OncoCareBrain-GPT

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

# 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 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.