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TCMLLM
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TCMLLM
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Lingdan-4B-Base is a Qwen3-based TCM model in the Lingdan-V2 family.
| Field | Value |
|---|---|
| Parameter scale | 4B |
| Architecture family | Qwen3 causal language model |
| Training stage | Continued pretraining (CPT) |
| Starting checkpoint / backbone | Qwen3 4B backbone |
| Intended use | Text completion, domain research, and further fine-tuning |
| Platform model ID | TCMLLM/Lingdan-4B-Base |
This checkpoint includes the following training stages, in order:
| Stage | Objective | Training data |
|---|---|---|
| CPT — continued pretraining | TCM-domain knowledge acquisition | 16.62B tokens |
Use Transformers ≥4.51.0; see the Qwen3 quickstart.
bash
pip install -U torch transformers acceleratepython
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "TCMLLM/Lingdan-4B-Base"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",).eval() # Base checkpoints are intended for completion and further fine-tuning.text = "中医辨证论治的基本概念是"inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate( **inputs, max_new_tokens=1024, do_sample=True, temperature=0.6, top_p=0.95, top_k=20,)new_tokens = outputs[0, inputs.input_ids.shape[1]:]print(tokenizer.decode(new_tokens, skip_special_tokens=True))Install vLLM in a GPU environment with compatible CUDA/PyTorch; see the Qwen3 vLLM guide.
bash
pip install -U "vllm>=0.9.0" openaiHugging Face:
bash
export VLLM_USE_MODELSCOPE=falsevllm serve TCMLLM/Lingdan-4B-Base \ --served-model-name Lingdan-4B-Base \ --host 127.0.0.1 \ --port 8000 \ --max-model-len 16384Replace the repository ID with a local model directory to reuse downloaded files. For two GPUs, append --tensor-parallel-size 2. Adjust the 16,384-token context limit (input + output) to available GPU memory.
python
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")response = client.completions.create( model="Lingdan-4B-Base", prompt="中医辨证论治的基本特点是", max_tokens=512, temperature=0.6, top_p=0.95, extra_body={"top_k": 20},)print(response.choices[0].text)EMPTY is a placeholder for the local server started without API-key authentication.
Lingdan-V2 is for research and does not replace qualified clinical judgment. Private clinical data are not released. Retrospective prescription agreement is not evidence of clinical safety or effectiveness; use de-identified inputs and professional review for any clinical application.
Lingdan-V2 model weights and project-owned documentation and images are released under the Apache License 2.0. See NOTICE for attribution notices. Third-party data and materials remain subject to their original terms.
Please cite Lingdan-V2 and the earlier Lingdan work:
bibtex
@misc{hua2026lingdanv2, title = {{Lingdan-V2}: Large language models for clinically grounded reasoning in traditional Chinese medicine}, author = {Hua, Rui and Wei, Yu and Jin, Ziri and Sun, Zhuo and Chang, Kai and Xia, Jianan and Shu, Zixin and Li, Xiaodong and Jia, Dongmei and Dong, Fei and Zhang, Runshun and Yu, Jian and Xu, Hao and Yu, Haibin and Li, Jiansheng and Liu, Baoyan and Wang, Wenjia and Zhou, Xuezhong}, year = {2026}, eprint = {2610.14705}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2610.14705}} @article{hua2024lingdan, title={Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models}, author={Hua, Rui and Dong, Xin and Wei, Yu and Shu, Zixin and Yang, Pengcheng and Hu, Yunhui and Zhou, Shuiping and Sun, He and Yan, Kaijing and Yan, Xijun and others}, journal={Journal of the American Medical Informatics Association}, volume={31}, number={9}, pages={2019--2029}, year={2024}, publisher={Oxford University Press}}Paper · GitHub · Hugging Face collection · ModelScope collection · LingLan benchmark
For task prompts and thinking-mode chat, see the SFT/R1 guide.
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "TCMLLM/Lingdan-4B-Base"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",).eval() # Base checkpoints are intended for completion and further fine-tuning.text = "中医辨证论治的基本概念是"inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate( **inputs, max_new_tokens=1024, do_sample=True, temperature=0.6, top_p=0.95, top_k=20,)new_tokens = outputs[0, inputs.input_ids.shape[1]:]print(tokenizer.decode(new_tokens, skip_special_tokens=True))export VLLM_USE_MODELSCOPE=falsevllm serve TCMLLM/Lingdan-4B-Base \ --served-model-name Lingdan-4B-Base \ --host 127.0.0.1 \ --port 8000 \ --max-model-len 16384from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")response = client.completions.create( model="Lingdan-4B-Base", prompt="中医辨证论治的基本特点是", max_tokens=512, temperature=0.6, top_p=0.95, extra_body={"top_k": 20},)print(response.choices[0].text)@misc{hua2026lingdanv2, title = {{Lingdan-V2}: Large language models for clinically grounded reasoning in traditional Chinese medicine}, author = {Hua, Rui and Wei, Yu and Jin, Ziri and Sun, Zhuo and Chang, Kai and Xia, Jianan and Shu, Zixin and Li, Xiaodong and Jia, Dongmei and Dong, Fei and Zhang, Runshun and Yu, Jian and Xu, Hao and Yu, Haibin and Li, Jiansheng and Liu, Baoyan and Wang, Wenjia and Zhou, Xuezhong}, year = {2026}, eprint = {2610.14705}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2610.14705}} @article{hua2024lingdan, title={Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models}, author={Hua, Rui and Dong, Xin and Wei, Yu and Shu, Zixin and Yang, Pengcheng and Hu, Yunhui and Zhou, Shuiping and Sun, He and Yan, Kaijing and Yan, Xijun and others}, journal={Journal of the American Medical Informatics Association}, volume={31}, number={9}, pages={2019--2029}, year={2024}, publisher={Oxford University Press}}