- Developers & Engineers: Build multimodal apps like visual assistants, document parsers, and image Q&A tools.
- Analysts & Researchers: Automate chart analysis, document image understanding, and multimodal content summarization.
- Educators & Students: Solve visual math problems, analyze diagrams, and work with text-image inputs in education.
- Enterprises & Teams: Deploy large-context QA systems, OCR pipelines, and image-based chat assistants via API/cloud.
- Open-Source & Edge Advocates: Innovate on a transparent multimodal foundation model with expansive support.
How to Use Llama 3.2 Vision?
- Select Model Size: Choose 11B or 90B based on your compute and accuracy needs.
- Deploy via Platforms: Available on Hugging Face, Oracle OCI, AWS Bedrock, Databricks, Vertex AI, Ollama, and local setups.
- Submit Image+Text: Send mixed prompts—images plus text—within 128K-token context for reasoning or captioning.
- Perform Vision Tasks: Handle image captioning, visual QA (VQAv2), chart or diagram interpretation (ChartQA, DocVQA), and photoreal understanding.
- Optimize Inference: Use grouped-query attention (GQA), quantization, and efficient pipelines—edge variants available for low-latency use.
- Vision Excellence: Achieves top-tier scores—DocVQA
70.7% and AI2 Diagram75.3% (11B) or90.1% &92.3% (90B). - Visual Math & Charts: ChartQA
85.5% and MathVista57.3% with chain-of-thought reasoning. - Massive Context Window: 128K tokens for long-form, multimodal workflows.
- Open-Source Availability: Licensed under Meta’s Community License; commercial-friendly with some usage restrictions.
- Wide Platform Reach: Available across major cloud & local platforms—accessible to developers everywhere.
- Outstanding vision reasoning benchmarks in open-source models
- Large context supports document and image workflows
- Multimodal in a single pipeline—no separate vision endpoint
- Available on multiple platforms, from cloud to edge
- Efficient inference via GQA and quantization options
- Vision focused—doesn’t support audio or video modalities
- In-context window, though large, may still limit ultra-long docs
- 90 B variant requires heavier compute resources
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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