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Understanding the DeepSeek-R1-Distill-Llama-70B model
Deploy your own secure DeepSeek-R1-Distill-Llama-70B model with Scaleway Managed Inference. Privacy-focused, fully managed.
h1 paragraph
Understanding the DeepSeek-R1-Distill-Llama-70B model
This page provides information on the DeepSeek-R1-Distill-Llama-70B model
validation posted
2025-02-06
2025-02-06
ai-data

Model overview

Attribute Details
Provider Deepseek
License MIT
Compatible Instances H100-2 (BF16)
Context Length up to 56k tokens

Model names

deepseek/deepseek-r1-distill-llama-70b:bf16

Compatible Instances

Instance type Max context length
H100-2 56k (BF16)

Model introduction

Released January 21, 2025, Deepseek’s R1 Distilled Llama 70B is a distilled version of the Llama model family based on Deepseek R1. DeepSeek R1 Distill Llama 70B is designed to improve the performance of Llama models on reasoning use case such as mathematics and coding tasks.

Why is it useful?

It is great to see Deepseek improving open(weight) models, and we are excited to fully support their mission with integration in the Scaleway ecosystem.

  • DeepSeek-R1-Distill-Llama was optimized to reach accuracy close to Deepseek-R1 in tasks like mathematics and coding, while keeping inference costs limited and tokens speed efficient.
  • DeepSeek-R1-Distill-Llama supports a context window of up to 56K tokens and tool calling, keeping interaction with other components possible.

How to use it

Sending Managed Inference requests

To perform inference tasks with your DeepSeek R1 Distill Llama deployed at Scaleway, use the following command:

curl -s \
-H "Authorization: Bearer <IAM API key>" \
-H "Content-Type: application/json" \
--request POST \
--url "https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/chat/completions" \
--data '{"model":"deepseek/deepseek-r1-distill-llama-70b:fp8", "messages":[{"role": "user","content": "There is a llama in my garden, what should I do?"}], "max_tokens": 500, "temperature": 0.7, "stream": false}'

Make sure to replace <IAM API key> and <Deployment UUID> with your actual IAM API key and the Deployment UUID you are targeting.

Ensure that the `messages` array is properly formatted with roles (user, assistant) and content. This model is better used without `system prompt`, as suggested by the model provider.

Receiving inference responses

Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the Managed Inference server. Process the output data according to your application's needs. The response will contain the output generated by the LLM model based on the input provided in the request.

Despite efforts for accuracy, the possibility of generated text containing inaccuracies or [hallucinations](/managed-inference/concepts/#hallucinations) exists. Always verify the content generated independently.