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Nim/meta/llama-3.2-11b-vision-instructVision Meets Language

Try Nim/meta/llama-3.2-11b-vision-instructAPI Documentation

Process Images with Text

Multimodal Input

Text and Images

Handle text+image inputs for text outputs using nim/meta/llama-3.2-11b-vision-instruct model.

Image Reasoning

Visual Question Answering

Answer questions about images, charts, and documents with nim/meta/llama-3.2-11b-vision-instruct API.

Compact Power

11B Parameters

Deploy 11 billion parameter nim meta llama 3.2 11b vision instruct as nim/meta/llama-3.2-11b-vision-instruct alternative.

Examples

See what Nim/meta/llama-3.2-11b-vision-instruct can create

Copy any prompt below and try it yourself in the playground.

Chart Analysis

“<image>Analyze this sales chart. Identify the peak month and its value.”

Document QA

“<image>Extract key entities from this invoice image, including date, total, and items.”

Image Caption

“<image>Provide a detailed caption describing the scene, objects, and actions in this image.”

Visual Reasoning

“<image>What objects are present? Describe their positions and relationships.”

For Developers

A few lines of code.
Vision reasoning. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Nim/meta/llama-3.2-11b-vision-instruct

Read the docs

It is an 11B parameter multimodal LLM from Meta that processes text and images for text output. Optimized for image reasoning, captioning, and VQA. Supports 128k context length.

Send text and image inputs via API to get text responses. Uses vision adapter with cross-attention layers. Available through NIM for easy integration.

Excels in document VQA, image captioning, visual reasoning, and chart analysis. Handles college-level math problems from images.

Supports English, French, German, Hindi, Italian, Portuguese, Spanish, Thai. Trained on 6B image-text pairs with December 2023 cutoff.

This NIM-optimized model offers compact vision capabilities versus larger 90B version. Ideal for edge deployment and API use.

Use standard LLM endpoints with image URLs in messages. Compatible with Together AI and similar platforms for inference.

Ready to create?

Start generating with Nim/meta/llama-3.2-11b-vision-instruct on ModelsLab.

Try Nim/meta/llama-3.2-11b-vision-instructAPI Documentation