Llama 3.2 90B Vision
Llama 3.2 90B Vision is a powerful AI model that can understand and generate text based on images. It's designed to recognize objects, reason about images, and answer questions about what it sees. This model is part of a larger collection of multimodal large language models that can handle both text and images as inputs. What makes Llama 3.2 90B Vision unique is its ability to learn from a massive dataset of 6 billion image and text pairs, and its use of a separately trained vision adapter to improve its image recognition capabilities. With its high accuracy and efficiency, this model can be used for a variety of applications, such as visual question answering, image captioning, and document analysis. It's also designed to be safe and responsible, with built-in safety mitigations to prevent misuse. Whether you're a developer or researcher, Llama 3.2 90B Vision is a valuable resource for exploring the possibilities of multimodal AI.
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 process both text and images. This model is designed to perform various tasks such as visual recognition, image reasoning, captioning, and answering general questions about an image.
Capabilities
The Llama 3.2-Vision model is a powerful tool that can understand and generate text based on images. It’s capable of:
- Visual recognition: identifying objects, scenes, and actions in images
- Image reasoning: answering questions about images and understanding the relationships between objects
- Captioning: generating text that describes an image
- Visual question answering: answering questions about images, such as “What is the color of the car in the picture?”
This model is also great at:
- Document visual question answering: understanding the layout and content of documents, such as maps or contracts, and answering questions about them
- Image-text retrieval: finding images that match a given text description
- Visual grounding: understanding how language references specific parts of an image
Strengths
The Llama 3.2-Vision model has several strengths that make it stand out:
- Multimodal capabilities: it can understand and generate both text and images
- Large training dataset: it was trained on a massive dataset of 6 billion image and text pairs
- High accuracy: it outperforms many other models on common industry benchmarks
Technical Details
- Model architecture: The model is built on top of the Llama 3.1 text-only model and uses an optimized transformer architecture.
- Training data: The model was trained on a large dataset of 6B image and text pairs.
- Training energy use: The model was trained using a cumulative of 2.02M GPU hours of computation.
Performance
The Llama 3.2-Vision model is a powerhouse when it comes to processing images and text. But how does it perform in various tasks? Let’s dive in and explore its speed, accuracy, and efficiency.
- Speed: The model’s architecture is designed for efficient processing, allowing for fast generation of text based on images and prompts.
- Accuracy: The model achieves impressive accuracy in image understanding tasks, such as:
- VQAv2 (val): 66.8% (11B) and 73.6% (90B)
- Text VQA (val): 73.1% (11B) and 73.5% (90B)
- DocVQA (val, unseen): 62.3% (11B) and 70.7% (90B)
Use Cases
The Llama 3.2-Vision model can be used in a variety of applications, such as:
- Visual question answering: answering questions about images, such as “What is the color of the car in the picture?”
- Image captioning: generating text that describes an image
- Document visual question answering: understanding the layout and content of documents, such as maps or contracts, and answering questions about them
- Image-text retrieval: finding images that match a given text description
Limitations
The Llama 3.2-Vision model is not perfect. Let’s explore some of its limitations.
- Limited language support: While the model has been trained on a broad range of languages, it only officially supports eight languages for text-only tasks: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- Data freshness: The pretraining data for the model has a cutoff of December 2023, which means that the model may not be aware of events or developments that have occurred after that date.
- Energy consumption: Training the model required significant amounts of energy, with an estimated 584 tons CO2eq of location-based greenhouse gas emissions.
