Edit model card
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)


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:

  1. 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.

  2. 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.
Downloads last month
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.