Automated version control for LLMs using DVC and CI/CD

In the realm of large language models (LLMs), a robust system for version control is crucial for handling large datasets, model weights, and experiment metadata, which traditional systems like Git struggle to manage. Data Version Control (DVC) extends Git-like functionalities to data science workflows, allowing for seamless versioning of large files, model artifacts, and ML pipelines. This tutorial demonstrates how to integrate DVC into a CI/CD pipeline using CircleCI for automating experiment tracking and model versioning in an LLM training workflow, specifically showcasing a LoRA-based fine-tuning process. It involves setting up a Python development environment, utilizing a virtual environment for dependency management, and using CircleCI to automate training, DVC versioning, and pushing model artifacts to Google Drive as a remote storage backend. The process ensures a reproducible, transparent, and scalable workflow that supports collaborative model development and easy rollback to previous versions. This setup serves as a versatile foundation for any ML Ops-ready workflow and can be extended to more advanced machine learning scenarios.

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