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Understanding the Llama-2-7b-chat model

Reviewed on 06 March 2024

Model overview

Model Namellama-2-7b-chat
Compatible InstancesH100 (FP16, FP8) - L4 (FP16, FP8)
Context size4,096 tokens

Model names


Compatible Instances

  • H100 (FP16, FP8)
  • L4 (FP16, FP8)

Model introduction

This is the Llama-2-7b-chat model, developed by Meta, fine-tuned on instructions to make it better a being a chat bot.

Why is it useful?

The Llama-2-7b-chat model is versatile, knowledgeable, creative, constantly learning, and friendly, making it a valuable conversational companion and source of assistance.

How to use it

Sending Managed Inference requests

To perform inference tasks with your Llama-2 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>" \
--data '{"model": "llama-2-7b-chat", "messages":[{"role": "user","content": "There is a llama in my garden, what should I do?"}], "max_tokens": 200, "top_p": 1, "temperature": 1, "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.


The model name allows Scaleway to put your prompts in the expected format.


Ensure that the messages array is properly formatted with roles (system, user, assistant) and content.

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 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 exists. Always verify the content generated independently.

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