Llama 3 8b Instruct | Meta LLM API

Name: Llama 3 8b Instruct API
Brand: meta-llama
Price: 0.03 USD
Availability: InStock

Utilizzo API

Usa i seguenti esempi di codice per integrare la nostra API:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="meta-llama/llama-3-8b-instruct",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

Introduzione al modello

Meta-Llama llama-3-8b-instruct

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

Why It Looks Great

Large Language Model architecture for efficient processing
8192 context window for long document handling
Competitive pricing at $0.0/$0.0 per million tokens

Key Features

Context Window: 8192 tokens
Max Output: 16384 tokens
Vision: Supported
Function Calling: Supported

Specifications

Specification	Value
Provider	Meta-Llama
Model Type	Large Language Model (LLM)
Architecture	N/A
Context Window	8192 tokens
Max Output	16384 tokens
Input	Text
Output	Text
Vision	Supported
Function Calling	Supported

Pricing

Token Type	Cost per Million Tokens
Input	$0.0
Output	$0.0

How to Use

Write your prompt — describe the task, provide context, and specify desired output format.
Submit — the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: meta-llama/llama-3-8b-instruct

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="meta-llama/llama-3-8b-instruct",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "meta-llama/llama-3-8b-instruct",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: meta-llama/llama-3-8b-instruct
Provider: Meta-Llama

Domande frequenti su Llama 3 8b Instruct

Quanto costa Llama 3 8b Instruct via API?+

Prezzi su WaveSpeedAI: $0.03 per milione di token in input e $0.04 per milione di token in output. Prompt caching e batch processing sono fatturati separatamente e riducono il costo effettivo su carichi lunghi e ripetitivi.

Qual è la context window di Llama 3 8b Instruct?+

Llama 3 8b Instruct supporta fino a 8K token di contesto e fino a 16K token di output per richiesta.

Llama 3 8b Instruct è compatibile con OpenAI?+

Sì. WaveSpeedAI espone Llama 3 8b Instruct tramite un endpoint compatibile con OpenAI all'indirizzo https://llm.wavespeed.ai/v1. Punta l'SDK ufficiale di OpenAI a questa base URL con la tua API key WaveSpeedAI — senza altre modifiche al codice.

Come si inizia con Llama 3 8b Instruct?+

Accedi a WaveSpeedAI, crea una API key in Access Keys, poi invia una richiesta a https://llm.wavespeed.ai/v1/chat/completions con il model id mostrato sopra. I nuovi account ricevono crediti gratuiti per testare Llama 3 8b Instruct.

Prezzi