Llama-3.2-11B-Vision-Instruct
Maintainer: meta-llama
447
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The Llama-3.2-11B-Vision-Instruct is a multimodal large language model (LLM) developed by Meta that is optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. It is part of the Llama 3.2-Vision collection, which also includes a larger 90B version. The models are built on top of the Llama 3.1 text-only model, using a separately trained vision adapter to integrate image inputs.
Model inputs and outputs
Inputs
- Text: The model can take text prompts as input, such as questions or descriptions about an image.
- Image: The model can also take image inputs, which it uses in combination with the text prompts for tasks like visual reasoning and image captioning.
Outputs
- Text: The primary output of the
Llama-3.2-11B-Vision-Instructmodel is text, such as answers to questions or descriptions of images.
Capabilities
The Llama-3.2-11B-Vision-Instruct model excels at multimodal tasks that combine vision and language, outperforming many other open-source and closed-source models on benchmarks like Visual Question Answering (VQA) and Document Visual Question Answering (DocVQA). It can understand the content and layout of images and documents, and then answer questions about them.
What can I use it for?
The Llama-3.2-11B-Vision-Instruct model is intended for commercial and research use cases that involve combining images and text, such as:
- Visual Question Answering: Building AI assistants that can look at images and understand and answer questions about them.
- Document Visual Question Answering: Developing systems that can understand the text and layout of documents like maps or contracts, and answer questions about them directly from the image.
- Image Captioning: Generating natural language descriptions of images that capture the key details and storyline.
- Image-Text Retrieval: Building search engines that can match images to their corresponding text descriptions.
- Visual Grounding: Connecting language references to specific parts of an image, allowing AI models to identify objects or regions based on natural language.
Things to try
One interesting capability of the Llama-3.2-11B-Vision-Instruct model is its ability to generate creative responses to prompts that combine text and images. For example, you could try asking it to write a haiku poem inspired by a given image, and see how it weaves the visual details into a concise poetic form.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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