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Available now on ModelsLab · Language Model

Meta: Llama 3.1 70B InstructInstruct Precisely. Scale Smart

Try Meta: Llama 3.1 70B InstructAPI Documentation

Deploy Llama 3.1 Power

128K Context

Handle Long Inputs

Process 128,000 tokens for summarization and extended dialogues with Meta: Llama 3.1 70B Instruct.

Multilingual Support

Eight Languages Native

Supports English, German, French, Hindi, Spanish, Italian, Portuguese, Thai in Meta Llama 3.1 70B Instruct.

Instruction Tuned

Follow Complex Tasks

Execute precise instructions for code generation and analysis using Meta: Llama 3.1 70B Instruct model.

Examples

See what Meta: Llama 3.1 70B Instruct can create

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

Code Review

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

Text Summary

“Summarize this 500-word article on quantum computing advancements, highlighting key breakthroughs and implications for AI.”

Multilingual Q&A

“Explain neural networks in German, then translate to Spanish, keeping technical terms accurate.”

Data Analysis

“Analyze this sales dataset: Q1:100k, Q2:150k, Q3:120k, Q4:200k. Predict Q1 trends and suggest optimizations.”

For Developers

A few lines of code.
Instruct Llama. 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 70B Instruct

Read the docs

Meta: Llama 3.1 70B Instruct is a 70B parameter LLM optimized for instruction following and multilingual tasks. It uses SFT and RLHF for helpful responses. Context reaches 128K tokens.

Integrate via LLM endpoint with JSON payloads for prompts. Set max_tokens up to 4096 typically. Supports frequency_penalty and tool calls.

Supports 128,000 tokens total for prompt and response. Deployments often cap output at 2048 tokens for latency. Tune per workload.

Yes, handles English, French, German, Hindi, Italian, Portuguese, Spanish, Thai. Ideal for global customer support and translation.

Excels in content creation, code generation, summarization, sentiment analysis. Powers conversational AI and enterprise tools.

Offers open-source performance rivaling closed models on benchmarks. Use as cost-effective alternative via API for instruct tasks.

Ready to create?

Start generating with Meta: Llama 3.1 70B Instruct on ModelsLab.

Try Meta: Llama 3.1 70B InstructAPI Documentation