Unlock the full potential of your AI projects with the SLM Innovator Lab, powered by the Azure AIML Platform. Our lab is tailored for customers who excel in fine-tuning and deploying multiple SLM models on Azure, as well as those aiming to optimize base model performance through fine-tuning to create RAG applications. With the advanced capabilities of AI Studio, you can establish efficient and scalable LLMOps.
This hands-on lab is suitable for the following purposes:
- 1-day workshop (4-7 hours depending on customer) / 2-day workshop with LLMOps hands-on
- Hackathon starter code
- Reference guide for SLM fine-tuning&serving PoC/Prototype
Hands-on guide: https://azure.github.io/slm-innovator-lab/
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 AI Studio getting started: Create a project
-
[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 SLM/LLM fine-tuning] A single NVIDIA A100 GPU node (Standard_NC24ads_A100_v4) is recommended. If you do not have a dedicated quota or are on a tight budget, choose Low-priority VM.
-
[Compute cluster - for SLM/LLM deployment] A single NVIDIA V100 GPU node (Standard_NC6s_v3) or A single NVIDIA A100 GPU node (Standard_NC24ads_A100_v4) is recommended.
Please do not forget to modify the .env file to match your account. Rename .env.sample to .env or copy and use it
This workshop assumes that you are configuring in a public environment and you have access to the internet. If you are configuring in a private environment, you may need to set up a private network to access the services. The following are some common issues you may encounter when you configure in a private environment:
- If you set up the Azure ML workspace and Azure AI Studio in private network, you may need to set up a VPN or a private link to access the services.
- If you are using a low-priority VM, you may need to wait for the VM to be available. The availability of the VMs may vary by region.
- If you have blob storage, you can use it to store the data and models. However, you may need to set up the connection to the blob storage in the Azure ML workspace.
- If you have a quota issue, you may need to request a quota increase for the VMs or GPUs.
- Once you configure the network in Azure ML workspace, you can not change it. You may need to create a new workspace if you want to change the network.
- If you are using a compute instance which is not in the same region as the Azure ML workspace, you may need to set up a VPN or a private link to access the services.
- If you are using a compute instance which created in Azure AI Studio, you can't execute training jobs in the compute instance. You may need to create a new compute instance in Azure ML workspace.
- If you run into an PermissionMismatch error when you download the artifacts, you may need to asign the correct permission to the Azure ML workspace.
- Create your compute instance in Azure ML. For code development, we recommend Standard_DS11_v2 (2 cores, 14GB RAM, 28GB storage, No GPUs).
- Open the terminal of the CI and run:
git clone https://github.com/Azure/slm-innovator-lab.git cd slm-innovator-lab && conda activate azureml_py310_sdkv2 pip install -r requirements.txt
Expand
- Evolve-Instruct
- GLAN (Generalized Instruction Tuning)
- Auto Evolve-Instruct
- 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.
- microsoft/Phi-3-mini-128k-instruct: This is Microsoft's official Phi-3-mini-128k-instruct model.
- microsoft/Phi-3.5-mini-instruct: This is Microsoft's official Phi-3.5-mini-instruct model.
- microsoft/Phi-3.5-MoE-instruct: This is Microsoft's official Phi-3.5-MoE-instruct model.
- Korean language proficiency evaluation for LLM/SLM models using KMMLU, CLIcK, and HAE-RAE dataset
- daekeun-ml/Phi-3-medium-4k-instruct-ko-poc-v0.1
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 opencode@microsoft.com with any additional questions or comments.
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.
This sample code is provided under the MIT-0 license. See the LICENSE file.