LLM Fine-tuning on Azure

Note: Please use https://github.com/Azure/azure-llm-fine-tuning instead of this repo.

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This hands-on walks you through fine-tuning an open source LLM on Azure and serving the fine-tuned model on Azure. It is intended for Data Scientists and ML engineers who have experience with fine-tuning but are unfamiliar with Azure ML and Mlflow. We are using the Microsoft Phi-3-mini-4k-instruct model, but you can use it freely as long as it is a public liecnse LLM registered with Hugging Face.

Contents

• Dataset preparation
• Fine tuning

Requirements

Before starting, you have met the following requirements:

• Azure ML getting started: Connect to Azure ML workspace and get your <WORKSPACE_NAME>, <RESOURCE_GROUP> and <SUBSCRIPTION_ID>.
• Azure ML CLI v2
• [Compute instance - for code development] A low-end instance without GPU is recommended: Standard_DS11_v2 (2 cores, 14GB RAM, 28GB storage, No GPUs).
• [Compute cluster - for LLM training] A single NVIDIA A100 GPU node (Standard_NC24ads_A100_v4) and a single NVIDIA V100 GPU node (Standard_NC6s_v3) is recommended. If you do not have a dedicated quota or are on a tight budget, choose Low-priority VM.

How to get started

1. Create your compute instance. For code development, we recommend Standard_DS11_v2 (2 cores, 14GB RAM, 28GB storage, No GPUs).
2. Open the terminal of the CI and run:

   git clone https://github.com/daekeun-ml/azure-llm-fine-tuning.git
   conda activate azureml_py310_sdkv2
   pip install -r requirements.txt

3. (Optional) If you are interested in dataset preprocessing, see the hands-ons in dataset-preparation folder.
4. Go to fine-tuning folder and modify config.yml.
5. Choose one of two options. By default, we recommend MLflow.
   • [Option 1. MLflow] Run 1_training_mlflow.ipynb and 2_serving.ipynb, respectively.
   • [Option 2. Custom] Run 1_training_custom.ipynb and 2_serving.ipynb, respectively.

References

• Azure Machine Learning examples
• Finetune Small Language Model (SLM) Phi-3 using Azure ML
• microsoft/Phi-3-mini-4k-instruct: This is Microsoft's official Phi-3-mini-4k-instruct model.
• daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-v0.1

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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License Summary

This sample code is provided under the MIT-0 license. See the LICENSE file.