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.
MIT License
This hands-on walks you through fine-tuning an open source SLM/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. This hands-on is suitable for the following purposes:
Before starting, you have met the following requirements:
Standard_DS11_v2
(2 cores, 14GB RAM, 28GB storage, No GPUs).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.Standard_DS11_v2
(2 cores, 14GB RAM, 28GB storage, No GPUs).git clone https://github.com/Azure/slm-innovator-lab.git
conda activate azureml_py310_sdkv2
pip install -r requirements.txt
1_training_mlflow.ipynb
and 2_serving.ipynb
, respectively.1_training_custom.ipynb
and 2_serving.ipynb
, respectively.phi3/dataset-preparation
folder.1_training_mlflow.ipynb
and 2_serving.ipynb
, respectively.config.yml
.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.
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This sample code is provided under the MIT-0 license. See the LICENSE file.