Outscale

Mistral AI models are available on the Outscale platform as managed deployments. Through the Outscale marketplace, you can subscribe to a Mistral service that will, on your behalf, provision a virtual machine and a GPU then deploy the model on it. As of today, the following models are available:

Mistral Small (24.09)
Codestral (24.05)
Ministral 8B (24.10)

For more details, visit the models page.

Getting Started

The following sections outline the steps to query a Mistral model on the Outscale platform.

Deploying the Model

Follow the steps described in the Outscale documentation to deploy a service with the model of your choice.

Querying the Model (Chat Completion)

Deployed models expose a REST API that you can query using Mistral's SDK or plain HTTP calls. To run the examples below, you will need to set the following environment variables:

OUTSCALE_SERVER_URL: The URL of the VM hosting your Mistral model.
OUTSCALE_MODEL_NAME: The name of the model to query (e.g., small-2409, codestral-2405).

import os
from mistralai import Mistral
client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
resp = client.chat.complete(
    model=os.environ["OUTSCALE_MODEL_NAME"],
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        }
    ],
    temperature=0
)
print(resp.choices[0].message.content)

import os
from mistralai import Mistral
client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
resp = client.chat.complete(
    model=os.environ["OUTSCALE_MODEL_NAME"],
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        }
    ],
    temperature=0
)
print(resp.choices[0].message.content)

Querying the Model (FIM Completion)

Codestral can be queried using an additional completion mode called fill-in-the-middle (FIM). For more information, see the code generation section.

 import os
 from mistralai import Mistral
 client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
 resp = client.fim.complete(
     model=os.environ["OUTSCALE_MODEL_NAME"],
     prompt="def count_words_in_file(file_path: str) -> int:",
     suffix="return n_words"
 )
 print(resp.choices[0].message.content)

 import os
 from mistralai import Mistral
 client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
 resp = client.fim.complete(
     model=os.environ["OUTSCALE_MODEL_NAME"],
     prompt="def count_words_in_file(file_path: str) -> int:",
     suffix="return n_words"
 )
 print(resp.choices[0].message.content)

Going Further

For more information and examples, you can check:

The Outscale documentation explaining how to subscribe to a Mistral service and deploy it.