Colab tutorial demonstrates QLoRA + DPO fine-tuning pipeline for Liquid AI's LFM2

A Google Colab tutorial documents an end-to-end, open-source workflow to fine-tune Liquid AI’s LFM2 for chat-style tasks, using QLoRA for efficient quantized loading and DPO to shape response preferences. The notebook demonstrates the complete pipeline from loading the base checkpoint to producing an SFT-tuned, preference — aligned checkpoint, enabling developers on Colab to test or deploy a chat-adapted LFM2 model with minimal memory overhead.

The guide lists required packages and specific version minimums (transformers>=4.55, trl>=0.12, peft>=0.13, datasets>=2.20, accelerate>=0.34, and bitsandbytes) and targets MODEL_ID "LiquidAI/LFM2-1.2B". It provides a BitsAndBytesConfig set up for 4 — bit nf4 quantization, auto-detects BF16 when supported, and defines training variables including SFT_SAMPLES=500, SFT_STEPS=60, DPO_STEPS=40 and MAX_LEN=1024.

Implementation steps walk through loading the base LFM2 checkpoint with QLoRA, preparing supervised chat datasets, and running lightweight fine-tuning using TRL and PEFT. The notebook shows how to prepare a LoRA adapter (via SFTConfig and SFTTrainer), merge the adapter back into the model, and optionally extend the pipeline with DPO (DPOConfig and DPOTrainer) to refine preferences using chosen and rejected answers. A small chat-generation helper is included for quick evaluation.

The combined strategy — QLoRA for efficient weight handling, LoRA adapters for lightweight supervised fine-tuning, and DPO for preference alignment — yields a compact, preference — aware checkpoint the author presents as ready for further testing or deployment. The workflow emphasizes cost-and memory — efficiency suitable for Colab, but it explicitly asserts that a GPU runtime is required and that numeric precision (bfloat16 or float16) affects feasibility and performance. Overall, the notebook packages a reproducible, resource — conscious route to adapt Liquid AI’s LFM2 to conversational tasks on Colab, balancing low-memory quantization with adapter — based tuning and optional preference optimization for improved response behavior.