Meta: Llama 3.2 11B Vision Instruct

Llama 3.2 11B Vision Instruct is a powerful multimodal model developed by Meta. It excels in visual recognition, image reasoning, and captioning tasks. The model has 11 billion parameters and is optimized for understanding images and generating relevant text. It can answer questions about images and create detailed captions. This model is designed for various applications, including chatbots and image analysis.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "meta-llama/llama-3.2-11b-vision-instruct",
    messages: [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What's in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            }
          }
        ]
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="meta-llama/llama-3.2-11b-vision-instruct",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What's in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
          }
        }
      ]
    }
  ]
)
print(completion.choices[0].message.content)

Meta: Llama 3.2 11B Vision Instruct

Context131072

Input$0.055 / M

Output$0.055 / M

Try Meta: Llama 3.2 11B Vision Instruct

Let's chat with Meta: Llama 3.2 11B Vision Instruct now and verify the model's response effectiveness to your questions.

What can I do for you？

Description

Llama 3.2 11B Vision Instruct is a new model from a leading provider. It was launched in September 2024. This model has 11 billion parameters. It is designed to handle both visual and text data.

Llama 3.2 excels in tasks like image captioning. It also performs well in visual question answering. The model can recognize images and reason about them. It can process up to 128,000 tokens, which is great for long conversations.

In pre-training, it used 6 billion image-text pairs. This makes it ready for complex questions. The model's performance is strong. In tests, it scored 66.8% on the VQA v2 validation set.

It also achieved 73.1% on text VQA tasks. This shows it understands visual information well. The model includes a vision adapter. This feature boosts its ability to reason with visual data.

Developers can use Llama 3.2 for many applications. It works well for chatbots and image analysis tools. The model supports English, Spanish, and French. This makes it useful for a wider audience.

If you need a powerful multimodal solution, Llama 3.2 is a top choice. Use our AIAPILAB services to integrate this model and get better deals.

Model API Use Case

The Llama 3.2 11B Vision Instruct API is great at handling different types of tasks. It combines visual and text data effectively. It can recognize images and understand what they show. Then, it can create captions that describe the images.

For example, a developer might use this API to build a chatbot. This chatbot could answer questions about pictures. In a real-world setting, a retail company could use the API to look at product images. 

The model has been trained with 6 billion image-text pairs. This training helps it perform well in visual question answering. It has impressive scores, like 75.2% for general tasks.

Imagine a user uploads a picture of a dress. They might ask, "What is the color and style?" The API quickly analyzes the image. It could respond, "This is a red floral dress, perfect for summer outings."

It can also handle tasks like answering questions from scanned documents. This ability is useful for many businesses. It is valuable for areas like e-commerce and customer service. 

For more information, visit [Dataloop](https://dataloop.ai/library/model/meta-llama_llama-32-11b-vision-instruct/).

Model Review

Pros

1. Llama 3.2 11B Vision Instruct deftly merges text and image inputs for rich interactions. 2. It excels at visual reasoning, accurately interpreting complex images and generating insights. 3. The model processes up to 128,000 tokens, allowing for extensive and detailed conversations. 4. With high accuracy on benchmarks, it reliably delivers precise answers to visual questions. 5. It supports multiple languages, making it accessible for diverse users and applications.

Cons

1. The model struggles with multiple image inputs, limiting its versatility in complex tasks. 2. It may produce biased or inaccurate responses due to the training data's inherent flaws. 3. The model demands significant computational resources, making deployment challenging for some users.

Comparison

Feature/Aspect	Model A (e.g., GPT-4)	Model B (e.g., Google Gemini)	Meta: Llama 3.2 11B Vision Instruct
Parameters	175 billion	70 billion	11 billion
Training Data	Trained on diverse datasets, including web text	Trained on large-scale multilingual datasets	Pretrained on 6 billion image-text pairs
Context Length	Up to 32k tokens	Up to 64k tokens	Up to 128k tokens
Input Modality	Text only	Text + Image	Text + Image
Primary Use Cases	General Text Generation, Conversational AI	Multimodal Tasks, Visual Reasoning	Visual Question Answering, Image Captioning

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "meta-llama/llama-3.2-11b-vision-instruct",
    messages: [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What's in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            }
          }
        ]
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="meta-llama/llama-3.2-11b-vision-instruct",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What's in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
          }
        }
      ]
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1: What tasks can Llama 3.2 11B Vision Instruct perform?  
A1: Llama 3.2 excels in image captioning and visual question answering.

Q2: How does Llama 3.2 handle images?  
A2: Llama 3.2 processes images alongside text for multimodal tasks.

Q3: What input formats does Llama 3.2 accept?  
A3: Llama 3.2 accepts text and images as input for generating responses.

Q4: How can I deploy Llama 3.2 11B Vision Instruct?  
A4: You can deploy Llama 3.2 via cloud platforms like Azure or AWS.

Q5: What languages does Llama 3.2 support?  
A5: Llama 3.2 supports multiple languages, primarily English for image tasks.