How to Use the LLaVA-Onevision Model

Welcome to our guide on using the powerful LLaVA-Onevision model, an advanced multimodal language model that seamlessly integrates various forms of media. This model can significantly enhance your projects through its capabilities in image-text understanding. In this article, we will walk you through the process of using LLaVA-Onevision, provide troubleshooting tips, and share helpful insights along the way.

Model Overview

The LLaVA-Onevision model, developed by fine-tuning Qwen2 on GPT-generated multimodal instruction data, is designed to excel in tasks involving single images, multiple images, and videos. It exhibits strong transfer learning abilities, allowing it to tackle various tasks effectively.

Getting Started

To use the LLaVA-Onevision model, you will need to have the transformers library installed. You can install it from a specified branch:

• Install Transformers: Use this link to get the right branch of transformers.

Using the Pipeline

The easiest way to implement the LLaVA-Onevision model is to utilize the pipeline functionality. Below is a step-by-step guide on how you can do this:

python
from transformers import pipeline, AutoProcessor
from PIL import Image
import requests

model_id = 'llava-hfllava-onevision-qwen2-72b-ov-hf'
pipe = pipeline('image-to-text', model=model_id)
processor = AutoProcessor.from_pretrained(model_id)

url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# Define a chat history and use apply_chat_template to get correctly formatted prompt
conversation = [{
    'role': 'user',
    'content': [{
        'type': 'text',
        'text': 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud',
        'type': 'image',
    }],
}]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
outputs = pipe(image, prompt=prompt, generate_kwargs={'max_new_tokens': 200})
print(outputs)  # This will output the generated text

In this snippet, the code runs like a well-organized kitchen where each chef knows their task:

• The processor is like the sous-chef, preparing ingredients (the chat history and images) before the main chef (the model) starts cooking (generating text).
• The pipeline is the main chef that takes the prepared ingredients, follows the recipe (the prompts), and delivers a delicious meal (the generated text output).

Using Pure Transformers

If you prefer direct control, you can access the model using PyTorch directly:

python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

model_id = 'llava-hfllava-onevision-qwen2-72b-ov-hf'
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
).to(0)

processor = AutoProcessor.from_pretrained(model_id)

# Define a chat history and use apply_chat_template to get correctly formatted prompt
conversation = [{
    'role': 'user',
    'content': [{
        'type': 'text',
        'text': 'What are these?',
        'type': 'image',
    }],
}]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

image_file = 'http://images.cocodataset.org/val2017/00000039769.jpg'
raw_image = Image.open(requests.get(image_file, stream=True).raw)

inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))

Model Optimization

Performance can be enhanced using various optimizations:

• 4-bit Quantization: Install bitsandbytes to enable efficient memory usage.
• Flash-Attention 2: For faster performance, follow the instructions on the Flash Attention repository.

Troubleshooting

If you encounter issues while using the model, consider these troubleshooting tips:

• Ensure that you have the latest versions of the required libraries installed.
• Check that your GPU configuration conforms to the requirements for running models in float16 or 4-bit modes.
• Verify that your prompts are formatted correctly to avoid runtime errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Running the LLaVA-Onevision model can significantly enhance your multimedia projects. With various methods for implementation and optimization, you can tailor your approach to fit your specific needs. 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.