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Available now on ModelsLab · Language Model

Meta Llama 3.1 8B Instruct TurboTurbocharge Llama Responses

Try Meta Llama 3.1 8B Instruct TurboAPI Documentation

Deploy Turbo Performance

131K Context

Handle Long Inputs

Process 131k token context window for extended dialogues and documents.

Function Calling

Enable Tool Use

Supports function calling for structured outputs and agent workflows.

150 Tokens/Second

Run High Throughput

Achieve 150 tokens per second speed on Meta Llama 3.1 8B Instruct Turbo API.

Examples

See what Meta Llama 3.1 8B Instruct Turbo can create

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

Code Review

“Review this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”

Text Summary

“Summarize key points from this article on quantum computing advancements in 2024, focusing on hardware breakthroughs.”

JSON Extraction

“Extract product details as JSON from: Apple iPhone 15 Pro, 256GB, Titanium frame, A17 chip, released 2023.”

Multilingual Query

“Translate to French and explain: What is recursive neural network architecture used for in NLP tasks?”

For Developers

A few lines of code.
Instruct Turbo. 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 Meta Llama 3.1 8B Instruct Turbo

Read the docs

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Try Meta Llama 3.1 8B Instruct TurboAPI Documentation