Instructions to use Feng613/SleepVLM-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Feng613/SleepVLM-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Feng613/SleepVLM-3B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Feng613/SleepVLM-3B")
model = AutoModelForMultimodalLM.from_pretrained("Feng613/SleepVLM-3B")
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 Settings

vLLM

How to use Feng613/SleepVLM-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Feng613/SleepVLM-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Feng613/SleepVLM-3B",
		"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/Feng613/SleepVLM-3B

SGLang

How to use Feng613/SleepVLM-3B 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 "Feng613/SleepVLM-3B" \
    --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": "Feng613/SleepVLM-3B",
		"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 "Feng613/SleepVLM-3B" \
        --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": "Feng613/SleepVLM-3B",
		"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 Feng613/SleepVLM-3B with Docker Model Runner:
```
docker model run hf.co/Feng613/SleepVLM-3B
```

Feng613 commited on Mar 17

Commit

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@@ -79,18 +79,6 @@ model-index:
 > Guifeng Deng, Pan Wang, Jiquan Wang, Tao Li, Haiteng Jiang. "SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model." *In preparation.*
 > This repository will be made public upon release of the preprint.
-## Authors
-| Name | Affiliation | ORCID |
-|------|------------|-------|
-| Guifeng Deng | Zhejiang University | [0009-0001-1940-7797](https://orcid.org/0009-0001-1940-7797) |
-| Pan Wang | Wenzhou Medical University | [0009-0001-6664-6934](https://orcid.org/0009-0001-6664-6934) |
-| Jiquan Wang | Zhejiang University | |
-| Tao Li ✉ | Zhejiang University | [0000-0003-3831-901X](https://orcid.org/0000-0003-3831-901X) |
-| Haiteng Jiang ✉ | Zhejiang University | [0000-0003-0739-8413](https://orcid.org/0000-0003-0739-8413) |
-**Correspondence:** Tao Li (litaozjusc@zju.edu.cn) and Haiteng Jiang (h.jiang@zju.edu.cn)
 ## Overview
 **SleepVLM-3B** is a rule-grounded vision-language model for explainable automated sleep staging from polysomnography (PSG) recordings. Unlike conventional black-box classifiers that output only a stage label, SleepVLM generates clinician-readable natural-language rationales citing specific AASM scoring rules for every 30-second epoch, making each staging decision auditable against the clinical standard.

 > Guifeng Deng, Pan Wang, Jiquan Wang, Tao Li, Haiteng Jiang. "SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model." *In preparation.*
 > This repository will be made public upon release of the preprint.
 ## Overview
 **SleepVLM-3B** is a rule-grounded vision-language model for explainable automated sleep staging from polysomnography (PSG) recordings. Unlike conventional black-box classifiers that output only a stage label, SleepVLM generates clinician-readable natural-language rationales citing specific AASM scoring rules for every 30-second epoch, making each staging decision auditable against the clinical standard.