MedGo: Medical Large Language Model Based on Qwen3-32B

English | 简体中文

📋 Table of Contents

• Introduction
• Key Features
• Performance
• Quick Start
• Training Details
• Use Cases
• Limitations & Risks
• Citation
• License
• Contributing
• Contact

🎯 Introduction

MedGo is a general-purpose medical large language model fine-tuned from Qwen3-32B, designed for clinical medicine and research scenarios. The model is trained on large-scale multi-source medical corpora and enhanced with complex case data, supporting various capabilities including medical Q&A, clinical summary, clinical reasoning, multi-turn dialogue, and scientific text generation.

🌟 Core Capabilities

• 📚 Medical Knowledge Q&A: Professional responses based on authoritative medical literature and clinical guidelines
• 📝 Clinical Documentation: Automated medical record summaries, diagnostic reports, and medical documentation
• 🔍 Clinical Reasoning: Differential diagnosis, examination recommendations, and treatment suggestions
• 💬 Multi-turn Dialogue: Patient-doctor interaction simulation and complex case discussions
• 🔬 Research Support: Literature summarization, research idea generation, and quality control review

✨ Key Features

Feature	Details
Base Architecture	Qwen3-32B
Parameters	32B
Domain	Clinical Medicine, Research Support, Healthcare System Integration
Fine-tuning Method	SFT + Preference Alignment (DPO/KTO)
Data Sources	Authoritative medical literature, clinical guidelines, real cases (anonymized)
Deployment	Local deployment, HIS/EMR system integration
License	Apache 2.0

📊 Performance

MedGo demonstrates excellent performance across multiple medical and general evaluation benchmarks, showing competitive results among 32B-parameter models:

Key Benchmark Results

• AIMedQA: Medical question answering comprehension
• CME: Clinical reasoning evaluation
• DiagnosisArena: Diagnostic capability assessment
• MedQA / MedMCQA: Medical multiple-choice questions
• PubMedQA: Biomedical literature Q&A
• MMLU-Pro: Comprehensive capability evaluation

Performance Highlights:

• ✅ Average Score: ~70 points (excellent performance in the 32B parameter class)
• ✅ Strong Tasks: Clinical reasoning (DiagnosisArena, CME) and multi-turn medical Q&A
• ✅ Balanced Capability: Good performance in medical semantic understanding and multi-task generalization

🚀 Quick Start

Requirements

• Python >= 3.8
• PyTorch >= 2.0
• Transformers >= 4.35.0
• CUDA >= 11.8 (for GPU inference)

Installation

# Clone the repository
git clone https://github.com/OpenMedZoo/MedGo.git
cd MedGo

# Install dependencies
pip install -r requirements.txt

Model Download

Download model weights from HuggingFace:

# Using huggingface-cli
huggingface-cli download OpenMedZoo/MedGo --local-dir ./models/MedGo

# Or using git-lfs
git lfs install
git clone https://huggingface.co/OpenMedZoo/MedGo

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_path = "OpenMedZoo/MedGo"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype="auto"
)

# Medical Q&A example
messages = [
    {"role": "system", "content": "You are a professional medical assistant. Please answer questions based on medical knowledge."},
    {"role": "user", "content": "What is hypertension and what are the common treatment methods?"}
]

# Generate response
inputs = tokenizer.apply_chat_template(
    messages, 
    tokenize=True, 
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
print(response)

Batch Inference

# Use the provided inference script
python scripts/inference.py \
    --model_path OpenMedZoo/MedGo \
    --input_file examples/medical_qa.jsonl \
    --output_file results/predictions.jsonl \
    --batch_size 4

Accelerated Inference with vLLM

from vllm import LLM, SamplingParams

# Initialize vLLM
llm = LLM(model="OpenMedZoo/MedGo", trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512)

# Batch inference
prompts = [
    "What are the symptoms and treatment methods for diabetes?",
    "What dietary precautions should hypertensive patients take?"
]

outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    print(output.outputs[0].text)

🔧 Training Details

MedGo employs a two-stage fine-tuning strategy to balance general medical knowledge with clinical task adaptation.

