How to Use LLaVA: An Open-Source Large Multimodal Model for Chat and Visual Tasks

How to Use LLaVA Model in Your Application

LLaVA is a large multimodal model that can understand and follow visual and language instructions. It can chat with you and perform various tasks based on your queries and images. You can use LLaVA to enhance your application with powerful visual and language capabilities, such as image captioning, visual question answering, visual dialog, visual reasoning, text summarization, natural language generation, and more. In this article, I will show you how to use LLaVA in your application step by step.

Step 1: Install LLaVA

To use LLaVA, you need to install it on your machine or server. You can download the LLaVA code and models from GitHub. You will also need to install some dependencies, such as PyTorch, DeepSpeed, and HuggingFace Transformers. Please refer to the README file for detailed installation instructions.

Step 2: Prepare Your Data

To use LLaVA, you need to prepare your data in a specific format. You need to have a JSON file that contains a list of multimodal examples. Each example consists of an image URL, a caption, and a query. The image URL is the link to the image that you want LLaVA to process. The caption is a brief description of the image content. The query is the instruction that you want LLaVA to follow based on the image and the caption. For example:

[
  {
    "image_url": "https://example.com/image1.jpg",
    "caption": "A man holding a guitar on a stage",
    "query": "Write a song title for this image"
  },
  {
    "image_url": "https://example.com/image2.jpg",
    "caption": "A woman wearing a red dress and a hat",
    "query": "What color is her hat?"
  },
  ...
]

You can create your own data or use some existing datasets, such as CC3M, VQA v2.0, COCO Captioning, VisDial v1.0, CLEVR, and more.

Step 3: Run LLaVA

To run LLaVA, you need to use the llava command with some arguments. You need to specify the path to your data file, the path to the model checkpoint, the output file name, and some other options. For example:

llava --data_file data.json --model_file llava-13b-v1.pt --output_file output.json --batch_size 16 --num_workers 4

This command will run LLaVA on the data file data.json using the model checkpoint llava-13b-v1.pt. It will save the results in the output file output.json. It will use a batch size of 16 and 4 workers for parallel processing. You can adjust these parameters according to your hardware specifications and preferences. Please refer to the documentation for more details on the arguments.

Step 4: Analyze the Results

After running LLaVA, you can analyze the results in the output file. The output file is also a JSON file that contains a list of multimodal examples with responses. Each example consists of an image URL, a caption, a query, and a response. The response is the output that LLaVA generated based on the image, the caption, and the query. For example:

        

            


        
        [
  {
    "image_url": "https://example.com/image1.jpg",
    "caption": "A man holding a guitar on a stage",
    "query": "Write a song title for this image",
    "response": "Rocking Out Loud"
  },
  {
    "image_url": "https://example.com/image2.jpg",
    "caption": "A woman wearing a red dress and a hat",
    "query": "What color is her hat?",
    "response": "Her hat is red"
  },
  ...
]

You can evaluate the quality of the responses using various metrics, such as BLEU, ROUGE, METEOR, CIDEr, SPICE, etc. You can also compare the responses with human-generated ones or ground-truth answers if available.

Step 5: Fine-Tune LLaVA

If you are not satisfied with the results of LLaVA or want to customize it for your specific application domain or task, you can fine-tune LLaVA with your own visual instructions. Visual instruction tuning is a novel technique that allows you to teach LLaVA how to generate similar or related content based on your examples. For example, you can provide an image of a desired output or a sketch of a concept, and LLaVA will learn to generate similar or related content. To fine-tune LLaVA, you need to prepare your data in a similar format as before, but with an additional field called target. The target is the image URL or the text that you want LLaVA to generate based on the image, the caption, and the query. For example:

[
  {
    "image_url": "https://example.com/image1.jpg",
    "caption": "A man holding a guitar on a stage",
    "query": "Write a song title for this image",
    "target": "Rocking Out Loud"
  },
  {
    "image_url": "https://example.com/image2.jpg",
    "caption": "A woman wearing a red dress and a hat",
    "query": "What color is her hat?",
    "target": "Her hat is red"
  },
  {
    "image_url": "https://example.com/image3.jpg",
    "caption": "A cat sitting on a sofa",
    "query": "Draw a dog sitting on a sofa",
    "target": "https://example.com/image3-dog.jpg"
  },
  ...
]
        

            


        
        

You can create your own data or use some existing datasets, such as LLaVA-IF, which contains 150K GPT-generated multimodal instruction-following examples.

To fine-tune LLaVA, you need to use the llava-finetune command with some arguments. You need to specify the path to your data file, the path to the model checkpoint, the output directory name, and some other options. For example:

llava-finetune --data_file data.json --model_file llava-13b-v1.pt --output_dir output --batch_size 16 --num_workers 4 --num_epochs 10 --learning_rate 1e-4

This command will fine-tune LLaVA on the data file data.json using the model checkpoint llava-13b-v1.pt. It will save the fine-tuned model and the logs in the output directory output. It will use a batch size of 16 and 4 workers for parallel processing. It will run for 10 epochs with a learning rate of 1e-4. You can adjust these parameters according to your hardware specifications and preferences.

Step 6: Enjoy LLaVA

After fine-tuning LLaVA, you can enjoy using it in your application. You can run LLaVA with the fine-tuned model checkpoint and see how it improves its performance and adapts to your domain or task. You can also experiment with different queries and images and see how LLaVA responds. You can also share your results and feedback with the LLaVA community and contribute to its development and improvement.

LLaVA is an exciting project that aims to develop a general-purpose visual assistant that can chat with you and follow your visual and language instructions. It is an open-source project that collaborates with the research community to advance the state-of-the-art in AI. You can use LLaVA to enhance your application with powerful visual and language capabilities and create engaging and useful multimodal assistants for everyone.