Instructions to use mao1207/BioMed-VITAL-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mao1207/BioMed-VITAL-models with Transformers:

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

pipe = pipeline("text-generation", model="mao1207/BioMed-VITAL-models")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("mao1207/BioMed-VITAL-models")
model = AutoModelForCausalLM.from_pretrained("mao1207/BioMed-VITAL-models")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mao1207/BioMed-VITAL-models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mao1207/BioMed-VITAL-models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mao1207/BioMed-VITAL-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mao1207/BioMed-VITAL-models

SGLang

How to use mao1207/BioMed-VITAL-models 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 "mao1207/BioMed-VITAL-models" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mao1207/BioMed-VITAL-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mao1207/BioMed-VITAL-models" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mao1207/BioMed-VITAL-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mao1207/BioMed-VITAL-models with Docker Model Runner:
```
docker model run hf.co/mao1207/BioMed-VITAL-models
```

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Check out the documentation for more information.

Overview

BioMed-VITAL is a multimodal foundation model specifically tuned for biomedical applications. It leverages visual and textual data to improve understanding and reasoning within the biomedical domain.

Model Training

The training of BioMed-VITAL involved two key stages, both incorporating clinician preferences to ensure the relevance and quality of the training data:

Data Generation: During this stage, the GPT-4V generator was prompted with a diverse set of clinician-selected demonstrations. This approach facilitated the generation of domain-specific, preference-aligned data candidates, tailored to reflect real-world clinical scenarios and preferences.
Data Selection: A separate selection model was trained to explicitly incorporate clinician and policy-guided preferences. This model employed a sophisticated rating function to evaluate and select the highest quality data for further tuning of BioMed-VITAL. This selection process was critical in refining the dataset to ensure that only the most relevant and accurate instructional data was used.

Performance and Evaluation

The effectiveness of BioMed-VITAL was demonstrated through significant improvements in two key areas:

Open Visual Chat: The model showed a relative improvement of 18.5%, indicating enhanced capabilities in engaging in visual dialogues pertinent to biomedical contexts.
Medical Visual Question Answering (VQA): BioMed-VITAL achieved a win rate of up to 81.73% in this domain, showcasing its superior performance in interpreting and responding to complex medical imagery and queries.

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