Instructions to use X-iZhang/libra-llava-med-v1.5-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use X-iZhang/libra-llava-med-v1.5-mistral-7b with Transformers:

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

pipe = pipeline("image-text-to-text", model="X-iZhang/libra-llava-med-v1.5-mistral-7b")
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("X-iZhang/libra-llava-med-v1.5-mistral-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use X-iZhang/libra-llava-med-v1.5-mistral-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "X-iZhang/libra-llava-med-v1.5-mistral-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "X-iZhang/libra-llava-med-v1.5-mistral-7b",
		"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/X-iZhang/libra-llava-med-v1.5-mistral-7b

SGLang

How to use X-iZhang/libra-llava-med-v1.5-mistral-7b 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 "X-iZhang/libra-llava-med-v1.5-mistral-7b" \
    --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": "X-iZhang/libra-llava-med-v1.5-mistral-7b",
		"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 "X-iZhang/libra-llava-med-v1.5-mistral-7b" \
        --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": "X-iZhang/libra-llava-med-v1.5-mistral-7b",
		"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 X-iZhang/libra-llava-med-v1.5-mistral-7b with Docker Model Runner:
```
docker model run hf.co/X-iZhang/libra-llava-med-v1.5-mistral-7b
```

LLaVA-Med v1.5 (based on Mistral-7B-Instruct-v0.2)

LLaVA-Med (Large Language and Vision Assistant for bioMedicine) is an open-source large vision-language model adapted for biomedical applications. Built upon LLaVA and enhanced through curriculum learning, LLaVA-Med is fine-tuned specifically for open-ended biomedical question answering tasks.

This release aims to support research reproducibility for the corresponding paper, which demonstrates improved performance on biomedical VQA benchmarks such as PathVQA and VQA-RAD.

📌 Note: For original model weights, refer to microsoft/llava-med-v1.5-mistral-7b.

📃 Original paper: LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day.

🔬 Experimental Usage in Libra's repo

This model checkpoint is intended for experimental use and can be tested directly within the Libra repository.

Key Modification

To enable the re-trained vision encoder during inference, ensure the following configuration is applied:

"unfreeze_mm_vision_tower": true

📚 Learn More

For a deeper dive into the methodology, theoretical insights, and performance benchmarks of the Libra framework, please see the following resources:

🔗 Project Website: Libra v1.0
📄 Paper: arXiv:2411.19378
💻 Code Repository: X-iZhang/Libra (GitHub)
📷 Related Project: CCD – Clinical Change Detection; see technical details in the paper here.