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Introduction
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 modelOUTSCALE_MODEL_NAME: the name of the model to query (e.g.small-2409,codestral-2405)
1 client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
2
3 resp = client.chat.complete(
4 model=os.environ["OUTSCALE_MODEL_NAME"],
5 messages=[
6 {
7 "role": "user",
8 "content": "Who is the best French painter? Answer in one short sentence.",
9 }
10 ],
11 temperature=0
12 )
13
14 print(resp.choices[0].message.content)
15 ```
16</TabItem>
17<TabItem value="ts" label="TypeScript">
18 ```typescript
19 import { Mistral } from "@mistralai/mistralai";
20
21 const client = new Mistral({
22 serverURL: process.env.OUTSCALE_SERVER_URL || ""
23 });
24
25 const modelName = process.env.OUTSCALE_MODEL_NAME|| "";
26
27 async function chatCompletion(user_msg: string) {
28 const resp = await client.chat.complete({
29 model: modelName,
30 messages: [
31 {
32 content: user_msg,
33 role: "user",
34 },
35 ],
36 });
37 if (resp.choices && resp.choices.length > 0) {
38 console.log(resp.choices[0]);
39 }
40 }
41
42 chatCompletion("Who is the best French painter? Answer in one short sentence.");
43 ```
44</TabItem>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.
1 client = Mistral(server_url=os.environ["OUTSCALE_SERVER_URL"])
2
3 resp = client.fim.complete(
4 model = os.environ["OUTSCALE_MODEL_NAME"],
5 prompt="def count_words_in_file(file_path: str) -> int:",
6 suffix="return n_words"
7 )
8
9 print(resp.choices[0].message.content)
10 ```
11</TabItem>
12<TabItem value="ts" label="TypeScript">
13 ```typescript
14 import { Mistral} from "@mistralai/mistralai";
15
16 const client = new Mistral({
17 serverURL: process.env.OUTSCALE_SERVER_URL || ""
18 });
19
20 const modelName = "codestral-2405";
21
22 async function fimCompletion(prompt: string, suffix: string) {
23 const resp = await client.fim.complete({
24 model: modelName,
25 prompt: prompt,
26 suffix: suffix
27 });
28 if (resp.choices && resp.choices.length > 0) {
29 console.log(resp.choices[0]);
30 }
31 }
32
33 fimCompletion("def count_words_in_file(file_path: str) -> int:",
34 "return n_words");
35 ```
36</TabItem>Going further
For more information and examples, you can check:
- The Outscale documentation explaining how to subscribe to a Mistral service and deploy it.