Llama-3.2-90B-Vision-Instruct-Turbo

This documentation is valid for the following list of our models:

• meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo

Model Overview

A pretrained, instruction-tuned image reasoning model that takes text and images as input and generates text output. The Llama 3.2-Vision models are fine-tuned for tasks like visual recognition, image-based reasoning, captioning, and answering general questions about images. They outperform many open and proprietary multimodal models on standard benchmarks.

How to Make a Call

1

Setup You Can’t Skip

▪️ Create an Account: Visit the Apilaplas API website and create an account (if you don’t have one yet). ▪️ Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.

2

Copy the code example

At the bottom of this page, you'll find a code example that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

3

Modify the code example

▪️ Replace <YOUR_LAPLASAPI_KEY> with your actual Apilaplas API key from your account. ▪️ Insert your question or request into the content field—this is what the model will respond to.

4

^_(Optional) Adjust other optional parameters if needed

Only model and messages are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding API schema, which lists all available parameters along with notes on how to use them.

5

Run your modified code

Run your modified code in your development environment. Response time depends on various factors, but for simple prompts it rarely exceeds a few seconds.

If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our Quickstart guide.

API Schema

Generate a conversational response using a language model.

post

Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.

Authorizations

access-token

AuthorizationstringRequired

Bearer key

Body

application/json

modelundefined · enumRequiredPossible values:

max_tokensnumber · min: 1OptionalDefault: 512

stopany ofOptional

stringOptional

or

string[]Optional

or

any · nullableOptional

streambooleanOptionalDefault: false

ninteger · min: 1Optional

seedinteger · min: 1Optional

top_pnumber · min: 0.01 · max: 1Optional

top_knumberOptional

temperaturenumberOptional

repetition_penaltynumber · nullableOptional

logprobsboolean · nullableOptional

echobooleanOptional

min_pnumber · max: 1Optional

presence_penaltynumber · min: -2 · max: 2 · nullableOptional

frequency_penaltynumber · min: -2 · max: 2 · nullableOptional

tool_choiceany ofOptional

string · enumOptionalPossible values:

or

response_formatone ofOptional

or

or

Responses

201Success

post

/v1/chat/completions

HTTPcURLJavaScriptPython

POST /v1/chat/completions HTTP/1.1
Host: api.apilaplas.com
Authorization: Bearer YOUR_SECRET_TOKEN
Content-Type: application/json
Accept: */*
Content-Length: 555

{
  "model": "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
  "messages": [
    {
      "role": "system",
      "content": "text",
      "name": "text"
    }
  ],
  "max_tokens": 512,
  "stop": "text",
  "stream": false,
  "stream_options": {
    "include_usage": true
  },
  "n": 1,
  "seed": 1,
  "top_p": 1,
  "top_k": 1,
  "temperature": 1,
  "repetition_penalty": 1,
  "logprobs": true,
  "echo": true,
  "min_p": 1,
  "presence_penalty": 1,
  "frequency_penalty": 1,
  "logit_bias": {
    "ANY_ADDITIONAL_PROPERTY": 1
  },
  "tools": [
    {
      "type": "function",
      "function": {
        "description": "text",
        "name": "text",
        "parameters": null
      }
    }
  ],
  "tool_choice": "none",
  "response_format": {
    "type": "text"
  }
}

201Success

No content

Code Example (Python)

import requests

response = requests.post(
    "https://api.apilaplas.com/v1/chat/completions",
    headers={
        "Content-Type":"application/json", 

        # Insert your LAPLAS API Key instead of <YOUR_LAPLASAPI_KEY>:
        "Authorization":"Bearer <YOUR_LAPLASAPI_KEY>",
        "Content-Type":"application/json"
    },
    json={
        "model":"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
        "messages":[
            {
                "role":"user",

                # Insert your question for the model here, instead of Hello:
                "content":"Hello"
            }
        ]
    }
)

data = response.json()
print(data)

Response

{'id': 'npQeQJu-2j9zxn-92da8ec6698de8e9', 'object': 'chat.completion', 'choices': [{'index': 0, 'finish_reason': 'stop', 'logprobs': None, 'message': {'role': 'assistant', 'content': 'Hello! How can I assist you today?', 'tool_calls': []}}], 'created': 1744207509, 'model': 'meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo', 'usage': {'prompt_tokens': 28, 'completion_tokens': 25, 'total_tokens': 53}}