Have you ever wondered how advanced AI models can interpret images and respond to queries about them? Introducing Multi-Crop LLaVA-3b, an innovative model that allows AI to extract visual information from various parts of an image by creating multiple tokens for those segments. This tutorial will walk you through using the Multi-Crop LLaVA-3b model with some user-friendly steps.

Understanding Multi-Crop LLaVA

Imagine you have a garden with a variety of flowers, and you want to write a poem about each flower individually rather than just a single poem about the entire garden. Instead of creating all your sentiments based on just one perspective of the garden, you observe multiple angles and specific flowers, gathering detailed information that enriches your final piece. This is exactly how the Multi-Crop LLaVA model works by dissecting images into several smaller parts (or crops), analyzing them individually, and combining the insights to generate accurate responses.

Getting Started with Multi-Crop LLaVA-3b

Follow these easy steps to implement Multi-Crop LLaVA-3b in your Python environment:

• Requirements: Ensure you have the necessary libraries installed, especially the transformers library.
• Import Necessary Modules: Start with importing the required classes.

from transformers import AutoModel, AutoProcessor
import torch

• Load the Model: Initialize the model and processor.

model = AutoModel.from_pretrained("visheratin/MC-LLaVA-3b", torch_dtype=torch.float16, trust_remote_code=True).to("cuda")
processor = AutoProcessor.from_pretrained("visheratin/MC-LLaVA-3b", trust_remote_code=True)

• Process Inputs: Prepare your inputs with the image and prompt text.

with torch.inference_mode():
    inputs = processor(prompt, [raw_image], model, max_crops=100, num_tokens=728)
    output = model.generate(**inputs, max_new_tokens=200, use_cache=True, do_sample=False,
        eos_token_id=processor.tokenizer.eos_token_id, pad_token_id=processor.tokenizer.eos_token_id)

• Decode and Print Result: Retrieve and display the model’s output.

result = processor.tokenizer.decode(output[0]).replace(prompt, "").replace(im_end, "")
print(result)

Benchmarks

The performance of the Multi-Crop LLaVA model has been impressive in various benchmarks:

• TextVQA: 50.9%
• GQA: 59.5%
• VQAv2: 76.72%
• VizWiz: 32.68%
• V*-bench (OCR): 56.66%, GPT4V-hard: 52.94%, direct attributes: 40.86%, relative position: 56.57%

Troubleshooting

If you encounter any issues while using Multi-Crop LLaVA-3b, here are some troubleshooting tips:

• Ensure all necessary libraries are correctly installed and updated to the latest version.
• Check if your GPU is compatible and properly set up, especially for CUDA usage.
• If the model is not generating output, inspect your prompt and image inputs for correctness.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

License and Acknowledgments

The Multi-Crop LLaVA-3b model is licensed under the MIT license. It is crucial to adhere to the terms laid out by OpenAI and Google Gemini regarding the use of synthetic data for model training.

Thanks to Lambda for providing the necessary resources for model training and to ML Collective for ongoing support.

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.

Conclusion

Multi-Crop LLaVA-3b is a game-changer in the world of AI image understanding. By following these steps and guidelines, you can harness its power to gain detailed insights from images. Happy coding!