Fine-Tune LLM in VS Code AI Toolkit Locally on Custom Dataset
AI Summary
Summary: Microsoft AI Toolkit for VS Code
- Introduction:
- Microsoft announced an AI toolkit for Visual Studio Code (VS Code).
- Simplifies AI application development.
- Integrates Azure AI Studio tools and models, Hugging Face catalog.
- Toolkit allows browsing, downloading, fine-tuning, and deploying AI models.
- Toolkit Features:
- Browse AI models from Azure ML and Hugging Face.
- Download models for local fine-tuning and testing.
- Deploy fine-tuned models to Azure Cloud.
- Demonstration:
- Video tutorial on installing and using the AI toolkit on Windows.
- Fine-tuning a model (53 mini 4K instruct) locally in VS Code.
- Using a custom dataset from Hugging Face or other sources.
- Sponsorship:
- Mast Compute sponsored the VM and NVIDIA RTX a6000 GPU used in the video.
- Discount code provided for GPU rental services.
- Prerequisites:
- Windows users need Windows Subsystem for Linux.
- GPU required for model fine-tuning.
- Fine-Tuning Process:
- Adjusting model parameters to adapt to specific tasks or datasets.
- Techniques like Low Rank Adaptation (Lora) and Quantized Low Rank Adaptation (QLora) are used.
- Using the Toolkit:
- Install the AI toolkit extension in VS Code.
- Create a fine-tuning project and configure it.
- Select a model from the catalog.
- Configure dataset and fine-tuning settings.
- Generate a Hugging Face token for access.
- Set up the project environment in VS Code.
- Modify configuration files as needed.
- Run fine-tuning scripts and monitor the process.
- Conclusion:
- The toolkit streamlines the process of fine-tuning AI models.
- Users can fine-tune models on their own datasets within VS Code.
- The tutorial covers the setup and execution of a fine-tuning project.