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Available now on ModelsLab · Language Model

Meta Llama 3 8B Instruct ReferenceEfficient reasoning. Production-ready.

Try Meta Llama 3 8B Instruct ReferenceAPI Documentation

Compact Power. Enterprise Scale.

Instruction-Tuned

Dialogue Optimized Performance

Fine-tuned for conversation with supervised learning and human feedback alignment.

Fast Inference

Grouped Query Attention

GQA architecture accelerates token generation without sacrificing output quality.

Extended Context

8K Token Window

Handle longer conversations and complex multi-turn interactions seamlessly.

Examples

See what Meta Llama 3 8B Instruct Reference can create

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

Code Documentation

“Write comprehensive API documentation for a Python function that validates email addresses using regex patterns. Include parameter descriptions, return types, and usage examples.”

Technical Explanation

“Explain how transformer attention mechanisms work in large language models. Use analogies to make it accessible to someone new to machine learning.”

Data Analysis

“Generate Python code to load a CSV file, calculate summary statistics, and create visualizations for sales data across regions.”

Problem Solving

“Provide step-by-step solutions to optimize database queries for a web application handling millions of daily requests.”

For Developers

A few lines of code.
Text and code. Eight billion parameters.

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 8B Instruct Reference

Read the docs

Llama 3 8B combines efficient 8B parameters with instruction-tuning optimized for dialogue, outperforming many larger open-source models on benchmarks. It uses Grouped Query Attention for faster inference and supports an 8K token context window.

Yes. The model is specifically optimized for code synthesis and mathematical reasoning. It achieves 62.2% on HumanEval benchmarks, making it effective for generating production-quality code.

It was trained on 15 trillion tokens from publicly available sources using supervised fine-tuning and reinforcement learning with human feedback to optimize for helpfulness and safety.

Yes. It's an open-weight model released under the Meta Llama 3 Community License Agreement, available on Hugging Face and other platforms.

The model's knowledge cutoff is March 2023, meaning it has training data up to that date.

Ready to create?

Start generating with Meta Llama 3 8B Instruct Reference on ModelsLab.

Try Meta Llama 3 8B Instruct ReferenceAPI Documentation