The model name allows Scaleway to put your prompts in the expected format.
Understanding the Mistral-7b-instruct-v0.3 model
Model overviewLink to this anchor
Attribute | Details |
---|---|
Provider | Mistral |
Compatible Instances | L4, L40S, H100, H100-2 (BF16) |
Context size | 32K tokens |
Model nameLink to this anchor
mistral/mistral-7b-instruct-v0.3:bf16
Compatible InstancesLink to this anchor
Instance type | Max context length |
---|---|
L4 | 32k (BF16) |
L40S | 32k (BF16) |
H100 | 32k (BF16) |
H100-2 | 32k (BF16) |
Model introductionLink to this anchor
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?Link to this anchor
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 itLink to this anchor
Sending Inference requestsLink to this anchor
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.scaleway.com/v1/chat/completions" \--data '{"model":"mistral/mistral-7b-instruct-v0.3:bf16", "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.
Ensure that the messages
array is properly formatted with roles (system, user, assistant) and content.
Receiving Managed 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 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.