Cost-Effective LoRA Fine-Tuning on a Single GPU with Unsloth

Train a custom 8B parameter model on your internal support transcripts in under an hour for less than $2 in compute.

Prerequisites

NVIDIA GPU with 16GB+ VRAM or Google Colab
Hugging Face account

Step 1: Step 1: Dataset Formatting & Tokenization

Structure your training pairs into ShareGPT or Alpaca conversational formatting with strict user and assistant tokens.

Step 2: Step 2: Applying 4-bit QLoRA Rank Decompositions

Use Unsloth to patch attention kernels for 2x faster training and 70% reduced memory footprint.