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" (Thinking Machines/Schulman, 2025-09), now the settled convention for LoRA/QLoRA SFT.

Target Modules

Target all-linear modules, not just attention:

target_modules = [
    "q_proj", "k_proj", "v_proj", "o_proj",   # attention
    "gate_proj", "up_proj", "down_proj",      # MLP — matters most
]

The MLP layers (gate_proj, up_proj, down_proj) matter most — attention-only targeting was the older, weaker convention. Dropping modules to save memory is a Failure Mode below, not a valid optimization.

Alpha and Learning Rate

• lora_alpha = 2 * r is the settled convention (NeurIPS 2025 "intruder dimensions" result). Don't hand-tune alpha independently of rank — derive it from rank every time.
• LoRA learning rate ≈ 10x the equivalent full-fine-tune LR. For QLoRA specifically, 2e-4 is the standard starting point. Full hyperparameter tables and worked examples: references/hyperparameters.md.

Rank by Task

Rank is task-shaped, not a single global default:

Higher rank isn't automatically better — it raises capacity to memorize as fast as it raises capacity to generalize. Start at the row matching the task, and only move up a row if the lower rank measurably underfits on held-out eval, not as a default hedge.

Effective Batch Size

Keep effective batch size under 32. This recipe was validated at that scale — pushing effective batch higher is an untested extrapolation, not a free throughput win.

Unsloth Defaults

Unsloth is the reference implementation this plugin assumes as the default fast path — except for messages-shaped conversational SFT with assistant_only_loss=True, where Unsloth 2026.7.x's compiled trainer has no messages-shaped path at all and the plain-TRL escape hatch (references/unsloth-trl-mapping.md) is the default for that combination, not a rare-regression fallback. Its out-of-the-box defaults, and why each one is set that way:

• lora_dropout=0 — the optimized kernel path assumes zero dropout; setting a nonzero value forfeits the fused-kernel speedup.
• bias="none" — bias terms add adapter parameters for negligible quality gain at this rank range.
• use_gradient_checkpointing="unsloth" — Unsloth's checkpointing variant, not vanilla HF checkpointing; saves roughly 30% VRAM over no checkpointing.
• optim="adamw_8bit" — 8-bit AdamW cuts optimizer-state memory with negligible quality impact at LoRA/QLoRA adapter scale.
• random_state fixed — pins LoRA initialization for reproducibility across runs; treat it like any other seed, not a tunable.

These show up together on the get_peft_model call:

model = FastLanguageModel.get_peft_model(
    model,
    r=32,
    target_modules=target_modules,
    lora_alpha=64,               # 2 * r
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",
    random_state=3407,
)

Exact kwarg names and their plain-TRL/PEFT equivalents, plus a full worked config including SFTConfig: references/unsloth-trl-mapping.md and references/hyperparameters.md.

LoRA vs QLoRA vs Full FT

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