Llama 3.1 (versión de 8 mil millones de parámetros) es un modelo de lenguaje autorregresivo que utiliza una arquitectura de transformador optimizada. Fue lanzado por Meta AI el 23 de julio de 2024, y utiliza ajuste fino supervisado (SFT) y aprendizaje por refuerzo con retroalimentación humana (RLHF) para alinearse con las preferencias humanas de utilidad y seguridad. La fecha de corte de conocimiento para este modelo es el 31 de diciembre de 2023.
First install the requests library:
pip install requestsNext, 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": "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)The Llama-3.1-8B-Instruct API is compatible with the OpenAI specification.
First install the openai library:
pip install openaiNext, 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="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")) When using AI Endpoints, the following rate limits apply:
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.
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.
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