Understanding the DeepSeek-R1-Distill-Llama-70B model

Model overviewLink to this anchor

Model namesLink to this anchor

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

Compatible InstancesLink to this anchor

Model introductionLink to this anchor

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?Link to this anchor

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 itLink to this anchor

Sending Managed Inference requestsLink to this anchor

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.

Receiving inference responsesLink to this anchor

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.