For questions or issues, please open a GitHub issue at the [official repository](https://github.com/Jiawei-Yang/DeTok).
|
razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_scurrying_hare
|
razor534
| 2025-08-06T06:03:00Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stealthy scurrying hare",
"unsloth",
"trl",
"genrl-swarm",
"I am stealthy_scurrying_hare",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-20T13:28:42Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_scurrying_hare
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stealthy scurrying hare
- unsloth
- trl
- genrl-swarm
- I am stealthy_scurrying_hare
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_scurrying_hare
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_scurrying_hare", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
IncarnateWorld/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mammalian_scavenging_grasshopper
|
IncarnateWorld
| 2025-08-06T06:02:29Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am mammalian_scavenging_grasshopper",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T06:01:54Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am mammalian_scavenging_grasshopper
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### 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
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dinesh-001/whisper_finetune_v0
|
dinesh-001
| 2025-08-06T06:00:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T06:00:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Model Card Contact
[More Information Needed]
|
mradermacher/aquif-3-mini-i1-GGUF
|
mradermacher
| 2025-08-06T06:00:05Z | 273 | 0 |
transformers
|
[
"transformers",
"gguf",
"language",
"aquif",
"text-generation-inference",
"math",
"coding",
"small",
"pt",
"en",
"ja",
"zh",
"th",
"es",
"hi",
"fr",
"de",
"it",
"base_model:aquiffoo/aquif-3-mini",
"base_model:quantized:aquiffoo/aquif-3-mini",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-06T04:26:59Z |
---
base_model: aquiffoo/aquif-3-mini
language:
- pt
- en
- ja
- zh
- th
- es
- hi
- fr
- de
- it
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- language
- aquif
- text-generation-inference
- math
- coding
- small
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/aquiffoo/aquif-3-mini
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#aquif-3-mini-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/aquif-3-mini-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/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ1_S.gguf) | i1-IQ1_S | 1.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ1_M.gguf) | i1-IQ1_M | 1.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_S.gguf) | i1-IQ2_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_M.gguf) | i1-IQ2_M | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_S.gguf) | i1-IQ3_S | 1.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_M.gguf) | i1-IQ3_M | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_0.gguf) | i1-Q4_0 | 2.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_1.gguf) | i1-Q4_1 | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q6_K.gguf) | i1-Q6_K | 2.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
jxchlee/klue-ner-koelectra
|
jxchlee
| 2025-08-06T05:58:36Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"tokenclassification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T05:58:13Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- tokenclassification
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue-ner dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
bestow136/CAPIMAC
|
bestow136
| 2025-08-06T05:58:12Z | 0 | 0 | null |
[
"arxiv:2507.03917",
"license:apache-2.0",
"region:us"
] | null | 2025-08-06T05:53:33Z |
---
license: apache-2.0
---
ijcai2025๏ผConsistency-Aware Padding for Incomplete Multi-Modal Alignment Clustering Based on Self-Repellent Greedy Anchor Search
If this article is helpful to you, please consider citing it.
@article{ma2025consistency, title={Consistency-Aware Padding for Incomplete Multi-Modal Alignment Clustering Based on Self-Repellent Greedy Anchor Search}, author={Ma, Shubin and Zhao, Liang and Lu, Mingdong and Guo, Yifan and Xu, Bo}, journal={arXiv preprint arXiv:2507.03917}, year={2025} }
environment: python3.8 torch1.8.1+cu111
|
pipoiwoczz/dqn-SpaceInvadersNoFrameskip-v4
|
pipoiwoczz
| 2025-08-06T05:57:36Z | 29 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-06T05:56:59Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 753.50 +/- 299.27
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pipoiwoczz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pipoiwoczz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pipoiwoczz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Aria12138/cs5210-25su-finetuned-bio2box-merged
|
Aria12138
| 2025-08-06T05:53:46Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-06T05:34:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
antericinfo/qwen3mini
|
antericinfo
| 2025-08-06T05:53:37Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T05:44:03Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-0.6B-Base
---
# Qwen3-0.6B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-0.6B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 0.6B
- Number of Paramaters (Non-Embedding): 0.44B
- Number of Layers: 28
- Number of Attention Heads (GQA): 16 for Q and 8 for KV
- Context Length: 32,768
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
> [!TIP]
> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-0.6B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-0.6B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-0.6B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
moyixiao/Qwen3-0.6Bus-grpo120
|
moyixiao
| 2025-08-06T05:50:00Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:moyixiao/qwen3_grpo2_96",
"base_model:finetune:moyixiao/qwen3_grpo2_96",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T05:48:45Z |
---
base_model: moyixiao/qwen3_grpo2_96
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** moyixiao
- **License:** apache-2.0
- **Finetuned from model :** moyixiao/qwen3_grpo2_96
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
taengk/klue-ner-koelectra
|
taengk
| 2025-08-06T05:46:05Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T05:45:45Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
goosego/klue-ner-koelectra
|
goosego
| 2025-08-06T05:45:31Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T05:45:17Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
mjhwang/klue-ner-koelectra
|
mjhwang
| 2025-08-06T05:44:41Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"base_model:monologg/koelectra-base-v3-discriminator",
"base_model:finetune:monologg/koelectra-base-v3-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-06T05:44:19Z |
---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
model-index:
- name: klue-ner-koelectra
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. -->
# klue-ner-koelectra
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
ekiprop/SST-2-GLoRA-p30-seed63
|
ekiprop
| 2025-08-06T05:41:39Z | 16 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:roberta-base",
"lora",
"transformers",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"license:mit",
"region:us"
] | null | 2025-08-06T05:38:57Z |
---
library_name: peft
license: mit
