Fine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head
Making developers awesome at machine learning In this article, you will learn how to compare two strategies for adapting a large language model to a specific domain: fine-tuning with LoRA versus training a scikit-learn classifier on frozen embeddings. This article presents a practical comparison of fine-tuning a local LLM with LoRA (Low-Rank Adaptation, an algorithm for efficient weight updating) against training only a downstream scikit-learn classifier on frozen embeddings. We cover, analyze, and discuss when each strategy is preferred based on cost and performance. To make the most of the practical example below, we recommend a basic level of familiarity with LLMs, key processes like model fine-tuning, and associated libraries like Hugging Face’s transformers. If you are running this practical comparison on Google Colab, first and foremost, make sure you set the running environment…