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.
RANK_REASON This is a tutorial demonstrating a technical process for fine-tuning an existing model, not a novel research paper or a new model release. [lever_c_demoted from research: ic=1 ai=1.0]
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