Welcome to the world of advanced AI models with BakLLaVA! Based on the original Llava architecture and enhanced with the Mistral-7b backbone, BakLLaVA opens the door to exceptional capabilities in image-text processing. In this article, we’ll walk you through the steps to utilize this powerful model effectively.

Understanding BakLLaVA

Imagine BakLLaVA as a skilled chef in a kitchen, combining the finest ingredients (text and image data) to create a sumptuous dish (rich output) that satisfies diverse appetites (user queries). The first version demonstrates that with the right mix, a Mistral 7B base can surpass larger models like Llama 2 13B in various benchmarks.

Getting Started with BakLLaVA

Before diving into the implementation, ensure you have the latest version of transformers (4.35.3) installed. This model supports multiple image and prompt generations, allowing for versatile interactions.

Steps to Use BakLLaVA

• Set Up Your Environment: Make sure you have the required libraries installed.
• Access the Model: You can find BakLLaVA at its repository on GitHub.
• Follow the Prompt Template: Structure your inputs according to the format USER: xxx n ASSISTANT:. Insert the image token where relevant.
• Run the Model: Execute the pipeline or use pure transformers as shown in the examples below.

Example Code for Using the Pipeline

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"}, {"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)

Using 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?"}, {"type": "image"}]},
]
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))

Optimizing the Model

To further enhance performance, consider various optimization techniques:

• 4-Bit Quantization: Install bitsandbytes and modify your model-loading snippet to include load_in_4bit=True.
• Use Flash-Attention: Following the guidelines from the Flash Attention repository, you can implement use_flash_attention_2=True.

Troubleshooting

If you encounter issues when running BakLLaVA, consider the following troubleshooting steps:

• Verify that all required libraries are correctly installed and updated.
• Check your input formats, ensuring you adhere to the required structure.
• Run tests with simple images and prompts to isolate the issue.

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

Conclusion

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.