The BakLLaVA model is an exciting advancement in the world of image-to-text processing, derived from the foundational Llava architecture. This guide will walk you through using this model effectively, with some troubleshooting tips to help you along the way!

What is BakLLaVA?

The BakLLaVA model utilizes the Mistral-7b as a text backbone. This means that it can efficiently generate text based on image prompts, leveraging its state-of-the-art architecture. As a bonus, BakLLaVA-1 has shown to outperform the Llama 2 13B in various benchmarks!

How to Use the Model

To get started with BakLLaVA, you’ll need to have transformers version 4.35.3 installed. With that set, you can proceed to utilize the model for multi-image and multi-prompt generation.

Setting Up Your Environment

• Ensure you have the correct version of transformers installed.
• Follow the correct prompt template in your code: USER: xxx followed by ASSISTANT:.
• Add the token image to the specified location in your prompts.

Using the Pipeline

Here’s a simple example of how to set up and use the BakLLaVA model:

from transformers import pipeline
from PIL import Image
import requests

model_id = "llava-hf/bakLlava-v1-hf"
pipe = pipeline("image-to-text", model=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)

conversation = [
    {"role": "user", "content": [{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"}]}
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)

Using Pure Transformers

For those who prefer a more traditional approach, here’s how you can run generation with pure transformers:

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "llava-hf/bakLlava-v1-hf"
model = LlavaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(0)
processor = AutoProcessor.from_pretrained(model_id)

conversation = [
    {"role": "user", "content": [{"type": "text", "text": "What are these?"}]}
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

image_file = "http://images.cocodataset.org/val2017/000000397689.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, 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

For those looking to improve the efficiency of your model, consider utilizing:

• 4-bit quantization with the bitsandbytes library. Install it using pip install bitsandbytes.
• Flash-Attention 2 to speed up generation. Refer to the repository of Flash Attention for installation details.

Troubleshooting

If you encounter any issues while using the BakLLaVA model, here are some tips:

• Ensure that all necessary libraries are updated to their latest versions.
• Double-check the prompt format; improper formatting can lead to errors in generation.
• If you experience performance issues, consider optimizing your model using 4-bit quantization and flash-attention methods.

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

Conclusion

With BakLLaVA, transforming images into coherent text narratives has never been easier! Keep experimenting with different prompts and images to discover the full potential of this robust model.

Final Thoughts

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.