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Instruction-tuned image reasoning model from Meta with 11B parameters. Optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The model can understand visual data, such as charts and graphs and also bridge the gap between vision and language by generating text to describe images details

Serverless API

Llama 3.2 11B Vision Instruct is available via Cyfuture AI' serverless API, where you pay per token. There are several ways to call the Cyfuture AI API, including Cyfuture AI' Python client, the REST API, or OpenAI's Python client.

See below for easy generation of calls and a description of the raw REST API for making API requests. See the Querying text models docs for details.

API Examples

Generate a model response using the image endpoint of llama-v3p2-11b-vision-instruct. API reference

import requests
import json

url = "https://api.cyfuture.ai/aiapi/inferencing/response"
payload = {
  "model": "Model Name",
  "max_tokens": 16384,
  "top_p": 1,
  "top_k": 40,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "temperature": 0.6,
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}
headers = {
  "Accept": "application/json",
  "Content-Type": "application/json",
  "Authorization": "Bearer <API_KEY>"
}
requests.request("POST", url, headers=headers, data=json.dumps(payload))
await fetch("https://api.cyfuture.ai/aiapi/inferencing/response", {
  method: "POST",
  headers: {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": "Bearer <API_KEY>"
  },
  body: JSON.stringify({
    model: ""Model Name"",
    max_tokens: 16384,
    top_p: 1,
    top_k: 40,
    presence_penalty: 0,
    frequency_penalty: 0,
    temperature: 0.6,
    messages: [
        {
            role: "user",
            content: "Hello, how are you?"
        }
    ]
  })
});
URI uri = URI.create("https://api.cyfuture.ai/aiapi/inferencing/response");
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
  .uri(uri)
  .header("Accept", "application/json")
  .header("Content-Type", "application/json")
  .header("Authorization", "Bearer <API_KEY>")
  .POST(HttpRequest.BodyPublishers.ofString("""{
    "model": ""Model Name"",
    "max_tokens": 16384,
    "top_p": 1,
    "top_k": 40,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "temperature": 0.6,
    "messages": [
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ]
  }"""))
  .build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
  
package main

import (
  "bytes"
  "net/http"
  "fmt"
)

apiUrl := "https://api.cyfuture.ai/aiapi/inferencing/response"
var jsonData = []byte(`{
 "model": "Model Name",
  "max_tokens": 16384,
  "top_p": 1,
  "top_k": 40,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "temperature": 0.6,
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}`)

req, err := http.NewRequest(POST, apiUrl, bytes.NewBuffer(jsonData))
req.Header.Set("Accept", "application/json")
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", "Bearer <API_KEY>")

client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
  panic(err)
}
defer resp.Body.Close()

fmt.Println("response Status:", resp.Status)
curl --request POST \
  --url https://api.cyfuture.ai/aiapi/inferencing/response \
  -H 'Accept: application/json' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  --data '{
   "model": "Model Name",
    "max_tokens": 16384,
    "top_p": 1,
    "top_k": 40,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "temperature": 0.6,
    "messages": [
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ]
  }'

On-demand deployments

On-demand deployments allow you to use Llama 3.2 11B Vision Instruct on dedicated GPUs with Cyfuture AI' high-performance serving stack with high reliability and no rate limits.

See the On-demand deployments guide for details.

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Model Details

Created by
dev@cyfuture.ai
Created
12/30/2024
Visibility
Public
Kind
Base model
Model size
14B parameters