Meta-Llama-3_3-70B-Instruct

Large Language Models (LLM)

O Llama 3.3 é um modelo de linguagem generativo ajustado por instruções e otimizado para casos de uso de diálogo multilingue. Foi lançado pela Meta AI a 6 de dezembro de 2024, utiliza uma arquitetura Transformer avançada e foi concebido para se alinhar com as preferências humanas em relação à utilidade e segurança através do ajuste fino supervisionado (SFT) e da aprendizagem por reforço com feedback humano (RLHF). A data limite de conhecimento para este modelo 70B é 31 de dezembro de 2023.

Experimente outro modelo

Sobre o modelo Meta-Llama-3_3-70B-Instruct

Publicado em huggingface

06/12/2024

Licença: Llama 3.3 Community

Preço de entrada

0.67 € /Mtoken(entrada)

Preço de saída

0.67 € /Mtoken(saída)

Funcionalidades suportadas

Chamada de funçãoStreaming

Formatos de saída

raw_textjson_objectjson_schema

Tamanhos de contexto

131k

Parâmetros

70B

Experimente o modelo brincando com ele.

Example of how to use Meta-Llama-3_3-70B-Instruct API in python

With a simple HTTP client (requests)

First install the requests library:

pip install requests

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

Finally, run the following Python code:

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": "Meta-Llama-3_3-70B-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)

With the Python OpenAI library

The Meta-Llama-3_3-70B-Instruct API is compatible with the OpenAI specification.

First install the openai library:

pip install openai

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

Finally, run the following Python code:

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="Meta-Llama-3_3-70B-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"))  

Model rate limit

When using AI Endpoints, the following rate limits apply:

Anonymous: 2 requests per minute, per IP and per model.
Authenticated with an API access key: 400 requests per minute, per Public Cloud project and per model.

If you exceed this limit, a 429 error code will be returned.

If you require higher usage, please get in touch with us to discuss increasing your rate limits.

Going Further

Want to explore the full capabilities of the LLM API? Dive into our dedicated Structured Output and Function Calling guides.

For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.

Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.

Meta-Llama-3_3-70B-Instruct