base_model: roberta-base
tags:
- base_model:adapter:roberta-base
- lora
- transformers
metrics:
- accuracy
model-index:
- name: SST-2-GLoRA-p30-seed63
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. -->
# SST-2-GLoRA-p30-seed63
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2018
- Accuracy: 0.9335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3798 | 0.0950 | 200 | 0.2151 | 0.9186 |
| 0.2903 | 0.1900 | 400 | 0.1979 | 0.9220 |
| 0.2661 | 0.2850 | 600 | 0.1986 | 0.9197 |
| 0.2509 | 0.3800 | 800 | 0.2018 | 0.9335 |
| 0.2433 | 0.4751 | 1000 | 0.2158 | 0.9255 |
| 0.2302 | 0.5701 | 1200 | 0.2079 | 0.9266 |
| 0.229 | 0.6651 | 1400 | 0.1874 | 0.9289 |
| 0.2303 | 0.7601 | 1600 | 0.1907 | 0.9335 |
| 0.2275 | 0.8551 | 1800 | 0.1804 | 0.9335 |
| 0.2131 | 0.9501 | 2000 | 0.1898 | 0.9323 |
### Framework versions
- PEFT 0.16.0
- Transformers 4.54.1
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
|
crystalline7/464998
|
crystalline7
| 2025-08-06T05:37:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:37:09Z |
[View on Civ Archive](https://civitaiarchive.com/models/493667?modelVersionId=548839)
|
crystalline7/603053
|
crystalline7
| 2025-08-06T05:36:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:36:50Z |
[View on Civ Archive](https://civitaiarchive.com/models/493667?modelVersionId=688113)
|
crystalline7/98755
|
crystalline7
| 2025-08-06T05:34:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:34:14Z |
[View on Civ Archive](https://civitaiarchive.com/models/123950?modelVersionId=135238)
|
crystalline7/210364
|
crystalline7
| 2025-08-06T05:34:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:33:58Z |
[View on Civ Archive](https://civitaiarchive.com/models/239066?modelVersionId=269592)
|
crystalline7/16158
|
crystalline7
| 2025-08-06T05:33:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:33:49Z |
[View on Civ Archive](https://civitaiarchive.com/models/16167?modelVersionId=19338)
|
crystalline7/658712
|
crystalline7
| 2025-08-06T05:33:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:33:22Z |
[View on Civ Archive](https://civitaiarchive.com/models/658780?modelVersionId=744976)
|
crystalline7/1003639
|
crystalline7
| 2025-08-06T05:33:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:32:59Z |
[View on Civ Archive](https://civitaiarchive.com/models/920599?modelVersionId=1098611)
|
crystalline7/912631
|
crystalline7
| 2025-08-06T05:32:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-06T05:32:15Z |
[View on Civ Archive](https://civitaiarchive.com/models/899735?modelVersionId=1006719)
|
ekiprop/SST-2-GLoRA-p10-seed63
|
ekiprop
| 2025-08-06T05:31:56Z | 105 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:roberta-base",
"lora",
"transformers",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"license:mit",
"region:us"
] | null | 2025-08-05T19:24:39Z |
---
library_name: peft
license: mit
base_model: roberta-base
tags:
- base_model:adapter:roberta-base
- lora
- transformers
metrics:
- accuracy
model-index:
- name: SST-2-GLoRA-p10-seed63
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. -->
# SST-2-GLoRA-p10-seed63
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2071
- Accuracy: 0.9209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.444 | 0.0950 | 200 | 0.2878 | 0.8830 |
| 0.3355 | 0.1900 | 400 | 0.2433 | 0.8876 |
| 0.3157 | 0.2850 | 600 | 0.2395 | 0.9083 |
| 0.307 | 0.3800 | 800 | 0.2217 | 0.9163 |
| 0.2917 | 0.4751 | 1000 | 0.2409 | 0.9083 |
| 0.2866 | 0.5701 | 1200 | 0.2141 | 0.9186 |
| 0.2834 | 0.6651 | 1400 | 0.2161 | 0.9174 |
| 0.2853 | 0.7601 | 1600 | 0.2127 | 0.9186 |
| 0.2881 | 0.8551 | 1800 | 0.2071 | 0.9209 |
| 0.2744 | 0.9501 | 2000 | 0.2122 | 0.9209 |
### Framework versions
- PEFT 0.16.0
- Transformers 4.54.1
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
|
GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA
|
GyunYeop
| 2025-08-06T05:30:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:K-intelligence/Midm-2.0-Base-Instruct",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:K-intelligence/Midm-2.0-Base-Instruct",
"region:us"
] |
text-generation
| 2025-08-06T05:28:47Z |
---
base_model: K-intelligence/Midm-2.0-Base-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:K-intelligence/Midm-2.0-Base-Instruct
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
NexVeridian/gpt-oss-120b-6bit
|
NexVeridian
| 2025-08-06T05:28:37Z | 159 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-120b",
"base_model:quantized:openai/gpt-oss-120b",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-06T04:44:26Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-120b
---
# NexVeridian/gpt-oss-120b-6bit
This model [NexVeridian/gpt-oss-120b-6bit](https://huggingface.co/NexVeridian/gpt-oss-120b-6bit) was
converted to MLX format from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-120b-6bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
flymy-ai/qwen-image-lora
|
flymy-ai
| 2025-08-06T05:26:17Z | 0 | 2 | null |
[
"region:us"
] | null | 2025-08-06T04:50:47Z |
## LORA Qwen-Image example
World first lora for [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image)
Trigger word: **Valentin**
## ๐งช Usage
---
### ๐ง Initialization
```python
from diffusers import DiffusionPipeline
import torch
model_name = "Qwen/Qwen-Image"
# Load the pipeline
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
pipe = pipe.to(device)
```
### ๐ Load LoRA Weights
```python
# Load LoRA weights
pipe.load_lora_weights('pytorch_lora_weights.safetensors', adapter_name="lora")
```
### ๐จ Generate Image with lora trained on person
```python
prompt = '''Valentin in a natural daylight selfie at a cafe entrance. He looks seriously into the camera, wearing a black coat or jacket and wireless earbud. Background includes wooden frames, warm pendant lights, and urban cafe details. With text "FLYMY AI"'''
negative_prompt = " "
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
num_inference_steps=50,
true_cfg_scale=5,
generator=torch.Generator(device="cuda").manual_seed(346346)
)
# Display the image (in Jupyter or save to file)
image.show()
# or
image.save("output.png")
```
### ๐ผ๏ธ Sample Output
## ๐ค Support
If you have questions or suggestions, join our community:
- ๐ [FlyMy.AI](https://flymy.ai)
- ๐ฌ [Discord Community](https://discord.com/invite/t6hPBpSebw)
- ๐ฆ [Follow us on X](https://x.com/flymyai)
- ๐ผ [Connect on LinkedIn](https://linkedin.com/company/flymyai)
- ๐ง [Support](mailto:[email protected])
**โญ Don't forget to star the repository if you like it!**
---
license: apache-2.0
---
|
copper-light/test-model
|
copper-light
| 2025-08-06T05:24:15Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"test-model",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-08-05T10:25:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
igorktech/Custom
|
igorktech
| 2025-08-06T05:24:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"base_model:igorktech/gemma-3n-e2b-it-language-pruned-v2",
"base_model:finetune:igorktech/gemma-3n-e2b-it-language-pruned-v2",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T20:52:46Z |
---
base_model: igorktech/gemma-3n-e2b-it-language-pruned-v2
library_name: transformers
model_name: Custom
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Custom
This model is a fine-tuned version of [igorktech/gemma-3n-e2b-it-language-pruned-v2](https://huggingface.co/igorktech/gemma-3n-e2b-it-language-pruned-v2).
