This is the 1st version of the MaaP FT on AI Studio doc.

Author: FillipeMagno

articles/ai-studio/how-to/fine-tune-maap.md

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+---
+title: Fine-tune models using User-managed compute
+titleSuffix: Azure AI Studio
+description: Learn how to do fine-tune models using User-managed compute.
+manager: scottpolly
+ms.service: azure-ai-studio
+ms.topic: how-to
+ms.date: 10/02/2024
+ms.reviewer: vkann
+reviewer: kvijaykannan
+ms.author: mopeakande
+author: msakande
+ms.custom: references_regions
+
+#customer intent: As a Data Scientist, I want to learn how to fine-tune models using user managed compute <what> so that <why>
+---
+
+# Fine-tune using User-managed compute
+
+In this article
+  - [Fine-tune using User-managed compute](#distillation)
+  - [Related Contents](#next-steps)
+
+
+## How to fine-tune foundation models using your own training data
+
+In order to improve model performance in your workload, you might want to fine tune a foundation model using your own training data. You can easily fine-tune these foundation models by using either the fine-tune settings in the studio or by using the code based samples.
+
+## Fine-tune using the User-managed compute
+
+You can access the fine-tune settings form using one of the following methods:
+1.	Choose the "Fine-Tuning" option from the left menu, and then select any foundation model.
+
+2.	Choose the "Model Card" option from the left menu for any foundation model, and then click the ‘Fine-tune’ button on the model card.
+Select the suitable Service method to fine-tune your model. You can choose between 'Hosted fine-tuning' or 'User-managed compute'. If you intend to use your own compute resources, select the 'User-managed compute' option. To learn more about Fine-tuning using Serverless API, refer to the related content articles.
+
+
+  > [!NOTE]
+    > Some foundation models support only the 'User-managed compute' option.
+
+![Fine tune options in AI Studio](./fine-tune-options.png)
+
+### Fine-tune Settings:
+
+#### Basic Settings
+
+- In the basic settings, provide a name for the fine-tuned model.
+
+#### Compute
+
+- Provide the Azure Machine Learning Compute cluster you would like to use for fine-tuning the model. Fine-tuning needs to run on GPU compute. Ensure that you have sufficient compute quota for the compute SKUs you wish to use.
+
+![Fine tune compute in AI Studio](./fine-tune-compute.png)
+
+#### Training Data
+
+1.	Pass in the training data you would like to use to fine-tune your model. You can choose to either upload a local file (in JSONL, CSV or TSV format) or select an existing registered dataset from your workspace.
+
+2.	Once you've selected the dataset, you need to map the columns from your input data, based on the schema needed for the task. For example: map the column names that correspond to the 'sentence' and 'label' keys for Text Classification.
+
+![Fine tune training data in AI Studio](./fine-tune-training-data.jpeg)
+
+
+#### Validation data
+
+- Pass in the data you would like to use to validate your model. Selecting Automatic split reserves an automatic split of training data for validation. Alternatively, you can provide a different validation dataset.
+
+#### Test Parameters 
+
+- Tuning hyperparameter is essential for optimizing large language models (LLMs) in real-world applications. It allows for improved performance and efficient resource usage. You can choose to keep the default settings or customize parameters like epochs or learning rate.
+
+#### Review 
+
+- Select Finish in the fine-tune form to submit your fine-tuning job. Once the job completes, you can view evaluation metrics for the fine-tuned model. You can then deploy this model to an endpoint for inferencing.
+
+## Related Contents
+- [Fine-tuning in Azure AI Studio - Azure AI Studio | Microsoft Learn](../fine-tuning-overview.md)
+- [Fine-tune a Llama 2 model in Azure AI Studio](../fine-tune-model-llama.md)
+- [Fine-tune a Phi-3 model in Azure AI Studio](../fine-tune-phi-3.md)
+- [Deploy Phi-3 family of small language models with Azure AI Studio](../deploy-models-phi-3.md)