llava-med-7b-delta
Maintainer: microsoft
53
🗣️
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
llava-med-7b-delta is a large language and vision assistant model focused on the biomedical domain. It was developed by researchers at Microsoft and is based on the LLaMA model. The model was initialized with the general-domain LLaVA and then continuously trained in a curriculum learning fashion, first on biomedical concept alignment and then on full-blown instruction tuning.
This model is similar to other medical-focused language models like MedAlpaca 13b and MedAlpaca 7b, which are also fine-tuned on medical datasets to improve performance on tasks like question answering and medical dialogue. However, llava-med-7b-delta goes beyond text-only capabilities by incorporating visual understanding through its connection to the general-domain LLaVA model.
The model was also trained on the PMC-15M dataset, a large-scale parallel image-text dataset for biomedical vision-language processing, which is the same dataset used to train the BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 model.
Model inputs and outputs
Inputs
- Images: The model can accept images as input, enabling it to perform visual reasoning and understanding tasks in the biomedical domain.
- Text: The model can also accept text input, allowing it to engage in language-based interactions and tasks.
Outputs
- Text generation: The model can generate relevant and coherent text in response to prompts, leveraging its biomedical knowledge.
- Multimodal understanding: The model can combine its understanding of both images and text to perform tasks like visual question answering or image captioning.
Capabilities
llava-med-7b-delta exhibits strong performance on a variety of biomedical tasks, particularly those that require both language and visual understanding. For example, the model can accurately describe the contents of a medical image, answer questions about a radiological scan, or provide step-by-step instructions for a medical procedure.
The model's visual understanding capabilities are a key strength, allowing it to excel at tasks like interpreting medical images and diagrams. This sets it apart from language-only models that may struggle with visual inputs.
What can I use it for?
Researchers and developers working on biomedical applications could use llava-med-7b-delta for a variety of projects, such as:
- Medical image analysis: The model could be used to build tools that analyze medical images, such as X-rays or MRI scans, and provide insights or recommendations.
- Biomedical question answering: The model could be integrated into chatbots or virtual assistants to answer questions about medical conditions, treatments, or procedures.
- Multimodal medical education: The model could be used to create interactive learning experiences that combine text, images, and video to teach medical concepts.
However, it's important to note that the model should only be used for research purposes and not for any clinical or deployed applications, as it has not been thoroughly tested for real-world use.
Things to try
One interesting aspect of llava-med-7b-delta is its ability to combine visual and language understanding to tackle complex biomedical tasks. For example, you could try prompting the model with a medical image and asking it to provide a step-by-step explanation of the procedure or condition depicted. This would showcase the model's capacity to integrate its knowledge of both visual and textual information.
Another avenue to explore would be using the model for creative or exploratory tasks, such as generating medical illustrations or diagrams based on textual descriptions. This could inspire new ways of visualizing and communicating biomedical concepts.
Ultimately, the versatility of llava-med-7b-delta makes it a valuable tool for researchers and developers working to advance the state of the art in biomedical artificial intelligence.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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