Meta: Llama 3.2 90B Vision Instruct

Llama 3.2 90B Vision Instruct is a powerful multimodal AI model developed by Meta. It has 90 billion parameters and excels in visual reasoning and language tasks. This model is capable of understanding images and text, making it ideal for tasks like image captioning and visual question answering. Trained on extensive datasets, it offers high accuracy and efficiency.

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-3.2-90b-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-3.2-90b-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 90B Vision Instruct

Context131072

Input$0.9 / M

Output$0.9 / M

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Description

Meta's Llama 3.2 90B Vision Instruct is a new model. It was released on September 25, 2024. This model has an impressive 90 billion parameters. It is designed for tasks that involve both visual and language skills.

Llama 3.2 Vision can handle text and image inputs. It uses a strong transformer architecture for better performance. This model can manage a context length of up to 128,000 tokens. This allows it to work with long conversations and complicated questions.

The model has been fine-tuned with human feedback. This makes it safer and more useful. It scores 78.1% on the VQAv2 benchmark. It also scores 90.1% on the DocVQA benchmark.

Its abilities include recognizing objects and actions. It can also describe images with sentences. Additionally, it can answer questions about document layouts.

Llama 3.2 Vision supports many languages. These include English, German, French, and Spanish, among others. It has been trained on 6 billion image-text pairs. This training helps it understand various contexts better.

Developers can use Llama 3.2 90B Vision Instruct for many purposes. It can improve workflows, research, and user experiences. For the best deals, use our AIAPILAB services to integrate this model.

Model API Use Case

The Llama 3.2 90B Vision Instruct API is great at recognizing images. It also handles image reasoning and captioning tasks well. This makes it useful for many applications. For example, in a store, it can look at product images. It extracts details like color, size, and brand. This helps create organized data for online shopping.

In tests, the model scored 78.1% in answering visual questions. It did even better in document questions, with a score of 90.1%. The API can process a large amount of text, up to 128,000 tokens. This allows it to manage tricky questions effectively.

Imagine a user uploads a financial report image. The API can spot trends and answer questions about the data. It can also create captions that summarize the content. This boosts productivity in areas like finance and marketing.

Additionally, the API supports multiple languages. It can work with text in English, German, and Spanish. By using this API, businesses can automate data extraction. This leads to better customer interaction through smart visual reasoning.

Model Review

Pros

1. Llama 3.2 90B Vision Instruct captures intricate visual details effectively. 2. It deciphers complex images, answering questions with precision. 3. The model generates vivid captions, enhancing image understanding. 4. Developers can adapt it for various applications, boosting productivity. 5. Its multilingual support broadens accessibility, catering to diverse users.

Cons

1. The model struggles with low-quality or distorted images, affecting accuracy. 2. Limited language support hampers usability outside the major languages. 3. High computational demands may limit accessibility for smaller developers.

Comparison

Feature/Aspect	Model A (e.g., GPT-4)	Model B (e.g., Gemini)	Llama 3.2 90B Vision Instruct
Parameters	175 billion	70 billion	90 billion
Context Length	32,000 tokens	64,000 tokens	128,000 tokens
Supported Modalities	Text only	Text and Image	Text and Image
Fine-tuning Techniques	RLHF and instruction tuning	Pretraining with diverse datasets	Supervised fine-tuning and RLHF
Performance Benchmarking	Strong performance in general tasks	Competitive in visual tasks	High accuracy in VQA, 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-3.2-90b-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-3.2-90b-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 90B Vision Instruct perform?  
A1: It excels in image captioning, visual question answering, and image reasoning.

Q2: How does Llama 3.2 handle images and text?  
A2: It integrates image data with text inputs for advanced multimodal processing.

Q3: What is the context window size for Llama 3.2?  
A3: The model supports a context window of 128,000 tokens.

Q4: What languages does Llama 3.2 support?  
A4: It officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Q5: How can developers fine-tune Llama 3.2?  
A5: Developers can fine-tune it with their datasets using provided tools and APIs.