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Model: OpenMedZoo/MedGo Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- zh
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- en
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metrics:
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- accuracy
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base_model:
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- Qwen/Qwen3-32B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- medical
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model-index:
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- name: Med-Go-32B
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results:
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- task:
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type: text-generation
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dataset:
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type: medical_eval_hle
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name: Medical-Eval-HLE
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metrics:
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- name: accuracy
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type: accuracy
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value: 19.4
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verified: false
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- task:
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type: text-generation
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dataset:
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type: supergpqa
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name: SuperGPQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 37.2
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medbullets
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name: Medbullets
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metrics:
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- name: accuracy
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type: accuracy
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value: 57.8
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verified: false
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- task:
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type: text-generation
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dataset:
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type: mmlu_pro
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name: MMLU-pro
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metrics:
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- name: accuracy
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type: accuracy
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value: 64.3
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verified: false
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- task:
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type: text-generation
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dataset:
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type: afrimedqa
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name: AfrimedQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 74.7
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medmcqa
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name: MedMCQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 68.3
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medqa_usmle
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name: MedQA-USMLE
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metrics:
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- name: accuracy
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type: accuracy
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value: 76.8
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verified: false
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- task:
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type: text-generation
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dataset:
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type: cmb
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name: CMB
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metrics:
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- name: accuracy
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type: accuracy
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value: 92.5
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verified: false
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- task:
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type: text-generation
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dataset:
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type: cmexam
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name: CMExam
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metrics:
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- name: accuracy
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type: accuracy
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value: 87.4
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verified: false
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- task:
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type: text-generation
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dataset:
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type: pubmedqa
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name: PubMedQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 76.6
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medexqa
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name: MedExQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 81.5
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verified: false
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- task:
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type: text-generation
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dataset:
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type: explaincpe
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name: ExplainCPE
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metrics:
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- name: accuracy
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type: accuracy
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value: 89.5
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verified: false
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- task:
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type: text-generation
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dataset:
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type: mmlu_med
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name: MMLU-Med
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metrics:
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- name: accuracy
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type: accuracy
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value: 87.4
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medxperqa
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name: MedXperQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 20.7
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verified: false
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- task:
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type: text-generation
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dataset:
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type: anesbench
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name: AnesBench
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metrics:
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- name: accuracy
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type: accuracy
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value: 53.1
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verified: false
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- task:
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type: text-generation
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dataset:
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type: diagnosisarena
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name: DiagnosisArena
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metrics:
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- name: accuracy
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type: accuracy
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value: 64.4
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verified: false
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- task:
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type: text-generation
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dataset:
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type: clinbench_hbp
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name: Clinbench-HBP
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metrics:
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- name: accuracy
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type: accuracy
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value: 80.6
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medpair
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name: MedPAIR
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metrics:
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- name: accuracy
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type: accuracy
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value: 32.3
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verified: false
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- task:
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type: text-generation
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dataset:
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type: amqa
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name: AMQA
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metrics:
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- name: accuracy
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type: accuracy
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value: 72.7
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verified: false
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- task:
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type: text-generation
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dataset:
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type: medethicaleval
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name: MedethicalEval
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metrics:
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- name: accuracy
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type: accuracy
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value: 92.2
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verified: false
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---
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# MedGo: Medical Large Language Model Based on Qwen3-32B
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<div align="center">
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[](https://huggingface.co/OpenMedZoo/MedGo)
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[](LICENSE)
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[](https://www.python.org/)
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English | [简体中文](./README_CN.md)
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</div>
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## 📋 Table of Contents
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- [Introduction](#introduction)
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- [Key Features](#key-features)
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- [Performance](#performance)
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- [Quick Start](#quick-start)
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- [Training Details](#training-details)
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- [Use Cases](#use-cases)
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- [Limitations & Risks](#limitations--risks)
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- [Citation](#citation)
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- [License](#license)
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- [Contributing](#contributing)
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- [Contact](#contact)
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## 🎯 Introduction
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**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.
