DeepSeek-R1-Distill-Llama-70B

Reasoning LLM

Model DeepSeek-R1-Distill-Llama-70B to model wytrenowany na bardzo dużej ilości danych za pomocą uczenia przez wzmacnianie. Został udostępniony przez DeepSeek 20 stycznia 2025 roku i jest destylowaną wersją modelu Llama 3.3 70B. Wiedza tego modelu obejmuje informacje do 1 lipca 2024.

Wypróbuj inny model

O modelu DeepSeek-R1-Distill-Llama-70B

Opublikowano huggingface

20/01/2025

Licencja: MIT & Meta Llama 3 Community License

Cena wejściowa

0.67 € /Mtoken(wejście)

Cena wyjściowa

0.67 € /Mtoken(wyjście)

Obsługiwane funkcje

Wywoływanie funkcjiReasoningStreaming

Formaty wyjściowe

raw_textjson_objectjson_schema

Rozmiary kontekstu

131k

Parametry

70B

Wypróbuj model, bawiąc się nim.

Example of how to use DeepSeek-R1-Distill-Llama-70B 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": "DeepSeek-R1-Distill-Llama-70B",
    "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 DeepSeek-R1-Distill-Llama-70B 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="DeepSeek-R1-Distill-Llama-70B",
        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.

DeepSeek-R1-Distill-Llama-70B