Instructions to use TCMLLM/Lingdan-4B-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TCMLLM/Lingdan-4B-R1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TCMLLM/Lingdan-4B-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TCMLLM/Lingdan-4B-R1")
model = AutoModelForCausalLM.from_pretrained("TCMLLM/Lingdan-4B-R1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TCMLLM/Lingdan-4B-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TCMLLM/Lingdan-4B-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TCMLLM/Lingdan-4B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TCMLLM/Lingdan-4B-R1

SGLang

How to use TCMLLM/Lingdan-4B-R1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TCMLLM/Lingdan-4B-R1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TCMLLM/Lingdan-4B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TCMLLM/Lingdan-4B-R1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TCMLLM/Lingdan-4B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TCMLLM/Lingdan-4B-R1 with Docker Model Runner:
```
docker model run hf.co/TCMLLM/Lingdan-4B-R1
```

TCMLLM commited on 8 days ago

Commit

c848ca3

verified ·

1 Parent(s): df7f277

Improve model card navigation and result placement

Browse files

Files changed (1) hide show

README.md +29 -39

README.md CHANGED Viewed

@@ -28,24 +28,14 @@ tags:
 </p>
 <p align="center">
   <a href="https://modelscope.cn/collections/TCMAIBJTU/Lingdan-V2"><img src="https://img.shields.io/badge/Models-ModelScope-624AFF" alt="ModelScope models" /></a>
   <a href="https://github.com/TCMAI-BJTU/LingLan"><img src="https://img.shields.io/badge/Benchmark-LingLan-2A8F5B" alt="LingLan benchmark" /></a>
   <img src="https://img.shields.io/badge/Model_scales-4B_%7C_8B_%7C_14B-0B5394" alt="Model scales: 4B, 8B, and 14B" />
   <a href="https://github.com/TCMAI-BJTU/Lingdan-V2/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache--2.0-D22128" alt="Apache License 2.0" /></a>
 </p>
-## Contents
-1. [Model Details](#model-details)
-2. [Quickstart](#quickstart)
-3. [Thinking Mode](#thinking-mode)
-4. [vLLM Deployment](#vllm-deployment)
-5. [Checkpoint Evaluation](#checkpoint-evaluation)
-6. [Task Prompts](#task-prompts)
-7. [Data Privacy and Responsible Use](#data-privacy-and-responsible-use)
-8. [License](#license)
-9. [Citation](#citation)
-10. [Related Links](#related-links)
 ## Model Details
@@ -60,6 +50,33 @@ tags:
 | Intended use | Structured TCM clinical reasoning and prescription-generation research |
 | Platform model ID | `TCMLLM/Lingdan-4B-R1` |
 ## Quickstart
 Use Transformers ≥4.51.0; see the [Qwen3 quickstart](https://qwen.readthedocs.io/en/stable/getting_started/quickstart.html).
@@ -185,33 +202,6 @@ print("Finish reason:", response.choices[0].finish_reason)
 `EMPTY` is a placeholder for the local server started without API-key authentication.
-## Checkpoint Evaluation
-Paper-reported results for **Lingdan-4B-R1**. Full: LingLan (25,620 items); Hard: LingLan-Hard (5,200 items). Overall Score averages 16 primary metrics, excluding dose MAE. All scores use a 0–100 scale except MAE (lower is better). The paper uses zero-shot Chinese prompts, temperature 0.6, and at most 8,192 generated tokens; reproduction also requires the same data and scoring pipeline.
-| Metric | LingLan Full | LingLan-Hard |
-| --- | ---: | ---: |
-| Overall Score ↑ | 63.4 | 40.4 |
-| Prescription generation F1 ↑ | 36.2 | 24.8 |
-| Clinical-record entity extraction F1 ↑ | 70.5 | 58.4 |
-| Classical-text entity extraction F1 ↑ | 58.5 | 42.5 |
-| Syndrome generation F1 ↑ | 9.5 | 1.6 |
-| Treatment-principle generation F1 ↑ | 13.9 | 2.4 |
-| Prescription dose cosine ↑ | 34.2 | 23.4 |
-| Prescription dose MAE ↓ | 4.6 | 4.7 |
-Task-level scores follow the appendix’s primary DTR metrics, not the character-level DTR-F1 variant.
-| Private prescription evaluation (4,348 cases) | Score |
-| --- | ---: |
-| Herb F1 ↑ | 52.7 |
-| Herb precision ↑ | 52.5 |
-| Herb recall ↑ | 54.1 |
-| Dose cosine ↑ | 49.5 |
-| Dose MAE ↓ | 5.062 |
-The private set contains 4,348 de-identified spleen/stomach disorder cases. See the [full comparison](https://github.com/TCMAI-BJTU/Lingdan-V2#main-results) for other checkpoints.
 ## Task Prompts
 For SFT/R1 models, fill the placeholders in these LingLan evaluation templates and pass the text as the user message.

