Text Generation
Safetensors
Chinese
Traditional Chinese Medicin
Multimodal LLM
multimodal
ShizhenGPT-7B-LLM / README.md
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metadata
license: apache-2.0
datasets:
  - FreedomIntelligence/TCM-Pretrain-Data-ShizhenGPT
  - FreedomIntelligence/TCM-Instruction-Tuning-ShizhenGPT
language:
  - zh
base_model:
  - Qwen/Qwen2.5-7B
pipeline_tag: text-generation
tags:
  - Traditional Chinese Medicin
  - Multimodal LLM
  - multimodal

ShizhenGPT-7B-LLM

ShizhenGPT is the first multimodal LLM for Traditional Chinese Medicine (TCM). It not only possesses strong expertise in TCM, but also supports TCM multimodal diagnostic capabilities, which involve looking (望), listening/smelling (闻), questioning (问), and pulse-taking (切).

👉 More details on GitHub: ShizhenGPT

Model Info

ShizhenGPT-7B-LLM is an LLM-only variant derived from ShizhenGPT-7B-Omni. If your use case requires only text-based capabilities, this version is recommended. Otherwise, please choose the appropriate multimodal version below:

Parameters Supported Modalities Link
ShizhenGPT-7B-LLM 7B Text HF Link
ShizhenGPT-7B-VL 7B Text, Image Understanding HF Link
ShizhenGPT-7B-Omni 7B Text, Four Diagnostics (望闻问切) HF Link
ShizhenGPT-32B-LLM 32B Text HF Link
ShizhenGPT-32B-VL 32B Text, Image Understanding HF Link
ShizhenGPT-32B-Omni 32B Text, Four Diagnostics (望闻问切) Available soon

Note: The LLM and VL models are parameter-split variants of ShizhenGPT-7B-Omni. Since their architectures align with Qwen2.5 and Qwen2.5-VL, they are easier to adapt to different environments. In contrast, ShizhenGPT-7B-Omni requires transformers==4.51.0.

Usage

You can use ShizhenGPT-7B-LLM in the same way as Qwen2.5-7B-Instruct. You can deploy it with tools like vllm or Sglang, or perform direct inference:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-LLM",torch_dtype="auto",device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-LLM")

input_text = "为什么我总是手脚冰凉,是阳虚吗?"
messages = [{"role": "user", "content": input_text}]

inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True
), return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📖 Citation

@misc{chen2025shizhengptmultimodalllmstraditional,
      title={ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine}, 
      author={Junying Chen and Zhenyang Cai and Zhiheng Liu and Yunjin Yang and Rongsheng Wang and Qingying Xiao and Xiangyi Feng and Zhan Su and Jing Guo and Xiang Wan and Guangjun Yu and Haizhou Li and Benyou Wang},
      year={2025},
      eprint={2508.14706},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.14706},
}