> ## Documentation Index
> Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-tune

> Fine-tune your models with Azure OpenAI

Azure OpenAI follows a similar fine-tuning process as OpenAI, with some Azure-specific configurations. The examples below show how to use Portkey with Azure OpenAI for fine-tuning.

### Upload a file

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    from portkey_ai import Portkey

    # Initialize the Portkey client
    portkey = Portkey(
        api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
        virtual_key="VIRTUAL_KEY" # Add your provider's virtual key for Azure OpenAI
    )

    # Upload a file for fine-tuning
    file = portkey.files.create(
        file="dataset.jsonl",
        purpose="fine-tune"
    )

    print(file)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript theme={"system"}
    import { Portkey } from "portkey-ai";
    import * as fs from 'fs';

    // Initialize the Portkey client
    const portkey = Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VIRTUAL_KEY"   // Add your provider's virtual key for Azure OpenAI
    });

    (async () => {
        // Upload a file for fine-tuning
        const file = await portkey.files.create({
            file: fs.createReadStream("dataset.jsonl"),
            purpose: "fine-tune"
        });

        console.log(file);
    })();
    ```
  </Tab>

  <Tab title="OpenAI Python">
    ```python theme={"system"}
    from openai import AzureOpenAI
    from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

    client = AzureOpenAI(
        api_key="AZURE_OPENAI_API_KEY",
        api_version="2023-05-15",
        azure_endpoint=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VIRTUAL_KEY",
            api_key="PORTKEY_API_KEY"
        )
    )

    # Upload a file for fine-tuning
    file = client.files.create(
        file=open("dataset.jsonl", "rb"),
        purpose="fine-tune"
    )

    print(file)
    ```
  </Tab>

  <Tab title="REST API">
    ```sh theme={"system"}
    curl -X POST --header 'x-portkey-api-key: <portkey_api_key>' \
     --header 'x-portkey-virtual-key: <virtual_key>' \
     --form 'file=@dataset.jsonl' \
     --form 'purpose=fine-tune' \
     'https://api.portkey.ai/v1/files'
    ```
  </Tab>
</Tabs>

### Create a fine-tuning job

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    from portkey_ai import Portkey

    # Initialize the Portkey client
    portkey = Portkey(
        api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
        virtual_key="VIRTUAL_KEY" # Add your provider's virtual key for Azure OpenAI
    )

    # Create a fine-tuning job
    fine_tune_job = portkey.fine_tuning.jobs.create(
        model="gpt-35-turbo", # Base model to fine-tune
        training_file="file_id", # ID of the uploaded training file
        validation_file="file_id", # Optional: ID of the uploaded validation file
        suffix="finetune_name", # Custom suffix for the fine-tuned model name
        hyperparameters={
            "n_epochs": 1
        }
    )

    print(fine_tune_job)
    ```
  </Tab>

  <Tab title="NodeJS">
    ```typescript theme={"system"}
    import { Portkey } from "portkey-ai";

    // Initialize the Portkey client
    const portkey = Portkey({
        apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
        virtualKey: "VIRTUAL_KEY"   // Add your provider's virtual key for Azure OpenAI
    });

    (async () => {
        // Create a fine-tuning job
        const fineTuneJob = await portkey.fineTuning.jobs.create({
            model: "gpt-35-turbo", // Base model to fine-tune
            training_file: "file_id", // ID of the uploaded training file
            validation_file: "file_id", // Optional: ID of the uploaded validation file
            suffix: "finetune_name", // Custom suffix for the fine-tuned model name
            hyperparameters: {
                n_epochs: 1
            }
        });

        console.log(fineTuneJob);
    })();
    ```
  </Tab>

  <Tab title="OpenAI Python">
    ```python theme={"system"}
    from openai import AzureOpenAI
    from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

    client = AzureOpenAI(
        api_key="AZURE_OPENAI_API_KEY",
        api_version="2023-05-15",
        azure_endpoint=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            virtual_key="VIRTUAL_KEY",
            api_key="PORTKEY_API_KEY"
        )
    )

    # Create a fine-tuning job
    fine_tune_job = client.fine_tuning.jobs.create(
        model="gpt-35-turbo", # Base model to fine-tune
        training_file="file_id", # ID of the uploaded training file
        validation_file="file_id", # Optional: ID of the uploaded validation file
        suffix="finetune_name", # Custom suffix for the fine-tuned model name
        hyperparameters={
            "n_epochs": 1
        }
    )

    print(fine_tune_job)
    ```
  </Tab>

  <Tab title="REST API">
    ```sh theme={"system"}
    curl -X POST --header 'Content-Type: application/json' \
     --header 'x-portkey-api-key: <portkey_api_key>' \
     --header 'x-portkey-virtual-key: <virtual_key>' \
     --data \
     $'{"model": "<base_model>", "suffix": "<finetune_name>", "training_file": "<file_id>", "validation_file": "<file_id>", "hyperparameters": {"n_epochs": 1}}\n' \
    'https://api.portkey.ai/v1/fine_tuning/jobs'
    ```
  </Tab>
</Tabs>

For more detailed examples and other fine-tuning operations (listing jobs, retrieving job details, canceling jobs, and getting job events), please refer to the [OpenAI fine-tuning documentation](/integrations/llms/openai/fine-tuning).

The Azure OpenAI fine-tuning API documentation is available at [Azure OpenAI API](https://learn.microsoft.com/en-us/rest/api/azureopenai/fine-tuning/create?view=rest-azureopenai-2025-01-01-preview\&tabs=HTTP).