 </p>
 <p align="center">
+  <a href="https://huggingface.co/collections/TCMLLM/lingdan-v2"><img src="https://img.shields.io/badge/Models-Hugging_Face-FFD21E?logo=huggingface&logoColor=black" alt="Hugging Face models" /></a>
   <a href="https://modelscope.cn/collections/TCMAIBJTU/Lingdan-V2"><img src="https://img.shields.io/badge/Models-ModelScope-624AFF" alt="ModelScope models" /></a>
   <a href="https://github.com/TCMAI-BJTU/LingLan"><img src="https://img.shields.io/badge/Benchmark-LingLan-2A8F5B" alt="LingLan benchmark" /></a>
   <img src="https://img.shields.io/badge/Model_scales-4B_%7C_8B_%7C_14B-0B5394" alt="Model scales: 4B, 8B, and 14B" />
   <a href="https://github.com/TCMAI-BJTU/Lingdan-V2/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache--2.0-D22128" alt="Apache License 2.0" /></a>
 </p>
+[Model](#model-details) · [Results](#checkpoint-evaluation) · [Quickstart](#quickstart) · [vLLM](#vllm-deployment) · [Prompts](#task-prompts) · [Citation](#citation)
 ## Model Details
 | Intended use | Structured TCM clinical reasoning and prescription-generation research |
 | Platform model ID | `TCMLLM/Lingdan-4B-R1` |
+## Checkpoint Evaluation
+Paper-reported results for **Lingdan-4B-R1**. Full: LingLan (25,620 items); Hard: LingLan-Hard (5,200 items). Overall Score averages 16 primary metrics, excluding dose MAE. All scores use a 0–100 scale except MAE (lower is better). The paper uses zero-shot Chinese prompts, temperature 0.6, and at most 8,192 generated tokens; reproduction also requires the same data and scoring pipeline.
+| Metric | LingLan Full | LingLan-Hard |
+| --- | ---: | ---: |
+| Overall Score ↑ | 63.4 | 40.4 |
+| Prescription generation F1 ↑ | 36.2 | 24.8 |
+| Clinical-record entity extraction F1 ↑ | 70.5 | 58.4 |
+| Classical-text entity extraction F1 ↑ | 58.5 | 42.5 |
+| Syndrome generation F1 ↑ | 9.5 | 1.6 |
+| Treatment-principle generation F1 ↑ | 13.9 | 2.4 |
+| Prescription dose cosine ↑ | 34.2 | 23.4 |
+| Prescription dose MAE ↓ | 4.6 | 4.7 |
+Task-level scores follow the appendix’s primary DTR metrics, not the character-level DTR-F1 variant.
+| Private prescription evaluation (4,348 cases) | Score |
+| --- | ---: |
+| Herb F1 ↑ | 52.7 |
+| Herb precision ↑ | 52.5 |
+| Herb recall ↑ | 54.1 |
+| Dose cosine ↑ | 49.5 |
+| Dose MAE ↓ | 5.062 |
+The private set contains 4,348 de-identified spleen/stomach disorder cases. See the [full comparison](https://github.com/TCMAI-BJTU/Lingdan-V2#main-results) for other checkpoints.
 ## Quickstart
 Use Transformers ≥4.51.0; see the [Qwen3 quickstart](https://qwen.readthedocs.io/en/stable/getting_started/quickstart.html).
 `EMPTY` is a placeholder for the local server started without API-key authentication.
 ## Task Prompts
 For SFT/R1 models, fill the placeholders in these LingLan evaluation templates and pass the text as the user message.