README.md

---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-3B-Instruct
tags:
- medical
- cancer
- Onco
---
# OncoCareBrain-GPT

## Model Description

OncoCareBrain-GPT is a specialized large language model fine-tuned for oncology applications. Built upon the powerful Qwen2.5-3B foundation model, it has undergone supervised fine-tuning (SFT) with tens of thousands of multi-omics data samples, including genomic, pathological, and clinical data. This model is specifically designed to serve the cancer care domain with advanced reasoning capabilities.

## Key Features

- **Intelligent Medical Q&A**: Quickly answers complex questions about cancer, leveraging a deep understanding of oncology concepts
- **Precision Decision Support**: Recommends optimal treatment plans based on multi-dimensional data analysis
- **Transparent Reasoning Process**: Generates detailed chains of thought to ensure model explainability and trust in clinical settings

## Intended Uses

- **Clinical Decision Support**: Assists healthcare providers in evaluating treatment options
- **Patient Education**: Helps patients better understand their condition and treatment plans
- **Medical Research**: Supports researchers in analyzing cancer data and generating insights

## Training Data

OncoCareBrain-GPT was fine-tuned on a diverse dataset comprising:
- Genomic data
- Pathological samples
- Clinical records and case studies

The model was trained to generate detailed reasoning chains, provide personalized prognostic assessments, and suggest evidence-based treatment recommendations.

## Technical Specifications

- **Base Model**: Qwen2.5-3B
- **Parameters**: 3 billion
- **Training Method**: Supervised Fine-Tuning (SFT)
- **Language Capabilities**: English, Chinese
- **Input Format**: Natural language
- **Output Format**: Detailed explanations with chain-of-thought reasoning

## Limitations

- The model should be used as a clinical decision support tool and not as a replacement for professional medical judgment
- Recommendations should be verified by qualified healthcare professionals
- Performance may vary depending on the complexity and rarity of cancer cases
- While the model supports English and Chinese, performance might vary between languages

## Ethical Considerations

- **Privacy**: The model operates on input data and does not store patient information
- **Bias**: While efforts have been made to minimize biases, users should be aware of potential biases in training data
- **Transparency**: The model provides reasoning chains to ensure transparency in its decision-making process

## How to Use

```python
# Example code for model inference
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DXCLab/OncoCareBrain-GPT")
model = AutoModelForCausalLM.from_pretrained("DXCLab/OncoCareBrain-GPT")

input_text = "Could you analyze this genomic profile and suggest potential treatment options for breast cancer with BRCA1 mutation?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1000)
response = tokenizer.decode(outputs[0])
print(response)
```

## Citation

If you use OncoCareBrain-GPT in your research, please cite:

```
@misc{OncoCareBrain-GPT,
  author = {DXCLab},
  title = {OncoCareBrain-GPT: A Specialized Language Model for Oncology},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/DXCLab/OncoCareBrain-GPT}}
}
```

## License

This model is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.

## Contact

For questions or feedback about OncoCareBrain-GPT, please visit our Hugging Face page at https://huggingface.co/DXCLab or open an issue in the repository.