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OpenMedZoo
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Model Details
Model Provider
OpenMedZoo
Model Tree
Qwen/Qwen3-32B
Input Modalities
Output Modalities
Supported Functionality
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.
| 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 |
MedGo demonstrates excellent performance across multiple medical and general evaluation benchmarks, showing competitive results among 32B-parameter models:
Performance Highlights:
bash
# Clone the repositorygit clone https://github.com/OpenMedZoo/MedGo.gitcd MedGo # Install dependenciespip install -r requirements.txtDownload model weights from HuggingFace:
bash
# Using huggingface-clihuggingface-cli download OpenMedZoo/MedGo --local-dir ./models/MedGo # Or using git-lfsgit lfs installgit clone https://huggingface.co/OpenMedZoo/MedGopython
from transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizermodel_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 examplemessages = [ {"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 responseinputs = 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)bash
# Use the provided inference scriptpython scripts/inference.py \ --model_path OpenMedZoo/MedGo \ --input_file examples/medical_qa.jsonl \ --output_file results/predictions.jsonl \ --batch_size 4python
from vllm import LLM, SamplingParams # Initialize vLLMllm = LLM(model="OpenMedZoo/MedGo", trust_remote_code=True)sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512) # Batch inferenceprompts = [ "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)MedGo employs a two-stage fine-tuning strategy to balance general medical knowledge with clinical task adaptation.
Objective: Establish a solid foundation of medical knowledge and improve Q&A standardization
Data Sources:
Training Methods:
Objective: Enhance complex case reasoning and multi-task processing capabilities
Data Sources:
Data Augmentation Techniques:
Training Methods:
| 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 |
If MedGo is helpful for your research or project, please cite our work:
bibtex
@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 }}This project is licensed under the Apache License 2.0.
Commercial Use Notice:
We welcome community contributions! Here's how to participate:
Thanks to all contributors to the MedGo project:
Special thanks to:
# Clone the repositorygit clone https://github.com/OpenMedZoo/MedGo.gitcd MedGo # Install dependenciespip install -r requirements.txt# Using huggingface-clihuggingface-cli download OpenMedZoo/MedGo --local-dir ./models/MedGo # Or using git-lfsgit lfs installgit clone https://huggingface.co/OpenMedZoo/MedGofrom transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizermodel_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 examplemessages = [ {"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 responseinputs = 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)# Use the provided inference scriptpython scripts/inference.py \ --model_path OpenMedZoo/MedGo \ --input_file examples/medical_qa.jsonl \ --output_file results/predictions.jsonl \ --batch_size 4from vllm import LLM, SamplingParams # Initialize vLLMllm = LLM(model="OpenMedZoo/MedGo", trust_remote_code=True)sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512) # Batch inferenceprompts = [ "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)@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 }}