How to Fine-Tune LFM2 Using QLoRA and DPO: A Complete Step-by-Step Coding Tutorial on Google Colab
This tutorial demonstrates how to fine-tune the LFM2 model using QLoRA and Direct Preference Optimization (DPO) on Google Colab. It covers loading the base LFM2 model with 4-bit quantization, preparing a dataset for supervised fine-tuning (SFT), and training a lightweight LoRA adapter. The process is extended with DPO to align the model's responses based on user preferences, resulting in an improved checkpoint ready for deployment. AI
IMPACT Provides a practical, step-by-step guide for customizing existing LLMs, potentially lowering the barrier for specialized model development.