Outscale

介绍

Mistral AI 模型在 Outscale 平台上作为托管部署提供。通过 Outscale 市场，您可以订阅 Mistral 服务，该服务将代表您配置虚拟机和 GPU，然后在其上部署模型。

目前，以下模型可用：

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

更多详细信息，请访问模型页面。

入门

以下部分概述了在 Outscale 平台上查询 Mistral 模型的步骤。

部署模型

按照Outscale 文档中描述的步骤，使用您选择的模型部署服务。

查询模型（聊天补全）

部署的模型暴露了一个 REST API，您可以使用 Mistral 的 SDK 或纯 HTTP 调用进行查询。要运行以下示例，您需要设置以下环境变量：

• OUTSCALE_SERVER_URL: 托管您的 Mistral 模型的虚拟机 URL
• OUTSCALE_MODEL_NAME: 要查询的模型名称（例如 small-2409、codestral-2405）

cURL

echo $OUTSCALE_SERVER_URL/v1/chat/completions
echo $OUTSCALE_MODEL_NAME
curl --location $OUTSCALE_SRV_URL/v1/chat/completions \
  --header "Content-Type: application/json" \
  --header "Accept: application/json" \
  --data '{
      "model": "'"$OUTSCALE_MODEL_NAME"'",
      "temperature": 0,
      "messages": [
        {"role": "user", "content": "Who is the best French painter? Answer in one short sentence."}
      ],
      "stream": false
    }'

Python

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)

TypeScript

import { Mistral } from "@mistralai/mistralai";

const client = new Mistral({
    serverURL: process.env.OUTSCALE_SERVER_URL || ""
});

const modelName = process.env.OUTSCALE_MODEL_NAME|| "";

async function chatCompletion(user_msg: string) {
    const resp = await client.chat.complete({
        model: modelName,
        messages: [
            {
                content: user_msg,
                role: "user",
            },
        ],
    });
    if (resp.choices && resp.choices.length > 0) {
        console.log(resp.choices[0]);
    }
}

chatCompletion("Who is the best French painter? Answer in one short sentence.");

查询模型（FIM 补全）

Codestral 可以使用一种称为“填充中间”(FIM) 的额外补全模式进行查询。更多信息，请参阅代码生成部分。

cURL

 curl --location $OUTSCALE_SERVER_URL/v1/fim/completions \
   --header "Content-Type: application/json" \
   --header "Accept: application/json" \
   --data '{
       "model": "'"$OUTSCALE_MODEL_NAME"'",
       "prompt": "def count_words_in_file(file_path: str) -> int:",
       "suffix": "return n_words",
       "stream": false
     }'

Python

 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)

TypeScript

 import { Mistral} from "@mistralai/mistralai";

 const client = new Mistral({
     serverURL: process.env.OUTSCALE_SERVER_URL || ""
 });

 const modelName = "codestral-2405";

 async function fimCompletion(prompt: string, suffix: string) {
     const resp = await client.fim.complete({
         model: modelName,
         prompt: prompt,
         suffix: suffix
     });
     if (resp.choices && resp.choices.length > 0) {
         console.log(resp.choices[0]);
     }
 }

 fimCompletion("def count_words_in_file(file_path: str) -> int:",
               "return n_words");

深入了解

更多信息和示例，您可以查看：

• Outscale 文档，解释如何订阅和部署 Mistral 服务。