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 = {HF中国镜像站},
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 HF中国镜像站 page at https://huggingface.co/DXCLab or open an issue in the repository.
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