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="igorktech/Custom", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/igorktech01/huggingface/runs/qhc20kr6)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
yyyyyxie/textflux
|
yyyyyxie
| 2025-08-06T05:19:01Z | 526 | 8 |
diffusers
|
[
"diffusers",
"safetensors",
"scene-text-synthesis",
"multilingual",
"diffusion",
"dit",
"ocr-free",
"textflux",
"flux",
"text-to-image",
"arxiv:2505.17778",
"base_model:black-forest-labs/FLUX.1-Fill-dev",
"base_model:finetune:black-forest-labs/FLUX.1-Fill-dev",
"license:cc-by-nc-2.0",
"region:us"
] |
text-to-image
| 2025-04-21T10:21:25Z |
---
license: cc-by-nc-2.0 # ๆ่
ไฝ ้ๆฉ็่ฎธๅฏ่ฏ๏ผไพๅฆ mit, cc-by-sa-4.0 ็ญ
tags:
- scene-text-synthesis
- multilingual
- diffusion
- dit
- ocr-free
- textflux
- flux # ๅฆๆไฝ ็ๆจกๅๅบไบFLUX
# - text-to-image # ่ฟๆฏไธไธช้็จ็่ฎก็ฎๆบ่ง่งๆ ็ญพ
# - generated_image_text # ๆดๅ
ทไฝ็ๆ ็ญพ
library_name: diffusers # ๅ ไธบไฝ ๆๅฐไบ Diffusers
pipeline_tag: text-to-image # ๆ่
ๆดๅ
ทไฝ็ไปปๅกๆ ็ญพ
base_model:
- black-forest-labs/FLUX.1-Fill-dev
# datasets: # ๅฆๆไฝ ๆฟๆ๏ผๅฏไปฅๅๅบไธป่ฆ็่ฎญ็ปๆฐๆฎ้๏ผๅณไฝฟๅฎไปฌๅฐๆชๅ
ฌๅผๅๅธ
# - your-custom-training-dataset-name
# metrics: # ๅฆๆไฝ ๆ่ฏไผฐๆๆ
# - fid
# - ocr_accuracy
# model-index: # ่ฟ้จๅๅธฎๅฉHugging Faceๆดๅฅฝๅฐ็ดขๅผๆจกๅๅๅ
ถ็ปๆ
# - name: TextFlux # ไฝ ็ๆจกๅๅ็งฐ
# results:
# - task:
# type: text-to-image # ไปปๅก็ฑปๅ
# name: Scene Text Synthesis # ไปปๅก็ๅ
ทไฝๅ็งฐ
# dataset: # ่ฏไผฐ็จ็ๆฐๆฎ้
# name: your-evaluation-dataset
# type: scene_text_images
# metrics: # ่ฏไผฐๆๆ
# - name: OCR Accuracy
# value: 90.5 # ไธพไพ
# type: ocr_accuracy
# - name: FID
# value: 30.2 # ไธพไพ
# type: fid
---
# TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis
<div style="display: flex; justify-content: center; align-items: center;">
<a href="https://arxiv.org/abs/2505.17778">
<img src='https://img.shields.io/badge/arXiv-2505.17778-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
</a>
<a href='https://huggingface.co/yyyyyxie/textflux'>
<img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
</a>
<a href="https://github.com/yyyyyxie/textflux">
<img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
</a>
<a href="https://huggingface.co/yyyyyxie/textflux" style="margin: 0 2px;">
<img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
</a>
<a href='https://yyyyyxie.github.io/textflux-site/'>
<img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
</a>
<a href="https://huggingface.co/yyyyyxie/textflux">
<img src="https://img.shields.io/badge/๐ค_HuggingFace-Dataset-ffbd45.svg" alt="HuggingFace">
</a>
</div>
**TextFlux** is an **OCR-free framework** using a Diffusion Transformer (DiT, based on [FLUX.1-Fill-dev](https://github.com/black-forest-labs/flux)) for high-fidelity multilingual scene text synthesis. It simplifies the learning task by providing direct visual glyph guidance through spatial concatenation of rendered glyphs with the scene image, enabling the model to focus on contextual reasoning and visual fusion.
## Key Features
* **OCR-Free:** Simplified architecture without OCR encoders.
* **High-Fidelity & Contextual Styles:** Precise rendering, stylistically consistent with scenes.
* **Multilingual & Low-Resource:** Strong performance across languages, adapts to new languages with minimal data (e.g., <1,000 samples).
* **Zero-Shot Generalization:** Renders characters unseen during training.
* **Controllable Multi-Line Text:** Flexible multi-line synthesis with line-level control.
* **Data Efficient:** Uses a fraction of data (e.g., ~1%) compared to other methods.
<div align="center">
<img src="https://image-transfer-season.oss-cn-qingdao.aliyuncs.com/pictures/abstract_fig.png" width="100%" height="100%"/>
</div>
## Updates
- **`2025/05/27`**: Our [**Full-Param Weights**](https://huggingface.co/yyyyyxie/textflux) and [**LoRA Weights**](https://huggingface.co/yyyyyxie/textflux-lora) are now available ๐ค!
- **`2025/05/25`**: Our [**Paper on ArXiv**](https://arxiv.org/abs/2505.17778) is available ๐ฅณ!
## Setup
1. **Clone/Download:** Get the necessary code and model weights.
2. **Dependencies:**
```bash
conda create -n textflux python==3.11.4 -y
conda activate textflux
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
# Ensure diffusers >= 0.32.1
```
## Gradio Demo
Provides "Normal Mode" (for pre-combined inputs) and "Custom Mode" (upload scene, draw masks, input text for automatic template generation and concatenation).
```bash
python demo.py
```
## Acknowledgement
Our code is modified based on [Diffusers](https://github.com/huggingface/diffusers). We adopt [black-forest-labs/FLUX.1-Fill-dev](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev) as the base model. Thanks to all the contributors for the helpful discussions!
## License
The use of this model, TextFlux, is governed by the **FLUX.1 [dev] Non-Commercial License Agreement** (or the specific version applicable to FLUX.1-Fill-dev, upon which TextFlux is based).
## Citation
```bibtex
@misc{xie2025textfluxocrfreeditmodel,
title={TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis},
author={Yu Xie and Jielei Zhang and Pengyu Chen and Ziyue Wang and Weihang Wang and Longwen Gao and Peiyi Li and Huyang Sun and Qiang Zhang and Qian Qiao and Jiaqing Fan and Zhouhui Lian},
year={2025},
eprint={2505.17778},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.17778},
}
```
|
Aria12138/cs5210-25su-finetuned-bio2box-lora
|
Aria12138
| 2025-08-06T05:14:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T05:14:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
ecamli/Qwen3-0.6B-Gensyn-Swarm-quiet_skittish_orangutan
|
ecamli
| 2025-08-06T05:11:07Z | 100 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am quiet_skittish_orangutan",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-27T12:33:08Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am quiet_skittish_orangutan
---
# 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 ๐ค 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]
|
fla-hub/rwkv7-0.1B-g1
|
fla-hub
| 2025-08-06T05:11:00Z | 42 | 6 |
transformers
|
[
"transformers",
"safetensors",
"rwkv7",
"text-generation",
"conversational",
"custom_code",
"en",
"zh",
"ja",
"ko",
"fr",
"ar",
"es",
"pt",
"arxiv:2503.14456",
"base_model:BlinkDL/rwkv7-g1",
"base_model:finetune:BlinkDL/rwkv7-g1",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-03-10T06:10:54Z |
---
base_model:
- BlinkDL/rwkv7-g1
language:
- en
- zh
- ja
- ko
- fr
- ar
- es
- pt
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
library_name: transformers
---
# rwkv7-0.1B-g1
<!-- Provide a quick summary of what the model is/does. -->
This is RWKV-7 g1 model under flash-linear attention format. The `g1` model series added significant more data and incorporated deep thinking abilities.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Bo Peng, Yu Zhang, Songlin Yang, Ruichong Zhang
- **Funded by:** RWKV Project (Under LF AI & Data Foundation)
- **Model type:** RWKV7
- **Language(s) (NLP):** Multilingal
- **License:** Apache-2.0
- **Parameter count:** 191M
- **Tokenizer:** RWKV World tokenizer
- **Vocabulary size:** 65,536
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
- **Paper:** https://arxiv.org/abs/2503.14456
## 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. -->
Install `flash-linear-attention` and the latest version of `transformers` before using this model:
```bash
pip install git+https://github.com/fla-org/flash-linear-attention
pip install 'transformers>=4.48.0'
```
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
You can use this model just as any other HuggingFace models:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-0.1B-g1', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-0.1B-g1', trust_remote_code=True)
model = model.cuda() # Supported on Nvidia/AMD/Intel eg. model.xpu()
prompt = "What is a large language model?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Default is True, set to False to disable thinking
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024,
do_sample=True,
temperature=1.0,
top_p=0.3,
repetition_penalty=1.2
)
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=False)[0]
print(response)
```
### Training Data
This model is trained on the World v3.5 with a total of more than 5 trillion tokens.