Stage I: General Medical Alignment

Objective: Establish a solid foundation of medical knowledge and improve Q&A standardization

• Data Sources:

  • Authoritative medical literature (PubMed, medical textbooks)
  • Clinical guidelines and diagnostic standards
  • Medical encyclopedia entries and terminology databases

• Training Methods:

  • Supervised Fine-Tuning (SFT)
  • Chain-of-Thought (CoT) guided samples
  • Medical terminology alignment and safety constraints

Stage II: Clinical Task Enhancement

Objective: Enhance complex case reasoning and multi-task processing capabilities

• Data Sources:

  • Real medical records (fully anonymized)
  • Outpatient and emergency records with complex multi-diagnosis samples
  • Research articles and quality control cases

• Data Augmentation Techniques:

  • Semantic paraphrasing and multi-perspective expansion
  • Complex case synthesis
  • Doctor-patient interaction simulation

• Training Methods:

  • Multi-Task Learning (medical record summary, differential diagnosis, examination suggestions, etc.)
  • Preference Alignment (DPO/KTO)
  • Expert feedback iterative optimization

Training Optimization Focus

• ✅ Strengthen information extraction and cross-evidence reasoning for complex cases
• ✅ Improve medical consistency and interpretability of outputs
• ✅ Optimize expression compliance and safety
• ✅ Continuous iteration through expert samples and automated evaluation

💡 Use Cases

✅ Suitable Scenarios

Scenario	Description
Clinical Assistance	Preliminary diagnosis suggestions, medical record writing, formatted report generation
Research Support	Literature summarization, research idea generation, data analysis assistance
Quality Control	Medical document compliance checking, clinical process quality control
System Integration	Embedded in HIS/EMR systems to provide intelligent decision support
Medical Education	Case discussions, medical knowledge Q&A, clinical reasoning training

🚫 Unsuitable Scenarios

• ❌ Cannot Replace Doctors: Only an auxiliary tool, not a standalone diagnostic basis
• ❌ High-Risk Operations: Not recommended for surgical decisions or other high-risk medical operations
• ❌ Rare Disease Limitations: May perform poorly on rare diseases outside training data
• ❌ Emergency Care: Not suitable for scenarios requiring immediate decisions

⚠️ Limitations & Risks

Model Limitations

1. Understanding Bias: Despite covering extensive medical knowledge, may still produce understanding biases or incorrect recommendations
2. Complex Cases: Higher risk for cases with complex conditions, severe complications, or missing information
3. Knowledge Currency: Medical knowledge continuously updates; training data may lag
4. Language Limitation: Primarily designed for Chinese medical scenarios; performance in other languages may vary

Usage Recommendations

• ⚠️ Use in controlled environments with clinical expert review of generated results
• ⚠️ Treat model outputs as auxiliary references, not final diagnostic conclusions
• ⚠️ For sensitive cases or high-risk scenarios, expert consultation is mandatory
• ⚠️ Deployment requires internal validation, security review, and clinical testing

Data Privacy & Compliance

• 🔒 Training data fully anonymized
• 🔒 Attention to patient privacy protection during use
• 🔒 Production deployment must comply with healthcare data security regulations (e.g., HIPAA, GDPR)
• 🔒 Local deployment recommended to avoid sensitive data transmission

📚 Citation

If MedGo is helpful for your research or project, please cite our work:

@misc{openmedzoo_2025,
    author       = { OpenMedZoo },
    title        = { MedGo (Revision 640a2e2) },
    year         = 2025,
    url          = { https://huggingface.co/OpenMedZoo/MedGo },
    doi          = { 10.57967/hf/7024 },
    publisher    = { Hugging Face }
}

📄 License

This project is licensed under the Apache License 2.0.

Commercial Use Notice:

• ✅ Commercial use and modification allowed
• ✅ Original license and copyright notice must be retained
• ✅ Contact us for technical support when integrating into healthcare systems

🤝 Contributing

We welcome community contributions! Here's how to participate:

Contribution Types

• 🐛 Submit bug reports
• 💡 Propose new features
• 📝 Improve documentation
• 🔧 Submit code fixes or optimizations
• 📊 Share evaluation results and use cases

🙏 Acknowledgments

Thanks to all contributors to the MedGo project:

• Model development and fine-tuning algorithm team
• Data annotation and quality control team
• Clinical expert guidance and review team
• Open-source community support and feedback

Special thanks to:

• Qwen Team for providing excellent foundation models
• All healthcare institutions that provided data and feedback

📧 Contact

• HuggingFace: Model Homepage

---