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### 🌟 Core Capabilities
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- **📚 Medical Knowledge Q&A**: Professional responses based on authoritative medical literature and clinical guidelines
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- **📝 Clinical Documentation**: Automated medical record summaries, diagnostic reports, and medical documentation
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- **🔍 Clinical Reasoning**: Differential diagnosis, examination recommendations, and treatment suggestions
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- **💬 Multi-turn Dialogue**: Patient-doctor interaction simulation and complex case discussions
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- **🔬 Research Support**: Literature summarization, research idea generation, and quality control review
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## ✨ Key Features
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| Feature | Details |
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|---------|---------|
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| **Base Architecture** | Qwen3-32B |
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| **Parameters** | 32B |
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| **Domain** | Clinical Medicine, Research Support, Healthcare System Integration |
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| **Fine-tuning Method** | SFT + Preference Alignment (DPO/KTO) |
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| **Data Sources** | Authoritative medical literature, clinical guidelines, real cases (anonymized) |
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| **Deployment** | Local deployment, HIS/EMR system integration |
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| **License** | Apache 2.0 |
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## 📊 Performance
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MedGo demonstrates excellent performance across multiple medical and general evaluation benchmarks, showing competitive results among 32B-parameter models:
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### Key Benchmark Results
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- **AIMedQA**: Medical question answering comprehension
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- **CME**: Clinical reasoning evaluation
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- **DiagnosisArena**: Diagnostic capability assessment
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- **MedQA / MedMCQA**: Medical multiple-choice questions
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- **PubMedQA**: Biomedical literature Q&A
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- **MMLU-Pro**: Comprehensive capability evaluation
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**Performance Highlights**:
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- ✅ **Average Score**: ~70 points (excellent performance in the 32B parameter class)
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- ✅ **Strong Tasks**: Clinical reasoning (DiagnosisArena, CME) and multi-turn medical Q&A
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- ✅ **Balanced Capability**: Good performance in medical semantic understanding and multi-task generalization
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## 🚀 Quick Start
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### Requirements
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- Python >= 3.8
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- PyTorch >= 2.0
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- Transformers >= 4.35.0
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- CUDA >= 11.8 (for GPU inference)
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### Installation
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```bash
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# Clone the repository
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git clone https://github.com/OpenMedZoo/MedGo.git
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cd MedGo
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# Install dependencies
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pip install -r requirements.txt
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```
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### Model Download
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Download model weights from HuggingFace:
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```bash
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# Using huggingface-cli
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huggingface-cli download OpenMedZoo/MedGo --local-dir ./models/MedGo
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# Or using git-lfs
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git lfs install
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git clone https://huggingface.co/OpenMedZoo/MedGo
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```
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### Basic Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model and tokenizer
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model_path = "OpenMedZoo/MedGo"
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map="auto",
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trust_remote_code=True,
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torch_dtype="auto"
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)
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# Medical Q&A example
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messages = [
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{"role": "system", "content": "You are a professional medical assistant. Please answer questions based on medical knowledge."},
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{"role": "user", "content": "What is hypertension and what are the common treatment methods?"}
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]
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# Generate response
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
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print(response)
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```
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### Batch Inference
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```bash
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# Use the provided inference script
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python scripts/inference.py \
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--model_path OpenMedZoo/MedGo \
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--input_file examples/medical_qa.jsonl \
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--output_file results/predictions.jsonl \
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--batch_size 4
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```
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### Accelerated Inference with vLLM
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```python
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from vllm import LLM, SamplingParams
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# Initialize vLLM
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llm = LLM(model="OpenMedZoo/MedGo", trust_remote_code=True)
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sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512)
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# Batch inference
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prompts = [
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"What are the symptoms and treatment methods for diabetes?",
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"What dietary precautions should hypertensive patients take?"
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]
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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print(output.outputs[0].text)
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```
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## 🔧 Training Details
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MedGo employs a **two-stage fine-tuning strategy** to balance general medical knowledge with clinical task adaptation.