## FAQ
Q: safetensors metadata is none.
A: upgrade transformers to >=4.48.0: `pip install 'transformers>=4.48.0'`
## Thinking Prompt
```
<|rwkv_tokenizer_end_of_text|>User: <Your Question Here>
Assistant: <think
```
Don't close the brackets for `<think`!
## Addidtional Caveats for Prompting
**Always add `<|rwkv_tokenizer_end_of_text|>` (Token ID = 0) before your prompt. The model is incapable of attending the first token it receives due to state initialization issues.**
Bad prompt example:
```
Mathews lifted a dark brow. "Are you sure about that? I mean, wouldn't it be better to wait until Dale is home safe and sound?"
"The longer I wait to tell her, the worse it will be for both of us."
"Good luck. You're going to need it," said
```
The model is unable to recall ` Mathews` because it is the very first token of the input.
Good prompt example:
```
<|rwkv_tokenizer_end_of_text|>Mathews lifted a dark brow. "Are you sure about that? I mean, wouldn't it be better to wait until Dale is home safe and sound?"
"The longer I wait to tell her, the worse it will be for both of us."
"Good luck. You're going to need it," said
```
the model will output ` Mathews` as expected.
Without this token: **`lambada_openai ppl=13.84 acc=48.13%`**
With this token added: **`lambada_openai ppl=12.36 acc=49.12%`**
Note: this phenomenon is very rare for Transformers but significant for RNNs. We speculate that the model uses the first token to pin the states, to better acquire information from later tokens.
|
econ6/navv2_2
|
econ6
| 2025-08-06T05:02:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-06T11:40:33Z |
---
license: apache-2.0
---
|
AlekseyCalvin/QWEN_IMAGE_nf4_w_AbliteratedTE_Diffusers
|
AlekseyCalvin
| 2025-08-06T05:01:51Z | 175 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"nf4",
"Abliterated",
"Qwen2.5-VL7b-Abliterated",
"instruct",
"Diffusers",
"Transformers",
"uncensored",
"text-to-image",
"image-to-image",
"image-generation",
"en",
"zh",
"base_model:Qwen/Qwen-Image",
"base_model:quantized:Qwen/Qwen-Image",
"license:apache-2.0",
"diffusers:QwenImagePipeline",
"region:us"
] |
text-to-image
| 2025-08-05T12:00:18Z |
---
library_name: diffusers
base_model: Qwen/Qwen-Image
base_model_relation: quantized
quantized_by: AlekseyCalvin
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-to-image
tags:
- nf4
- Abliterated
- Qwen2.5-VL7b-Abliterated
- instruct
- Diffusers
- Transformers
- uncensored
- text-to-image
- image-to-image
- image-generation
---
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" width="200"/>
<p>
# QWEN-IMAGE Model |nf4|+Abliterated Qwen2.5VL-7b
This repo contains a variant of QWEN's **[QWEN-IMAGE](https://huggingface.co/Qwen/Qwen-Image)**, the state-of-the-art generative model with extensive and (image/)text-to-image &/or instruction/control-editing capabilities. <br>
To make these cutting edge capabilities more accessible to those constrained to low-end consumer-grade hardware, **we've quantized the DiT (Diffusion Transformer) component of Qwen-Image to the 4-bit NF4 format** using the Bits&Bytes toolkit.<br>
This optimization was derived by us directly from the BF16 base model weights released on 08/04/2025, with no other mix-ins or modifications to the DiT component. <br>
*NOTE: Install `bitsandbytes` prior to inference.* <br>
**QWEN-IMAGE** is an open-weights customization-friendly frontier model released under the highly permissive Apache 2.0 license, welcoming unrestricted (within legal limits) commercial, experimental, artistic, academic, and other uses &/or modifications. <br>
To help highlight horizons of possibility broadened by the **QWEN-IMAGE** release, our quantization is bundled with an "Abliterated" (aka de-censored) finetune of [Qwen2.5-VL 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), QWEN-IMAGE model's sole conditioning encoder (of prompts, instructions, input images, controls, etc), as well as a powerful Vision-Language-Model in its own right. <br>
As such, our repo saddles a lean & prim NF4 DiT over the **[Qwen2.5-VL-7B-Abliterated-Caption-it](https://huggingface.co/prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it/tree/main)** by [Prithiv Sakthi](https://huggingface.co/prithivMLmods) (aka [prithivMLmods](https://github.com/prithivsakthiur)).
