Llama 3.2 11B Vision
Llama 3.2-Vision is a series of multimodal large language models developed by Meta, available in 11B and 90B scales, supporting image + text input and text output, optimized for visual recognition, image reasoning, image captioning, and visual question answering tasks.
Downloads 31.12k
Release Time : 9/18/2024
Model Overview
This series of models is built upon the pure-text model Llama 3.1, achieving alignment with human preferences through supervised fine-tuning and reinforcement learning from human feedback. Visual capabilities are enabled via an independently trained vision adapter.
Model Features
Multimodal Capability
Supports joint input of images and text, capable of understanding and generating text related to images.
Large-Scale Pretraining
Pretrained on 6 billion image-text pairs, equipped with strong visual and language understanding capabilities.
Instruction Tuning Optimization
Optimized for visual recognition, image reasoning, and other tasks through instruction tuning with 3 million synthetic samples.
Long Context Support
Supports 128k context length, suitable for handling complex multimodal tasks.
Safety Measures
Includes a three-layer protection strategy and specialized risk assessment to ensure safe model usage.
Model Capabilities
Visual Question Answering
Image Reasoning
Image Captioning
Image-Text Retrieval
Visual Grounding
Multilingual Text Processing
Use Cases
Education
University-Level Visual Reasoning
Used to solve university-level visual reasoning problems.
Achieved 50.7% (11B model) and 60.3% (90B model) accuracy on the MMMU-val test set.
Business
Chart Understanding
Used to comprehend and interpret data in business charts.
Achieved 83.4% (11B model) and 85.5% (90B model) accuracy on the ChartQA-test test set.
General
General Visual Question Answering
Used to answer various questions related to images.
Achieved 75.2% (11B model) and 78.1% (90B model) accuracy on the VQAv2-test test set.
Multilingual
Multilingual Text Processing
Used to handle text tasks in multiple languages.
Achieved 68.9% (11B model) and 86.9% (90B model) accuracy on the MGSM-CoT test set.
## 🚀 Transformers Library for Image-Text-to-Text
This library, `transformers`, falls under the `image-text-to-text` pipeline tag. It is associated with well - known entities like Facebook, Meta, and uses technologies such as PyTorch. It also has connections with the powerful Llama and Llama - 3 models.
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This README has been beautified by adding a proper title section and emoji for better visual hierarchy. The content is kept as it is since the original language is English, and the main focus was on enhancing the structure and readability.
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