Individual skill
lora-qlora-recipes
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.
plugins/llm-finetuning/skills/lora-qlora-recipes/SKILL.md
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- Last checked
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- License
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SKILL.mdcore · 7,673 bytesSKILL.mdcore · 7,673 bytesSKILL.mdcore · 7,673 bytesSKILL.mdcore · 7,673 bytesSKILL.mdcore · 7,673 bytesreferences/hyperparameters.mddoc · 5,873 bytesreferences/unsloth-trl-mapping.mddoc · 9,835 bytes
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# 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`.