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.jpg" width="1600"/>
<p>
# NOTICE:
*Do not be alarmed by the file warning from the ClamAV automated checker.* <br>
*It is a clear false positive.* *In assessing one of the typical Diffusers-adapted Safetensors shards (model weights), the checker reads:*
``The following viruses have been found: Pickle.Malware.SysAccess.sys.STACK_GLOBAL.UNOFFICIAL`` <br>
*However, a Safetensors by its sheer design can not contain suchlike inserts. You may confirm for yourself thru HF's built-in weight/index viewer. <br>
So, to be sure, this repo does **not** contain any pickle checkpoints, or any other pickled data.* <br>
# TEXT-TO-IMAGE PIPELINE EXAMPLE:
This repo is formatted for usage with Diffusers (0.35.0.dev0+) & Transformers libraries, vis-a-vis associated pipelines & model component classes, such as the defaults listed in `model_index.json` (in this repo's root folder). <br>
*Sourced/adapted from [the original base model repo](https://huggingface.co/Qwen/Qwen-Image) by QWEN.*
**EDIT:
We've confronted some issues with using the below pipeline. Will update once reliable adjustments are confirmed.** <br>
```python
from diffusers import DiffusionPipeline
import torch
import bitsandbytes
model_name = "AlekseyCalvin/QwenImage_nf4"
# Load the pipeline
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
pipe = pipe.to(device)
positive_magic = [
"en": "Ultra HD, 4K, cinematic composition." # for english prompt,
"zh": "่ถ
ๆธ
๏ผ4K๏ผ็ตๅฝฑ็บงๆๅพ" # for chinese prompt,
]
# Generate image
prompt = '''A coffee shop entrance features a chalkboard sign reading "Qwen Coffee ๐ $2 per cup," with a neon light beside it displaying "้ไนๅ้ฎ". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "ฯโ3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition'''
negative_prompt = " "
# Generate with different aspect ratios
aspect_ratios = {
"1:1": (1328, 1328),
"16:9": (1664, 928),
"9:16": (928, 1664),
"4:3": (1472, 1140),
"3:4": (1140, 1472)
}
width, height = aspect_ratios["16:9"]
image = pipe(
prompt=prompt + positive_magic["en"],
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cuda").manual_seed(42)
).images[0]
image.save("example.png")
```
<br>
# SHOWCASES FROM THE QWEN TEAM:
# MORE INFO:
- Check out the [Technical Report](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf) for QWEN-IMAGE, released by the Qwen team! <br>
- Find source base model weights here at [huggingface](https://huggingface.co/Qwen/Qwen-Image) and at [Modelscope](https://modelscope.cn/models/Qwen/Qwen-Image).
## QWEN LINKS:
<p align="center">
๐ <a href="https://chat.qwen.ai/"><b>Qwen Chat</b></a>   |   ๐ค <a href="https://huggingface.co/Qwen/Qwen-Image">Hugging Face</a>   |   ๐ค <a href="https://modelscope.cn/models/Qwen/Qwen-Image">ModelScope</a>   |    ๐ <a href="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf">Tech Report</a>    |    ๐ <a href="https://qwenlm.github.io/blog/qwen-image/">Blog</a>   
<br>
๐ฅ๏ธ <a href="https://huggingface.co/spaces/Qwen/qwen-image">Demo</a>   |   ๐ฌ <a href="https://github.com/QwenLM/Qwen-Image/blob/main/assets/wechat.png">WeChat (ๅพฎไฟก)</a>   |   ๐ซจ <a href="https://discord.gg/CV4E9rpNSD">Discord</a>  
</p>
## QWEN-IMAGE TECHNICAL REPORT CITATION:
```bibtex
@article{qwen-image,
title={Qwen-Image Technical Report},
author={Qwen Team},
journal={arXiv preprint},
year={2025}
}
```
|
mradermacher/aquif-3-mini-GGUF
|
mradermacher
| 2025-08-06T04:48:19Z | 193 | 0 |
transformers
|
[
"transformers",
"gguf",
"language",
"aquif",
"text-generation-inference",
"math",
"coding",
"small",
"pt",
"en",
"ja",
"zh",
"th",
"es",
"hi",
"fr",
"de",
"it",
"base_model:aquiffoo/aquif-3-mini",
"base_model:quantized:aquiffoo/aquif-3-mini",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-05T22:31:50Z |
---
base_model: aquiffoo/aquif-3-mini
language:
- pt
- en
- ja
- zh
- th
- es
- hi
- fr
- de
- it
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- language
- aquif
- text-generation-inference
- math
- coding
- small
---
## 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/aquiffoo/aquif-3-mini
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#aquif-3-mini-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/aquif-3-mini-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/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-GGUF/resolve/main/aquif-3-mini.f16.gguf) | f16 | 6.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
johnpaulbin/yt1
|
johnpaulbin
| 2025-08-06T04:44:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3n",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T04:44:13Z |
---
base_model: unsloth/gemma-3n-e4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** johnpaulbin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e4b-unsloth-bnb-4bit
This gemma3n 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)
|
Mitchins/sd15-onnx-int8
|
Mitchins
| 2025-08-06T04:39:38Z | 0 | 0 | null |
[
"onnx",
"stable-diffusion",
"text-to-image",
"cpu-optimized",
"raspberry-pi",
"quantized",
"int8",
"en",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:quantized:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-08-06T03:13:16Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- onnx
- text-to-image
- cpu-optimized
- raspberry-pi
- quantized
- int8
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
inference: true
---
# Stable Diffusion 1.5 ONNX INT8 CPU-Optimized
Fast INT8 quantized ONNX version of Stable Diffusion 1.5 optimized for CPU inference. Perfect for resource-constrained environments and fast generation.
## Model Details
- **Base Model**: [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)
- **Format**: ONNX with INT8 quantization
- **Precision**: INT8 (2x faster than FP16)
- **Target**: CPU inference (Intel/AMD/ARM)
- **Provider**: CPUExecutionProvider
## Key Features
๐ **2x Faster** - INT8 quantization provides significant speedup
๐พ **Lower Memory** - ~2GB RAM vs 2.5GB for FP16
๐ **Raspberry Pi Optimized** - Great for RPi projects
โก **Quick Generation** - Faster iterations
๐ฆ **Ultra Lightweight** - Minimal dependencies
## Quick Start
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
# Load quantized ONNX model
pipe = ORTStableDiffusionPipeline.from_pretrained(
"Mitchins/sd15-onnx-int8",
provider="CPUExecutionProvider"
)
# Generate image (use slightly more steps for INT8)
image = pipe(
"A cyberpunk cityscape at night, neon lights, detailed",
num_inference_steps=25, # +5 steps for INT8
guidance_scale=7.5
).images[0]
image.save("output.png")
```
## Installation
```bash
# Same lightweight setup
pip install optimum[onnxruntime] pillow
# Raspberry Pi
sudo apt install python3-pip
pip3 install optimum[onnxruntime] pillow
```
## Performance
| Hardware | Time (512x512) | Memory | vs FP16 |
|----------|----------------|--------|---------|
| RPi 4 (4GB) | ~1.5-3 min | ~2GB | ~2x faster |
| Intel i5 | ~20-45s | ~2GB | ~2x faster |
| M1 Mac | ~15-25s | ~2GB | ~2x faster |
| AMD Ryzen | ~20-45s | ~2GB | ~2x faster |
## Optimized Settings
```python
# Recommended settings for INT8
image = pipe(
prompt,
num_inference_steps=25, # Few more steps for quality
guidance_scale=8.0, # Slightly higher guidance
height=512, width=512 # Native resolution
).images[0]
# For fastest generation
image = pipe(
prompt,
height=256, width=256, # Quarter resolution
num_inference_steps=15, # Minimum steps
guidance_scale=7.0
).images[0]
```
## Quality vs Speed Trade-offs
| Steps | Quality | Speed | Use Case |
|-------|---------|-------|----------|
| 15 | Good | Fastest | Testing, previews |
| 20 | Very Good | Fast | Most use cases |
| 25 | Excellent | Medium | Best results |
| 30+ | Excellent+ | Slower | High quality needs |
## When to Use INT8
โ
**Choose INT8 for:**
- Raspberry Pi projects
- Fast iteration/testing
- Resource-constrained environments
- Batch generation
- Real-time applications
โ **Consider FP16 for:**
- Best possible quality
- Single high-quality images
- Professional use cases
## Raspberry Pi Tips
```bash
# Increase swap for large models
sudo dphys-swapfile swapoff
sudo nano /etc/dphys-swapfile # Set CONF_SWAPSIZE=2048
sudo dphys-swapfile setup
sudo dphys-swapfile swapon
# Monitor temperature
vcgencmd measure_temp
# Generate smaller images first
python3 -c "
pipe = ORTStableDiffusionPipeline.from_pretrained('.')
img = pipe('test', height=128, width=128, num_inference_steps=10).images[0]
img.save('test.png')
"
```
Perfect for edge AI and fast CPU inference!
