Image-Text-to-Text
Transformers
Safetensors
PyTorch
hulumed_qwen3
text-generation
medical
multimodal
vision-language-model
image-to-text
video-understanding
3d-understanding
qwen
conversational
custom_code
Instructions to use ZJU-AI4H/Hulu-Med-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZJU-AI4H/Hulu-Med-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ZJU-AI4H/Hulu-Med-4B", trust_remote_code=True) 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ZJU-AI4H/Hulu-Med-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZJU-AI4H/Hulu-Med-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZJU-AI4H/Hulu-Med-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZJU-AI4H/Hulu-Med-4B", "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/ZJU-AI4H/Hulu-Med-4B
- SGLang
How to use ZJU-AI4H/Hulu-Med-4B 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 "ZJU-AI4H/Hulu-Med-4B" \ --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": "ZJU-AI4H/Hulu-Med-4B", "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 "ZJU-AI4H/Hulu-Med-4B" \ --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": "ZJU-AI4H/Hulu-Med-4B", "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 ZJU-AI4H/Hulu-Med-4B with Docker Model Runner:
docker model run hf.co/ZJU-AI4H/Hulu-Med-4B
| { | |
| "chat_template": "\n{%- set identifier = 'im' %}\n{% for message in messages %}\n {% if add_system_prompt and loop.first and message['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are Hulu-Med, a helpful health assistant that can understand text, 2D images, videos, and 3D images.<|im_end|>\n' -}}\n {% endif %}\n {% if message['role'] == 'stream' %}\n {% set identifier = 'stream' %}\n {% else %}\n {% set identifier = 'im' %}\n {% endif %}\n {{- '<|' + identifier + '_start|>' + message['role'] + '\n' -}}\n {% if message['content'] is string %}\n {{- message['content'] + '<|' + identifier + '_end|>\n' -}}\n {% else %}\n {% for content in message['content'] %}\n {% if content is string %}\n {{- content -}}\n {% elif content['type'] == 'text' or 'text' in content %}\n {{- content['text'] -}}\n {% elif content['type'] == 'image' or 'image' in content %}\n {% if 'timestamp' in content %}\n {{- 'Time ' + content['timestamp'] | round(1) | string + 's: ' -}}\n {% endif %}\n {{- image_token + '\n' -}}\n {% elif content['type'] == 'video' or 'video' in content %}\n {% for i in range(content['num_frames']) %}\n {% if 'timestamps' in content %}\n {{- 'Time ' + content['timestamps'][i] | round(1) | string + 's:' -}}\n {% endif %}\n {% if i < content['num_frames'] - 1 %}\n {{- image_token + ',' -}}\n {% else %}\n {{- image_token + '\n' -}}\n {% endif %}\n {% endfor %}\n {% endif %}\n {% endfor %}\n {% if identifier == 'stream' %}\n {{- '<|' + identifier + '_end|>' -}}\n {% else %}\n {{- '<|' + identifier + '_end|>\n' -}}\n {% endif %}\n {% endif %}\n{% endfor %}\n{% if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' -}}\n{% endif %}\n" | |
| } | |