Llama 3.2 11B Vision
Llama 3.2 11B Vision is Meta's mid-size multimodal model designed to understand both text and images without the cost overhead of larger vision models. It processes image-and-text inputs to answer questions, describe visual content, and extract information from screenshots or documents.
At 11 billion parameters, it strikes a practical balance for teams deploying vision-language workloads at scale. It performs well on visual question answering, image captioning, and document understanding tasks, making it a sensible choice when GPT-4V-class performance is not strictly required but image comprehension is essential.
Key Features
Multimodal input: accepts both images and text in a single prompt
Visual question answering across photographs, charts, and diagrams
Document and screenshot understanding for extracting structured information
Instruction-following tuned for practical, open-ended visual tasks
Open-weight release allowing self-hosting and fine-tuning
Efficient inference footprint relative to larger 90B vision variant
Ideal Use Cases
Automating image description and alt-text generation for accessibility pipelines
Extracting data from scanned invoices, receipts, or forms
Building customer-facing chatbots that accept image uploads
Visual content moderation as a first-pass classifier
Research tooling where a self-hosted vision model is preferred
Example Prompts for Llama 3.2 11B Vision
Technical Specifications
| Provider | Meta |
| Category | Text |
| Modality | Text -> Text |
| Context Window | 128K tokens |
Frequently Asked Questions
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