## License
CreativeML Open RAIL-M (inherited from Stable Diffusion 1.5)
|
EVER-Z/Changen2-ChangeStar1x256
|
EVER-Z
| 2025-08-06T04:38:11Z | 88 | 0 | null |
[
"change-detection",
"remote-sensing",
"satellite",
"synthetic-data-pretraining",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2024-10-17T21:08:26Z |
---
license: cc-by-nc-sa-4.0
tags:
- change-detection
- remote-sensing
- satellite
- synthetic-data-pretraining
---
# Model Card for Changen2 pre-trained ChangeStar 1x256 models
<!-- Provide a quick summary of what the model is/does. -->
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Z-Zheng/pytorch-change-models
- **Paper:** https://ieeexplore.ieee.org/document/10713915
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```text
@article{zheng2024changen2,
author={Zheng, Zhuo and Ermon, Stefano and Kim, Dongjun and Zhang, Liangpei and Zhong, Yanfei},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model},
year={2024},
volume={},
number={},
pages={1-17},
doi={10.1109/TPAMI.2024.3475824}
}
```
|
youngha765/park
|
youngha765
| 2025-08-06T04:34:53Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-06T04:34:52Z |
---
license: apache-2.0
---
|
Enfysyz/JurisPrae
|
Enfysyz
| 2025-08-06T04:29:19Z | 88 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"llama-3",
"law",
"civil-law",
"legal-advice",
"ollama",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-07-25T20:11:57Z |
---
license: mit
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation
- llama-3
- law
- civil-law
- legal-advice
- gguf
- ollama
---
# JurisPrae: Your AI Civil Law Assistant
**JurisPrae** is a fine-tuned, 4-bit quantized version of the `meta-llama/Meta-Llama-3-8B-Instruct` model. It has been specifically trained to understand and respond to real-world questions about civil law, making legal information more accessible and understandable.
This repository contains the GGUF version of the model, suitable for running locally with tools like Ollama.
## ๐ฌ Showcase
Check out this video to see JurisPrae in action:
<video controls width="100%">
<source src="https://huggingface.co/Enfysyz/JurisPrae/resolve/main/JurisPrae_sample.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
## Running with Ollama
To run this model locally using [Ollama](https://ollama.com/ "null"), follow these steps:
1. **Download the GGUF file** from the "Files and versions" tab of this repository.
2. **Create a `Modelfile`** in the same directory as the downloaded GGUF file. Copy the following content into it:
```
# Replace with the actual name of your GGUF file
FROM ./JurisPrae-8B-Instruct-Q4_K_M.gguf
TEMPLATE """{{- if .System }}
<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>
{{- end }}
<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""
SYSTEM """You are a legal expert. Provide accurate, well-reasoned legal insights using proper legal terminology. Maintain a professional, objective tone. Be specific about which laws or legal principles apply. Explain the person's rights, cite the relevant statute(s), and give a clear legal opinion. When unsure, advise consulting a qualified attorney."""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
```
3. **Create the model in Ollama** by running the following command in your terminal:
```
ollama create JurisPrae -f ./Modelfile
```
4. **Run the model** and start chatting:
```
ollama run JurisPrae
```
## Model Details
### Model Description
JurisPrae is a state-of-the-art chatbot designed to provide information and answer questions related to civil law. It aims to democratize legal knowledge for students, legal professionals, and anyone curious about their rights and obligations.
- **Model type:** Causal language model (decoder-only)
- **Base Model:** `meta-llama/Meta-Llama-3-8B-Instruct`
- **Quantization:** The model has been quantized to 4-bit precision (GGUF), allowing for efficient performance on consumer hardware.
- **Fine-tuning Data:** See the "Training Data" section below.
## Uses
JurisPrae is intended for educational and informational purposes.
- **Natural Language Understanding:** Ask questions in plain English, just as you would on a forum like Reddit, and get clear, concise answers.
- **Legal Concept Explanations:** Break down complex legal jargon and concepts into easy-to-understand language.
- **Topic Exploration:** Learn about various civil law topics, including contracts, torts, property law, and family law.
- **Case Law Summaries:** Get high-level summaries of important legal cases to understand how the law has been applied.
## Training Data
The base model was fine-tuned on a curated dataset constructed from posts and comments from the **`r/legal_advice` subreddit**. This dataset was chosen to provide the model with a strong understanding of how non-lawyers formulate legal questions and the kinds of real-world issues they face. The data was carefully cleaned and formatted into a conversational instruction format.
## Limitations and Bias
- **Not a Lawyer:** JurisPrae is not a lawyer and its responses do not constitute legal advice. It is a tool for information, not a substitute for professional legal counsel.
- **Potential for Inaccuracies:** The model was trained on public forum data from `r/legal_advice`, which may contain inaccuracies. The model may therefore generate responses that are incorrect, incomplete, or outdated.
- **Jurisdictional Nuances:** The model may not capture the specific nuances of local or regional laws. Civil law can vary significantly between jurisdictions.
- **No Knowledge of Recent Events:** The model's knowledge is limited to its training data and it will not be aware of very recent legal developments or court rulings.
## โ ๏ธ Disclaimer
**JurisPrae does not provide legal advice.** The information provided by this chatbot is for educational purposes only. Always consult with a qualified attorney for advice on your specific situation. We are not liable for any actions taken based on the information provided by JurisPrae.
