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LLM Fine-tuning on Azure

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

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

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

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

License Summary

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