Welcome to another exciting guide on leveraging advanced AI models! Today, we will explore how to use the LLaVA-LLaMA-3-8B model, an innovative adaptation of the LLaVA framework, built upon the Llama-3-8B Large Language Model (LLM). This guide will provide you with a step-by-step approach to getting started with this powerful model.

Understanding the LLaVA Model

The LLaVA-LLaMA-3-8B is designed to enhance image processing and text generation tasks by utilizing the strengths of the Llama-3-8B LLM. Think of it as a versatile chef capable of cooking a wide array of dishes (text outputs) by expertly combining ingredients (image data) on its menu. In simpler terms, it can interpret images and generate detailed textual descriptions, making it immensely useful for numerous applications.

Installation Steps

Before diving into using the model, you first need to install it. Follow these steps:

• Open your command-line interface.
• Run the following command to install the LLaVA repository:

  pip install git+https://github.com/Victorwz/LLaVA-Llama-3.git

Loading the Model and Performing Inference

After successfully installing the model, you can proceed with loading it and making predictions. Here’s how you can do it:

• Import the necessary libraries:

from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import requests
import torch
from io import BytesIO

• Load the model and processor:

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = get_model_name_from_path('weizhiwang/LLaVA-Llama-3-8B')
tokenizer, model, image_processor, context_len = load_pretrained_model(model_name, None, None, False, False, device=device)

• Prepare inputs for the model:

text = "image + n + Describe the image."
conv = conv_templates['llama_3'].copy()
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()

• Prepare the image input:

url = "https://huggingface.co/adeptfuyu-8b/resolvemainbus.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()

• Generate text autoregressively:

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=image_tensor,
        do_sample=False,
        max_new_tokens=512,
        use_cache=True
    )
outputs = tokenizer.batch_decode(output_ids[:, input_ids.shape[1]:], skip_special_tokens=True)
print(outputs[0])

Example Output

After loading and processing an image, the model produces descriptive text. For instance, it might provide an output like:

"The image features a blue and orange double-decker bus parked on a street..."

Troubleshooting

If you encounter any issues, consider the following troubleshooting tips:

• Ensure that all libraries, particularly PyTorch, are correctly installed and compatible with your environment.
• Check your internet connection, especially when loading images from URLs.
• If the model fails to load, verify that the model name is correctly specified and available in the source repository.
• Consult the GitHub repository for updates and community support.

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

Fine-Tuning the Model

If you want to fine-tune the LLaVA-Llama-3 on your visual instruction data, please refer to a forked LLaVA-Llama-3 GitHub repository for data preparation and scripts. Adjustments may be needed in the data loading function and conversation templates due to a different tokenizer.

Performance Benchmarking

The LLaVA-Llama-3-8B has shown promising benchmark results, outperforming its predecessor:

• LLaVA-v1.5-7B: 35.3
• LLaVA-Llama-3-8B: 36.7

For detailed evaluation performance, refer to eval_outputs/LLaVA-Llama-3-8B_mmmu_val.json.

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.