|
nightmedia/gpt-oss-20b-q6-hi-mlx
|
nightmedia
| 2025-08-06T04:26:24Z | 206 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-06T02:24:25Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-20b
---
# gpt-oss-20b-q6-hi-mlx
This model [gpt-oss-20b-q6-hi-mlx](https://huggingface.co/gpt-oss-20b-q6-hi-mlx) was
converted to MLX format from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("gpt-oss-20b-q6-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
stellalisy/system_select_dpo-1b-lr1e-6-b0.1
|
stellalisy
| 2025-08-06T04:15:51Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T04:14:25Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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 ๐ค 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]
|
GeneroGral/Mistral-Nemo-12B_BBQ_Stereo6_dropout_batch
|
GeneroGral
| 2025-08-06T04:14:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:finetune:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-07-27T13:09:01Z |
---
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GeneroGral
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Base-2407
This mistral 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)
|
stellalisy/system_select_dpo-1b-lr1e-6-b0.0
|
stellalisy
| 2025-08-06T04:13:24Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T04:12:27Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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 ๐ค 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]
|
Bearrr310/Qwen2.5-1.5B-Instruct-SFT
|
Bearrr310
| 2025-08-06T04:12:27Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"dataset:sft_verl_0806nlr",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-04T08:54:39Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: sft_verl_0806nlr
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-SFT
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-SFT
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [sft_verl_0806nlr](https://huggingface.co/datasets/sft_verl_0806nlr) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Bearrr310/Qwen2.5-1.5B-Instruct-SFT", 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: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
EZCon/gemma-3-4b-it-4bit-mlx
|
EZCon
| 2025-08-06T04:01:54Z | 67 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
"mlx",
"conversational",
"base_model:google/gemma-3-4b-it",
"base_model:quantized:google/gemma-3-4b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-08-03T14:56:05Z |
---
tags:
- unsloth
- mlx
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, youโre required to review and
agree to Googleโs usage license. To do this, please ensure youโre logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model:
- google/gemma-3-4b-it
---
# EZCon/gemma-3-4b-it-4bit-mlx
This model was converted to MLX format from [`unsloth/gemma-3-4b-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3-4b-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3-4b-it-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Nerva1228/chuandaqi
|
Nerva1228
| 2025-08-06T03:59:32Z | 7 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-06T03:59:31Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: chuandaqi
---
# Chuandaqi
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `chuandaqi` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "chuandaqi",
"lora_weights": "https://huggingface.co/Nerva1228/chuandaqi/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Nerva1228/chuandaqi', weight_name='lora.safetensors')
image = pipeline('chuandaqi').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 5e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Nerva1228/chuandaqi/discussions) to add images that show off what youโve made with this LoRA.
|
EZCon/Qwen2-VL-2B-Instruct-8bit-mlx
|
EZCon
| 2025-08-06T03:58:11Z | 45 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"multimodal",
"qwen",
"qwen2",
"unsloth",
"vision",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:quantized:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
image-text-to-text
| 2025-08-01T01:56:25Z |
---
base_model: Qwen/Qwen2-VL-2B-Instruct
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- multimodal
- qwen
- qwen2
- unsloth
- transformers
- vision
- mlx
---
# EZCon/Qwen2-VL-2B-Instruct-8bit-mlx
This model was converted to MLX format from [`unsloth/Qwen2-VL-2B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2-VL-2B-Instruct-8bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
quyanh/Qwen2-7B-Instruct-Unlearn
|
quyanh
| 2025-08-06T03:56:50Z | 261 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-16T12:03:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
EZCon/Qwen2.5-VL-3B-Instruct-abliterated-4bit-mlx
|
EZCon
| 2025-08-06T03:55:45Z | 69 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"multimodal",
"abliterated",
"uncensored",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-05-15T02:27:51Z |
---
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- abliterated
- uncensored
- mlx
library_name: transformers
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---
# EZCon/Qwen2.5-VL-3B-Instruct-abliterated-4bit-mlx
This model was converted to MLX format from [`huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2.5-VL-3B-Instruct-abliterated-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
leoikaichen/clip-vit-base-patch32
|
leoikaichen
| 2025-08-06T03:53:39Z | 4 | 0 | null |
[
"pytorch",
"tf",
"jax",
"clip",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"region:us"
] | null | 2025-08-06T03:48:02Z |
---
tags:
- vision
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context theyโre being deployed within.
### Model Date
January 2021
### Model Type
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
### Use with Transformers
```python3
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIPโs performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Performance and Limitations
### Performance
We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
- Food101
- CIFAR10
- CIFAR100
- Birdsnap
- SUN397
- Stanford Cars
- FGVC Aircraft
- VOC2007
- DTD
- Oxford-IIIT Pet dataset
- Caltech101
- Flowers102
- MNIST
- SVHN
- IIIT5K
- Hateful Memes
- SST-2
- UCF101
- Kinetics700
- Country211
- CLEVR Counting
- KITTI Distance
- STL-10
- RareAct
- Flickr30
- MSCOCO
- ImageNet
- ImageNet-A
- ImageNet-R
- ImageNet Sketch
- ObjectNet (ImageNet Overlap)
- Youtube-BB
- ImageNet-Vid
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with โMiddle Easternโ having the highest accuracy (98.4%) and โWhiteโ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
## Feedback
### Where to send questions or comments about the model
Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)
|
EZCon/SmolVLM2-500M-Video-Instruct-mlx
|
EZCon
| 2025-08-06T03:53:21Z | 36 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smolvlm",
"image-text-to-text",
"mlx",
"conversational",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:lmms-lab/M4-Instruct-Data",
"dataset:HuggingFaceFV/finevideo",
"dataset:MAmmoTH-VL/MAmmoTH-VL-Instruct-12M",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:orrzohar/Video-STaR",
"dataset:Mutonix/Vript",
"dataset:TIGER-Lab/VISTA-400K",
"dataset:Enxin/MovieChat-1K_train",
"dataset:ShareGPT4Video/ShareGPT4Video",
"base_model:HuggingFaceTB/SmolVLM-500M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-500M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-01T17:50:26Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
pipeline_tag: image-text-to-text
language:
- en
base_model:
- HuggingFaceTB/SmolVLM-500M-Instruct
tags:
- mlx
---
# EZCon/SmolVLM2-500M-Video-Instruct-mlx
This model was converted to MLX format from [`HuggingFaceTB/SmolVLM2-500M-Video-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/SmolVLM2-500M-Video-Instruct-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
hyper-accel/ci-random-gpt2-350m
|
hyper-accel
| 2025-08-06T03:50:19Z | 80 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-29T12:44:19Z |
---
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 ๐ค 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]
|
hdong0/Qwen2.5-Math-1.5B-untied-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_simple
|
hdong0
| 2025-08-06T03:49:31Z | 131 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:hdong0/Qwen2.5-Math-1.5B-untied",
"base_model:finetune:hdong0/Qwen2.5-Math-1.5B-untied",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-04T23:26:37Z |
---
base_model: hdong0/Qwen2.5-Math-1.5B-untied
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: Qwen2.5-Math-1.5B-untied-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_simple
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-Math-1.5B-untied-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_simple
This model is a fine-tuned version of [hdong0/Qwen2.5-Math-1.5B-untied](https://huggingface.co/hdong0/Qwen2.5-Math-1.5B-untied) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hdong0/Qwen2.5-Math-1.5B-untied-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_simple", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jaimefrevoltio/act_t2_picktobox_v2_onearm_s101
|
jaimefrevoltio
| 2025-08-06T03:47:20Z | 2 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:jaimefrevoltio/picktobox_v2_onearm_s101_base",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-06T03:47:13Z |
---
datasets: jaimefrevoltio/picktobox_v2_onearm_s101_base
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Nadhari/gemma-3n-swahili-E2B-it
|
Nadhari
| 2025-08-06T03:44:54Z | 84 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-06T03:02:54Z |
---
base_model: unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Alfaxad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
This gemma3n 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)
|
arianaazarbal/underspecified_hacker_3_iters_neutral_1
|
arianaazarbal
| 2025-08-06T03:42:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T08:46:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
legion1581/so101_test
|
legion1581
| 2025-08-06T03:40:03Z | 38 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:legion1581/record-test",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-06T03:39:36Z |
---
datasets: legion1581/record-test
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
pipoiwoczz/Taxi-V3
|
pipoiwoczz
| 2025-08-06T03:36:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-06T03:36:24Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-V3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pipoiwoczz/Taxi-V3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx
|
EZCon
| 2025-08-06T03:35:16Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"chat",
"abliterated",
"uncensored",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-06T03:33:22Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2-VL-2B-Instruct
tags:
- chat
- abliterated
- uncensored
- mlx
---
# EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx
This model was converted to MLX format from [`huihui-ai/Qwen2-VL-2B-Instruct-abliterated`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
VahidNaghashi/Vahid-human
|
VahidNaghashi
| 2025-08-06T03:34:54Z | 6 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-06T03:31:57Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of Vahid human in Iran
