> ## 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

> [tab] 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
>     provider="@PROVIDER" 
> )
>
> # Upload a file for fine-tuning
> file = portkey.files.create(
>     file="dataset.jsonl",
>     purpose="fine-tune"
> )
>
> print(file)
> ```

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

### Create a fine-tuning job

> [tab] 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
>     provider="@PROVIDER" 
> )
>
> # 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] 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
>     provider:"@PROVIDER"    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] 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(
>         provider="@PROVIDER",
>         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] REST API
>
> ```sh theme={"system"}
> curl -X POST --header 'Content-Type: application/json' \
>  --header 'x-portkey-api-key: <portkey_api_key>' \
>  --header 'x-portkey-provider: @provider' \
>  --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'
> ```

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](https://docs.portkey.ai/docs/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).

***

> [card] [Portkey is now PRISMA AIRS AI Gateway. See it in action.](https://www.paloaltonetworks.in/ai-security/ai-gateway?utm_source=portkey\&utm_medium=referral\&utm_campaign=prisma_airs\&utm_content=docs_nav#contact)
>
> Contact Us


## Related topics

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


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