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Understanding the Mistral-7b-instruct-v0.3 model

Reviewed on 26 June 2024

Model overview

AttributeDetails
ProviderMistral
Model Namemistral-7b-instruct-v0.3
Compatible InstancesL4 (BF16)
Context size32K tokens

Model name

mistral-7b-instruct-v0.3:bf16

Compatible Instance

  • L4 (BF16)

Model introduction

The first dense model released by Mistral AI, perfect for experimentation, customization, and quick iteration. At the time of the release, it matched the capabilities of models up to 30B parameters. This model is open-weight and distributed under the Apache 2.0 license.

Why is it useful?

Mistral-7B-Instruct-v0.3 is the smallest and latest Large Language Model (LLM) from Mistral AI, providing a 32k context window and support for function calling. It does not have any moderation mechanisms to finely respect guardrails. Use with caution for deployments in environments requiring moderated outputs.

How to use it

Sending Inference requests

To perform inference tasks with your Mistral model 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.scw.cloud/v1/chat/completions" \
--data '{"model":"mistral-7b-instruct-v0.3", "messages":[{"role": "user","content": "Explain Public Cloud in a nutshell."}], "top_p": 1, "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.

Tip

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

Note

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

Receiving Managed 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.

Note

Despite efforts for accuracy, the possibility of generated text containing inaccuracies or hallucinations exists. Always verify the content generated independently.

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