Name: Llama-3.1-8B-Instruct
Price: 0.1 EUR
Availability: InStock

pip install requests

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

import os
import requests
 
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
    "max_tokens": 512,
    "messages": [
        {
            "content": "Explain gravity to a 6 years old",
            "role": "user"
        }
    ],
    "model": "Llama-3.1-8B-Instruct",
    "temperature": 0,
}
 
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
 
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
    # Handle response
    response_data = response.json()
    # Parse JSON response
    choices = response_data["choices"]
    for choice in choices:
        text = choice["message"]["content"]
        # Process text and finish_reason
        print(text)
else:
    print("Error:", response.status_code, response.text)

pip install openai

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

import os

from openai import OpenAI

url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"

client = OpenAI(
    base_url=url,
    api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)

def chat_completion(new_message: str) -> str:
    history_openai_format = [{"role": "user", "content": new_message}]

    return client.chat.completions.create(
        model="Llama-3.1-8B-Instruct",
        messages=history_openai_format,
        temperature=0,
        max_tokens=1024
    ).choices.pop().message.content


if __name__ == '__main__':
    print(chat_completion("Explain gravity for a 6 years old"))  

Llama-3.1-8B-Instruct

Llama-3.1-8B-Instruct

À propos du modèle Llama-3.1-8B-Instruct

Token envoyés

Token reçus

Fonctionnalités prises en charge

Formats de sortie

Taille du contexte

Paramètres

Essayez le modèle.

Llama-3.1-8B-Instruct API

Example of how to use Llama-3.1-8B-Instruct API in python

With a simple HTTP client (requests)

With the Python OpenAI library

Model rate limit

Going Further

Commencez avec Llama-3.1-8B-Instruct

AI Endpoints - Getting Started

AI Endpoints - Features, Capabilities and Limitations

AI Endpoints - Troubleshooting

AI Endpoints - Structured Output

AI Endpoints - Function Calling

AI Endpoints - Using Virtual Models

Create your own Audio Summarizer Assistant

Build a Powerful Voice Assistant

Build a Python Chatbot with LangChain

Build a JavaScript Chatbot with LangChain

Create your own AI chatbot using LangChain4j

Create a Streaming Chatbot with LangChain4j

Enable Memory in your Chatbot using LangChain

Create a Memory Chatbot with LangChain4j

Build a RAG Chatbot with LangChain

Build a RAG Chatbot with LangChain4j

Using Structured Output with LangChain4j

Using Function Calling with LangChain4j

Model Context Protocol (MCP) with LangChain4j