> ## 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 Prisma AIRS AI Gateway with Azure OpenAI for fine-tuning.

### Upload a file

> [tab] REST API
>
> ```sh theme={"system"}
> curl -X POST --header 'Authorization: Bearer <portkey_api_key>' \
>  --header 'x-portkey-provider: @provider' \
>  --form 'file=@dataset.jsonl' \
>  --form 'purpose=fine-tune' \
>  'https://aigw.portkey.ai/v1/files'
> ```

> [tab] OpenAI Python
>
> ```python theme={"system"}
> from openai import AzureOpenAI
>
> client = AzureOpenAI(
>     api_key="PORTKEY_API_KEY",
>     api_version="2023-05-15",
>     azure_endpoint="https://aigw.portkey.ai/v1",
>     default_headers={"x-portkey-provider": "@PROVIDER"}
> )
>
> # Upload a file for fine-tuning
> file = client.files.create(
>     file=open("dataset.jsonl", "rb"),
>     purpose="fine-tune"
> )
>
> print(file)
> ```

### Create a fine-tuning job

> [tab] REST API
>
> ```sh theme={"system"}
> curl -X POST --header 'Content-Type: application/json' \
>  --header 'Authorization: Bearer <portkey_api_key>' \
>  --data \
>  $'{"model": "@provider/<base_model>", "suffix": "<finetune_name>", "training_file": "<file_id>", "validation_file": "<file_id>", "hyperparameters": {"n_epochs": 1}}\n' \
> 'https://aigw.portkey.ai/v1/fine_tuning/jobs'
> ```

> [tab] OpenAI Python
>
> ```python theme={"system"}
> from openai import AzureOpenAI
>
> client = AzureOpenAI(
>     api_key="PORTKEY_API_KEY",
>     api_version="2023-05-15",
>     azure_endpoint="https://aigw.portkey.ai/v1"
> )
>
> # Create a fine-tuning job
> fine_tune_job = client.fine_tuning.jobs.create(
>     model="@PROVIDER/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)
> ```

For more detailed examples and other fine-tuning operations (listing jobs, retrieving job details, cancelling jobs, and getting job events), please refer to the [OpenAI fine-tuning documentation](https://docs.portkey.ai/docs/aigw/integrations/llms/openai/fine-tuning.md).

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).


## Related topics

- [Fine-tune](https://docs.portkey.ai/docs/aigw/integrations/llms/fireworks/fine-tuning.md)
- [Data Service](https://docs.portkey.ai/docs/changelog/data-service.md)


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