Llama 3.2 90B Vision Instruct
The Llama 3.2 90B Vision Instruct model is a powerful tool for visual recognition, image reasoning, and captioning. It's built on top of the Llama 3.1 text-only model and uses a separately trained vision adapter to integrate with the pre-trained Llama 3.1 language model. This allows it to take images and text as inputs and provide accurate results. But what makes this model unique? It's optimized for visual recognition and image reasoning, making it perfect for tasks like visual question answering, document visual question answering, and image captioning. It's also designed to be safe and responsible, with a focus on protecting developers and the community from potential misuse. So, what can you do with this model? You can use it for a variety of applications, from commercial and research use to visual question answering and image-text retrieval. With its ability to take images and text as inputs, the possibilities are endless.
Table of Contents
Model Overview
The Llama 3.2-Vision model, developed by Meta, is a collection of multimodal large language models (LLMs) that can understand and respond to both images and text. These models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image.
Capabilities
The Llama 3.2-Vision model is a powerful tool for image recognition, image reasoning, captioning, and answering general questions about an image. It’s like having a machine that can look at a picture and understand your questions about it.
What can it do?
- Visual Recognition: It can identify objects, scenes, and actions in an image.
- Image Reasoning: It can answer questions about an image, like “What’s happening in this picture?”
- Captioning: It can generate a sentence or two that describes an image.
- Visual Question Answering (VQA): It can answer questions about an image, like “What’s the color of the car in this picture?”
- Document Visual Question Answering (DocVQA): It can understand both the text and layout of a document, like a map or contract, and answer questions about it.
- Image-Text Retrieval: It can find images that match a given text description.
- Visual Grounding: It can understand how language references specific parts of an image.
Performance
Llama 3.2-Vision is a powerhouse when it comes to handling image and text data. Let’s dive into its performance and see what makes it stand out.
Speed
How fast can Llama 3.2-Vision process image and text data? With its optimized transformer architecture and separately trained vision adapter, it can handle large-scale datasets with ease. But what does that mean in terms of numbers? Let’s take a look:
| Model | Training Time (GPU hours) |
|---|---|
| Llama 3.2-Vision 11B | 1.47M |
| Llama 3.2-Vision 90B | 8.85M |
As you can see, Llama 3.2-Vision is trained on a massive scale, with the 90B model taking a whopping 8.85M GPU hours to train. But don’t worry, that’s just a testament to its ability to handle complex tasks.
Accuracy
But speed is just one part of the equation. How accurate is Llama 3.2-Vision when it comes to image and text tasks? Let’s take a look at some benchmarks:
| Benchmark | Llama 3.2 11B | Llama 3.2 90B |
|---|---|---|
| VQAv2 (val) | 66.8 | 73.6 |
| Text VQA (val) | 73.1 | 73.5 |
| DocVQA (val, unseen) | 62.3 | 70.7 |
As you can see, Llama 3.2-Vision consistently outperforms other models in various image and text tasks. But what about its performance in specific areas like visual reasoning and chart understanding?
Limitations
Llama 3.2-Vision is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Limited Language Support
While Llama 3.2-Vision can understand and generate text in multiple languages, its official support is limited to English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. If you need to use the model with other languages, you’ll need to fine-tune it yourself, which can be a challenge.
Image Recognition Limitations
Llama 3.2-Vision is great at recognizing images, but it’s not foolproof. It may struggle with:
- Low-quality or distorted images
- Images with complex or abstract concepts
- Images with multiple objects or scenes
Data Limitations
The model was trained on a large dataset, but it’s not exhaustive. It may not perform well on:
- Images or text that are not well-represented in the training data
- Sarcasm, humor, or other forms of nuanced language
- Domain-specific knowledge or technical jargon
Safety and Responsibility
As with any AI model, there are concerns about safety and responsibility. Llama 3.2-Vision may:
- Generate biased or discriminatory content
- Be used for malicious purposes, such as spreading misinformation
- Require careful deployment and monitoring to ensure safe and responsible use
Technical Limitations
Finally, Llama 3.2-Vision has some technical limitations, including:
- High computational requirements for training and inference
- Limited support for certain input formats or modalities
- Potential for overfitting or underfitting, depending on the specific use case
Format
Llama 3.2-Vision is a multimodal large language model that accepts input in the form of text and images. It uses a transformer architecture and is optimized for visual recognition, image reasoning, captioning, and answering general questions about an image.
Model Architecture
Llama 3.2-Vision is built on top of the Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM.
Data Formats
Llama 3.2-Vision accepts input in the form of:
- Text: tokenized text sequences
- Images: image files in various formats (e.g. JPEG, PNG)
Input Requirements
- Text input: tokenized text sequences with a maximum length of 128k tokens
- Image input: image files with a maximum resolution of
1.8M pixels
Output Format
- Text output: generated text sequences
Example Code
Here’s an example of how to use Llama 3.2-Vision with the transformers library:
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
model_id = "meta-llama/Llama-3.2-90B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "If I had to write a haiku for this one, it would be: "}]}]
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(image, input_text, add_special_tokens=False, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=30)
print(processor.decode(output[0]))
Note that this example assumes you have the transformers library installed and have downloaded the Llama 3.2-Vision model.
