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I've read the LoRA theory five times and still haven't run one fine-tune of my own.
What you'll have at the end
A LoRA adapter trained on your own small dataset, saved to a file you can reload
You need
A dataset file already saved in the exact chat-turn shape your trainer expects, and a free account on a hosted notebook service that can hand you a GPU.
Not covered
Choosing which task actually needs fine-tuning, shaping raw examples into that dataset file, and squeezing extra quality out of the exact rank, alpha, and learning rate this run starts with.
Leans on
Format your first fine-tuning dataset
you don't yet have a dataset file in the shape a trainer expects to point this recipe at
Pick starting values for LoRA rank, alpha, and learning rate
the modest defaults here got you a working run, and you want to tune rank, alpha, and learning rate for real quality instead of a first pass
Fix a CUDA out of memory crash with QLoRA
the run in this recipe crashes partway through with a memory error instead of finishing
Catch an overfit fine-tune before you ship it
your training loss drops well below the healthy range and you need to know whether the adapter is actually usable
Checked 15 Aug 2026
Part of the LLM Fine-tuning cookbook