Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
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Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
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📦 Ships with wshobson-agents
</> SKILL.md
lora-qlora-recipes.SKILL.md
---name: lora-qlora-recipes
description: Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
---# LoRA & QLoRA Recipes
This skill assumes the routing decision already
happened — `finetuning-method-selection` should
have already pointed here because the data shape
is demonstrations (SFT), not preference pairs or
a verifiable reward signal. What follows is the
current best-practice recipe for configuring the
adapter itself: which modules to target, how to
size rank and alpha, what learning rate to use,
and when QLoRA buys real headroom versus when it
just adds risk. Dataset preparation and quality
checks are a separate concern — see
`dataset-curation`.
**Input:** a routing decision (SFT via LoRA/
QLoRA) plus a target size class.
**Output format:** a validated adapter config —
the kwarg values below, not free-form advice —
that `llm-finetuning-training-engineer` consumes
directly when it generates a runnable script.
## The Reference Recipe
The reference recipe is "LoRA Without Regret"
