What is Meta: Llama 3 8B Instruct model?

8B-parameter instruction-tuned LLM optimized for reasoning, code, and dialogue. Trained on 15T tokens with SFT and DPO. Supports 80K context.

How to use Meta: Llama 3 8B Instruct API?

Call LLM endpoint with prompt, max_tokens, and temperature params. Streams via SSE. OpenAI-compatible format.

Is Meta: Llama 3 8B Instruct alternative to proprietary models?

Matches benchmarks in instruction following and reasoning. Open-weight for custom fine-tuning. Efficient at 8B scale.

What context length for meta llama 3 8b instruct api?

Up to 80K tokens with QLoRA. Default 8192 in many providers. GQA maintains speed.

Meta: Llama 3 8B Instruct LLM strengths?

Code synthesis, multilingual support, tool integration. Low refusal rates post-alignment. Competitive on NLP benchmarks.

Deploy meta: llama 3 8b instruct model?

Use containers like NVIDIA NIM for GPU inference. Supports on-prem or cloud. Optimized for language tasks.

Available now on ModelsLab · Language Model

Meta: Llama 3 8B Instruct
Instruct Precisely. Scale Fast

Try Meta: Llama 3 8B Instruct API Documentation

Deploy Llama 3 Power

Instruction Tuning

Follows Complex Prompts

Handles multi-turn instructions, reasoning, and code synthesis with 8B parameters.

Extended Context

Supports 80K Tokens

Processes long contexts via QLoRA adaptation for coherent multilingual dialogue.

Efficient Inference

GQA Optimized

Uses Grouped Query Attention for fast deployment on standard hardware.

Examples

See what Meta: Llama 3 8B Instruct can create

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

Code Generator

“Write a Python function to compute Fibonacci sequence up to n terms using memoization. Include tests and docstring.”

Reasoning Chain

“Solve this logic puzzle step-by-step: Three houses in a row, owners A B C like tea coffee milk. Solve based on clues provided.”

Summarizer

“Summarize key advancements in Transformer architectures from 2017 to 2025, focusing on attention mechanisms.”

Multilingual Query

“Translate this technical explanation of neural networks into Spanish, then explain differences in terminology.”

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 8B Instruct

Read the docs

Ready to create?

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