Fine‑tuning large language models on modest hardware is a common pain point for developers, researchers, and small teams. The main obstacles are limited GPU memory, the complexity of chaining multiple libraries, and the difficulty of aligning model behavior with human preferences without expensive annotation pipelines. This tutorial tackles those issues head‑on by delivering a lightweight, end‑to‑end workflow that takes Liquid AI’s LFM2‑1.2B from a raw checkpoint to a preference‑aligned model using only open‑source tools that run comfortably on a free Colab GPU.
First, we address memory constraints with QLoRA: the base model is loaded in 4‑bit precision, slashing VRAM usage while preserving the expressive power needed for chat tasks. A simple tokenizer tweak (setting pad_token) ensures smooth batching. Next, we build a supervised fine‑tuning dataset from a public chat corpus, keeping only the message turns. A low‑rank LoRA adapter (r=16) is attached to all linear layers, and the SFT trainer runs for just a handful of steps, proving that even brief instruction tuning can noticeably improve responses.
After SFT, we merge the adapter back into the base model, creating a standalone checkpoint ready for the next stage. For teams that care about tone, safety, or specific business styles, we add a Direct Preference Optimization step using a tiny set of chosen‑rejected pairs. Another LoRA adapter tunes the model to favor helpful, polite, or concise answers, and the final merge yields a model that aligns with human preferences without requiring large‑scale reinforcement learning.
The entire pipeline—installation, quantization, SFT, merging, optional DPO, and final saving—fits into a single notebook, needs no paid resources, and produces a deployable artifact in minutes. By following these concrete steps, anyone can overcome hardware limits, avoid cumbersome setup, and obtain a custom LFM2 model ready for testing or production.
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