---
# DreamBooth model for the Vahid concept trained by VahidNaghashi on the vahid/myself dataset.
This is a Stable Diffusion model fine-tuned on the Vahid concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Vahid human**
This model was created as part of the DreamBooth Hackathon ๐ฅ. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `human` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('VahidNaghashi/Vahid-human')
image = pipeline().images[0]
image
```
|
GeerBox/ppo-SnowballTarget
|
GeerBox
| 2025-08-06T03:34:51Z | 135 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-08-06T03:01:27Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: GeerBox/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
winnieyangwannan/all_Llama-3.1-8B-Instruct_mlp_pnas_layer_22_4_all_37_0.0001_10_0
|
winnieyangwannan
| 2025-08-06T03:34:51Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T03:32:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
EZCon/gemma-3n-E2B-it-8bit-mlx
|
EZCon
| 2025-08-06T03:32:53Z | 22 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"gemma3",
"unsloth",
"gemma",
"google",
"mlx",
"conversational",
"en",
"base_model:google/gemma-3n-E2B-it",
"base_model:quantized:google/gemma-3n-E2B-it",
"license:gemma",
"endpoints_compatible",
"8-bit",
"region:us"
] |
image-text-to-text
| 2025-08-05T07:49:42Z |
---
base_model: google/gemma-3n-E2B-it
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
license: gemma
tags:
- gemma3
- unsloth
- transformers
- gemma
- google
- mlx
---
# EZCon/gemma-3n-E2B-it-8bit-mlx
This model was converted to MLX format from [`unsloth/gemma-3n-E2B-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3n-E2B-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3n-E2B-it-8bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Mitchins/sd15-onnx-fp16
|
Mitchins
| 2025-08-06T03:28:09Z | 0 | 0 | null |
[
"onnx",
"stable-diffusion",
"text-to-image",
"cpu-optimized",
"raspberry-pi",
"fp16",
"en",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:quantized:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-08-06T03:13:03Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- onnx
- text-to-image
- cpu-optimized
- raspberry-pi
- fp16
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
inference: true
---
# Stable Diffusion 1.5 ONNX FP16 CPU-Optimized
High-quality ONNX version of Stable Diffusion 1.5 optimized for CPU inference with FP16 precision. Perfect for edge deployment including Raspberry Pi.
## Model Details
- **Base Model**: [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)
- **Format**: ONNX
- **Precision**: FP16 (best quality/performance balance)
- **Target**: CPU inference (Intel/AMD/ARM)
- **Provider**: CPUExecutionProvider
## Key Features
๐ฏ **High Quality** - FP16 precision maintains excellent image quality
๐ **Raspberry Pi Ready** - Optimized for ARM with NEON
โก **Fast CPU Inference** - ONNX Runtime optimizations
๐ฆ **Lightweight** - No PyTorch dependency
๐ง **Easy Setup** - Single pip install
## Quick Start
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
# Load ONNX model
pipe = ORTStableDiffusionPipeline.from_pretrained(
"Mitchins/sd15-onnx-fp16",
provider="CPUExecutionProvider"
)
# Generate image
image = pipe(
"A serene mountain landscape at sunset, highly detailed",
num_inference_steps=20,
guidance_scale=7.5
).images[0]
image.save("output.png")
```
## Installation
```bash
# Minimal dependencies
pip install optimum[onnxruntime] pillow
# For ARM/Raspberry Pi
sudo apt install python3-pip
pip3 install optimum[onnxruntime] pillow
```
## Performance
| Hardware | Time (512x512) | Memory | Quality |
|----------|----------------|---------|---------|
| RPi 4 (4GB) | ~3-5 min | ~2.5GB | Excellent |
| Intel i5 | ~45-90s | ~2.5GB | Excellent |
| M1 Mac | ~30-45s | ~2.5GB | Excellent |
| AMD Ryzen | ~45-90s | ~2.5GB | Excellent |
## Memory Optimization
```python
# Lower memory usage (slower)
pipe.enable_attention_slicing()
# Generate smaller images for faster testing
image = pipe(
prompt,
height=256, width=256, # Faster than 512x512
num_inference_steps=15 # Fewer steps = faster
).images[0]
```
## Model Components
- `unet/model.onnx` - Main diffusion UNet (~3.4GB)
- `text_encoder/model.onnx` - CLIP text encoder (~470MB)
- `vae_decoder/model.onnx` - VAE decoder (~189MB)
- `vae_encoder/model.onnx` - VAE encoder (~130MB)
- `tokenizer/` - Text tokenization files
- `scheduler/` - Noise scheduler configuration
## Use Cases
โ
**Edge AI Applications**
โ
**Raspberry Pi Projects**
โ
**CPU-only Servers**
โ
**Offline Generation**
โ
**Research and Development**
## Comparison
| Version | Speed | Quality | Memory | Use Case |
|---------|-------|---------|---------|----------|
| **This (FP16)** | Medium | Excellent | ~2.5GB | Best quality |
| INT8 | Fast | Very Good | ~2GB | Speed focus |
| PyTorch | Slow | Excellent | ~4GB | Development |
Choose this FP16 version for the best image quality on CPU!
## License
CreativeML Open RAIL-M (inherited from Stable Diffusion 1.5)
|
junjiechen-chris/RAAT-QuestionContext-DPO
|
junjiechen-chris
| 2025-08-06T03:28:02Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T12:12:17Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** junjiechen-chris
- **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)
|
EZCon/gemma-3-4b-it-mlx
|
EZCon
| 2025-08-06T03:22:58Z | 32 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
"mlx",
"conversational",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T04:33:44Z |
---
tags:
- unsloth
- mlx
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, youโre required to review and
agree to Googleโs usage license. To do this, please ensure youโre logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model:
- google/gemma-3-4b-it
---
# EZCon/gemma-3-4b-it-mlx
This model was converted to MLX format from [`unsloth/gemma-3-4b-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3-4b-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3-4b-it-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
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