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### Stage I: General Medical Alignment
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**Objective**: Establish a solid foundation of medical knowledge and improve Q&A standardization
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- **Data Sources**:
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- Authoritative medical literature (PubMed, medical textbooks)
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- Clinical guidelines and diagnostic standards
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- Medical encyclopedia entries and terminology databases
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- **Training Methods**:
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- Supervised Fine-Tuning (SFT)
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- Chain-of-Thought (CoT) guided samples
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- Medical terminology alignment and safety constraints
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### Stage II: Clinical Task Enhancement
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**Objective**: Enhance complex case reasoning and multi-task processing capabilities
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- **Data Sources**:
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- Real medical records (fully anonymized)
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- Outpatient and emergency records with complex multi-diagnosis samples
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- Research articles and quality control cases
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- **Data Augmentation Techniques**:
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- Semantic paraphrasing and multi-perspective expansion
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- Complex case synthesis
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- Doctor-patient interaction simulation
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- **Training Methods**:
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- Multi-Task Learning (medical record summary, differential diagnosis, examination suggestions, etc.)
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- Preference Alignment (DPO/KTO)
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- Expert feedback iterative optimization
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### Training Optimization Focus
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- ✅ Strengthen information extraction and cross-evidence reasoning for complex cases
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- ✅ Improve medical consistency and interpretability of outputs
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- ✅ Optimize expression compliance and safety
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- ✅ Continuous iteration through expert samples and automated evaluation
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## 💡 Use Cases
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### ✅ Suitable Scenarios
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| Scenario | Description |
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|----------|-------------|
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| **Clinical Assistance** | Preliminary diagnosis suggestions, medical record writing, formatted report generation |
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| **Research Support** | Literature summarization, research idea generation, data analysis assistance |
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| **Quality Control** | Medical document compliance checking, clinical process quality control |
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| **System Integration** | Embedded in HIS/EMR systems to provide intelligent decision support |
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| **Medical Education** | Case discussions, medical knowledge Q&A, clinical reasoning training |
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### 🚫 Unsuitable Scenarios
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- ❌ **Cannot Replace Doctors**: Only an auxiliary tool, not a standalone diagnostic basis
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- ❌ **High-Risk Operations**: Not recommended for surgical decisions or other high-risk medical operations
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- ❌ **Rare Disease Limitations**: May perform poorly on rare diseases outside training data
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- ❌ **Emergency Care**: Not suitable for scenarios requiring immediate decisions
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## ⚠️ Limitations & Risks
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||||
### Model Limitations
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||||
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||||
1. **Understanding Bias**: Despite covering extensive medical knowledge, may still produce understanding biases or incorrect recommendations
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||||
2. **Complex Cases**: Higher risk for cases with complex conditions, severe complications, or missing information
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||||
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
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||||
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||||
### Usage Recommendations
|
||||
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||||
- ⚠️ 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
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||||
- 🔒 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
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||||
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||||
## 📚 Citation
|
||||
|
||||
If MedGo is helpful for your research or project, please cite our work:
|
||||
|
||||
```bibtex
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@misc{openmedzoo_2025,
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||||
author = { OpenMedZoo },
|
||||
title = { MedGo (Revision 640a2e2) },
|
||||
year = 2025,
|
||||
url = { https://huggingface.co/OpenMedZoo/MedGo },
|
||||
doi = { 10.57967/hf/7024 },
|
||||
publisher = { Hugging Face }
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||||
}
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||||
```
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||||
|
||||
## 📄 License
|
||||
|
||||
This project is licensed under the [Apache License 2.0](LICENSE).
|
||||
|
||||
**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](https://github.com/QwenLM/Qwen) for providing excellent foundation models
|
||||
- All healthcare institutions that provided data and feedback
|
||||
|
||||
## 📧 Contact
|
||||
|
||||
- **HuggingFace**: [Model Homepage](https://huggingface.co/OpenMedZoo/MedGo)
|
||||
|
||||
## Copyright
|
||||
- Publisher: Tongji University Affiliated East Hospital — Sole Corresponding Author
|
||||
- Co-developer / Technical Support: Shanghai Shuole Technology Co., Ltd.
|
||||
- Contact: dongfyy@pudong.gov.cn
|
||||
- Version: v1.0
|
||||
- Attribution (required):
|
||||
“Powered by Med-Go 32B, released by Tongji University Affiliated East Hospital (v1.0).”
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
</div>
|
||||
Reference in New Issue
Block a user