# Create Fine Tuning Job — OpenAI (ChatGPT)

> Creates a job that fine-tunes a specified model from a given dataset. See the documentation

- Key: `openai-create-fine-tuning-job`
- Type: Action (Write)
- Version: 0.0.19
- App: OpenAI (ChatGPT) (`openai`) — https://pipedream.com/apps/openai.md
- This page (HTML): https://pipedream.com/apps/openai/actions/create-fine-tuning-job
- Hints: open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/openai/actions/create-fine-tuning-job/create-fine-tuning-job.mjs

## Description

Creates a job that fine-tunes a specified model from a given dataset. [See the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/create)

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `model` | `string` | Yes | The name of the model to fine-tune. See the supported models. |
| `trainingFile` | `string` | Yes | The ID of an uploaded file that contains training data. You can use the Upload File action and reference the returned ID here. Options are loaded from the connected account. |
| `hyperParameters` | `object` | No | The hyperparameters used for the fine-tuning job. See details in the documentation. |
| `suffix` | `string` | No | A string of up to 18 characters that will be added to your fine-tuned model name. |
| `validationFile` | `string` | No | The ID of an uploaded file that contains validation data. See details in the documentation. Options are loaded from the connected account. |

## Run it

**MCP**

```ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js"
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js"
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const accessToken = await pd.rawAccessToken

const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": process.env.PIPEDREAM_PROJECT_ID!,
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "openai",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
  name: "openai-create-fine-tuning-job",
  arguments: {
    model: "Fine Tuning Model",
    trainingFile: "Training File",
  },
})
```

**TypeScript**

```ts
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "openai-create-fine-tuning-job",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    openai: { authProvisionId: "apn_xxxxxxx" },
    model: "Fine Tuning Model",
    trainingFile: "Training File",
  },
})

console.log(result)
```

**cURL**

```bash
curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "openai-create-fine-tuning-job",
    "configured_props": {
      "openai": { "authProvisionId": "apn_xxxxxxx" },
      "model": "Fine Tuning Model",
      "trainingFile": "Training File"
    }
  }'
```

---

- App: https://pipedream.com/apps/openai.md · All apps: https://pipedream.com/apps