Instructions to use PKUDigitalHealth/ECG-R1-8B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PKUDigitalHealth/ECG-R1-8B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="PKUDigitalHealth/ECG-R1-8B-SFT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("PKUDigitalHealth/ECG-R1-8B-SFT")
model = AutoModelForSeq2SeqLM.from_pretrained("PKUDigitalHealth/ECG-R1-8B-SFT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PKUDigitalHealth/ECG-R1-8B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PKUDigitalHealth/ECG-R1-8B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PKUDigitalHealth/ECG-R1-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/PKUDigitalHealth/ECG-R1-8B-SFT

SGLang

How to use PKUDigitalHealth/ECG-R1-8B-SFT 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 "PKUDigitalHealth/ECG-R1-8B-SFT" \
    --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": "PKUDigitalHealth/ECG-R1-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "PKUDigitalHealth/ECG-R1-8B-SFT" \
        --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": "PKUDigitalHealth/ECG-R1-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use PKUDigitalHealth/ECG-R1-8B-SFT with Docker Model Runner:
```
docker model run hf.co/PKUDigitalHealth/ECG-R1-8B-SFT
```

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

If you find this project useful, please give us a star🌟.

Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang, Xiang Lan, Zihan Wang
Deyun Zhang, Bo Liu, Yingying Zhang, Xian Wu, Hongyan Li, Shenda Hong

Introduction

Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation, often producing plausible but clinically incorrect analyses. To address this, we propose ECG-R1, the first reasoning MLLM designed for reliable ECG interpretation via three innovations. First, we construct the interpretation corpus using Protocol-Guided Instruction Data Generation, grounding interpretation in measurable ECG features and monograph-defined quantitative thresholds and diagnostic logic. Second, we present a modality-decoupled architecture with Interleaved Modality Dropout to improve robustness and cross-modal consistency when either the ECG signal or ECG image is missing. Third, we present Reinforcement Learning with ECG Diagnostic Evidence Rewards to strengthen evidence-grounded ECG interpretation. Additionally, we systematically evaluate the ECG interpretation capabilities of proprietary, open-source, and medical MLLMs, and provide the first quantitative evidence that severe hallucinations are widespread, suggesting that the public should not directly trust these outputs without independent verification.

Resource

Paper: ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
GitHub Repository: PKUDigitalHealth/ECG-R1
Online Platform: ECG-R1-Online-Platform
Model: 🤗 ECG-R1-8B
Data: 🤗 ECG-Protocol-Guided-Grounding-CoT

Citation

If you find ECG-R1 helpful for your research and applications, please cite our paper:

@misc{jin2026ecgr1,
      title={ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation}, 
      author={Jiarui Jin and Haoyu Wang and Xingliang Wu and Xiaocheng Fang and Xiang Lan and Zihan Wang and Deyun Zhang and Bo Liu and Yingying Zhang and Xian Wu and Hongyan Li and Shenda Hong},
      year={2026},
      eprint={2602.04279},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.04279}, 
}

Acknowledgement

We thank the authors of PULSE, ECG-Chat, GEM, and Swift for their publicly released models, datasets, and training codes.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for PKUDigitalHealth/ECG-R1-8B-SFT

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

(273)

this model

Dataset used to train PKUDigitalHealth/ECG-R1-8B-SFT

Paper for PKUDigitalHealth/ECG-R1-8B-SFT

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

Paper • 2602.04279 • Published Feb 4 • 1

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

If you find this project useful, please give us a star🌟.

Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang, Xiang Lan, Zihan Wang Deyun Zhang, Bo Liu, Yingying Zhang, Xian Wu, Hongyan Li, Shenda Hong

Introduction

Resource

Citation

Acknowledgement

Model tree for PKUDigitalHealth/ECG-R1-8B-SFT

Dataset used to train PKUDigitalHealth/ECG-R1-8B-SFT

Paper for PKUDigitalHealth/ECG-R1-8B-SFT

Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang, Xiang Lan, Zihan Wang
Deyun Zhang, Bo Liu, Yingying Zhang, Xian Wu, Hongyan Li, Shenda Hong