Llama-3.2-11B-Vision-Instruct
Excels in image reasoning capabilities on high-res images for visual understanding apps.Meta
MetaVersion: 6
Models from Microsoft, Partners, and Community models are a select portfolio of curated models both general-purpose and niche models across diverse scenarios by developed by Microsoft teams, partners, and community contributors
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About this model
The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image.Key model capabilities
- Visual Question Answering (VQA) and Visual Reasoning: Imagine a machine that looks at a picture and understands your questions about it.
- Document Visual Question Answering (DocVQA): Imagine a computer understanding both the text and layout of a document, like a map or contract, and then answering questions about it directly from the image.
- Image Captioning: Image captioning bridges the gap between vision and language, extracting details, understanding the scene, and then crafting a sentence or two that tells the story.
- Image-Text Retrieval: Image-text retrieval is like a matchmaker for images and their descriptions. Similar to a search engine but one that understands both pictures and words.
- Visual Grounding: Visual grounding is like connecting the dots between what we see and say. It's about understanding how language references specific parts of an image, allowing AI models to pinpoint objects or regions based on natural language descriptions.
Quick facts
Model providerMeta
TypeChat completion
LifecycleGenerally available (GA)
Input typetext, image, audio
Output typetext
Context window128k
Token limits4096 output
PricingView pricing