The realm of artificial intelligence is constantly evolving, and one of the latest innovations is LLaVA-Med, a cutting-edge model designed for biomedicine applications. In this blog, we’ll walk you through the steps to use LLaVA-Med effectively, ensuring you’re equipped to harness its full potential in your research projects.

What is LLaVA-Med?

LLaVA-Med stands for Large Language and Vision Assistant for Biomedicine. It is a model equipped with GPT-4 level capabilities, fine-tuned to understand and process visual and textual biomedical information. But before you dive in, let’s understand how to get it operational.

Step-by-Step Installation Guide

Follow these steps to get LLaVA-Med running on your system:

1. Clone the Repository:
   • Begin by cloning the LLaVA-Med Github repository.
   • Navigate to the LLaVA-Med folder:

   git clone https://github.com/microsoft/LLaVA-Med.git
   cd LLaVA-Med

2. Create a Conda Environment:

   conda create -n llava-med python=3.10 -y
   conda activate llava-med
   pip install --upgrade pip

3. Install Necessary Packages:
   • Uninstall existing installations of torch:

   pip uninstall torch torchvision -y
   pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
   pip install openai==0.27.8

   • Install other necessary packages:

   pip uninstall transformers -y
   pip install git+https://github.com/huggingface/transformers@cae78c46
   pip install -e .
   pip install einops ninja open-clip-torch
   pip install flash-attn --no-build-isolation

Applying Delta Weights

Remember, the LLaVA-Med model weights you downloaded are delta weights. To get the full potential, you need to apply them to the original LLaMA weights. Here’s how:

1. Download the delta weights and follow instructions to obtain the original LLaMA weights.
2. Run the following command:

python3 -m llava.model.apply_delta --base path/to/llama-7b --target output/path/to/llava_med_in_text_60k --delta path/to/llava_med_in_text_60k_delta

Evaluation and Fine-Tuning

Once you have your model set up, it’s time to evaluate it.

1. Generate responses using LLaVA-Med:

python model_vqa.py --model-name .checkpoints/LLaVA-7B-v0 --question-file data/eval/lava_med_eval_qa50_qa.jsonl --image-folder data/images --answers-file path/to/answer-file.jsonl

2. Utilize the GPT-assisted evaluation pipeline provided in the resources to understand your model’s capabilities.
3. Explore the various datasets available, such as VQA-Rad, SLAKE, and Pathology-VQA for fine-tuning your model further.

Understanding the Model Setup with an Analogy

Think of setting up the LLaVA-Med like preparing a well-balanced meal. The original ingredients (LLaMA weights) are essential for the meal (model), but to make it a gastronomic delight (full performance), you need to apply the right spices (delta weights) to enhance the flavor. Each step in the preparation – from cloning the repository (gathering ingredients) to installing packages (chopping and mixing) – is crucial to achieving the final dish (working model) that both satisfies and nurtures your research goals.

Troubleshooting Common Issues

While working with LLaVA-Med, you may encounter a few bumps along the way. Here are some troubleshooting tips:

• Issue: Missing dependencies during the package installation.
• Solution: Ensure you’re using the correct versions of Python and other dependencies. Refer to the respective package documentation for upgrade instructions.
• Issue: Model not generating responses.
• Solution: Double-check the paths to your weights and files. Ensure the model is correctly loaded before evaluation.

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Conclusion

By following these steps, you’re set to unlock the capabilities of LLaVA-Med in your biomedical research. Remember that consistent testing and tuning are key to success.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.