With the introduction of the Llama 3.1-8B Vision model, you can add vision capabilities to your Llama model. In this article, we’ll explore how to utilize this powerful model using Python. Follow these simple steps to get started!

Getting Started

To use the Llama 3.1-8B Vision model, you’ll need to follow a two-part usage guideline: loading the model and performing image recognition. Below are the steps involved.

1. Install Required Libraries

• Make sure you have the required libraries installed:
• PIL (for image processing)
• Transformers (for the model and tokenizer)
• Requests (to fetch the images)

2. Load and Use the Model

The following Python code snippet demonstrates how to load the model and use it to answer questions about an image:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import requests
from io import BytesIO

# Fetching the image
url = "https://huggingface.co/qresearch/llama-3-vision-alpha-hf/resolve/main/assets/demo-2.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content))

# Loading the model
model = AutoModelForCausalLM.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    trust_remote_code=True,
    torch_dtype=torch.float16,
).to("cuda")

tokenizer = AutoTokenizer.from_pretrained("qresearch/llama-3.1-8B-vision-378", use_fast=True)

# Answering a question about the image
print(
    model.answer_question(
        image,
        "Briefly describe the image",
        tokenizer,
        max_new_tokens=128,
        do_sample=True,
        temperature=0.3
    ),
)

Think of the model as a highly trained tour guide who is able to explain the details of any artwork (image) in front of them.

Quantization: Optimizing Performance

In addition to image recognition, you may want to optimize performance using 4-bit quantization. Here’s how you can achieve that:

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
import requests
from io import BytesIO

# Fetching the image
url = "https://huggingface.co/qresearch/llama-3-vision-alpha-hf/resolve/main/assets/demo-2.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content))

# Configuring quantization
bnb_cfg = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    llm_int8_skip_modules=["mm_projector", "vision_model"],
)

# Loading the model with quantization configuration
model = AutoModelForCausalLM.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    quantization_config=bnb_cfg,
)

tokenizer = AutoTokenizer.from_pretrained(
    "qresearch/llama-3.1-8B-vision-378",
    use_fast=True,
)

# Answering a question about the image
print(
    model.answer_question(
        image,
        "Briefly describe the image",
        tokenizer,
        max_new_tokens=128,
        do_sample=True,
        temperature=0.3
    ),
)

Imagine the quantization as a travel plan that helps the tour guide move around more efficiently, allowing them to provide the same quality of explanation with less effort.

Troubleshooting

If you encounter any issues while implementing the model, here are some troubleshooting tips:

• Check Dependencies: Ensure all required Python packages are installed and updated.
• CUDA Issues: Make sure your CUDA is properly installed and compatible with your hardware.
• Model Loading Errors: Verify the model names and URLs to ensure they are correct.
• Image Loading: Confirm that the image URL is reachable and correctly formatted.
• Performance Lag: Try using the quantization feature to improve performance.

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

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.