LLM Adaptation

Change a model's weights so it does a task better: supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT: LoRA, QLoRA, DoRA), full fine-tuning, distillation and compression recovery, plus the dataset and tokenizer work that decides whether any of it succeeds.

Deciding whether to adapt at all (prompt vs thinking vs RAG vs fine-tune vs post-train) is not this skill. Use the single build ladder in ai-architecture-advisor first, and come here once it says "fine-tune" or "distill".

When to Use This Skill

• Configuring LoRA or QLoRA: rank, alpha, alpha convention, target modules, learning rate
• Choosing full fine-tuning vs PEFT, or escalating when LoRA plateaus
• Building an SFT dataset: chat templates, prompt-token loss masking, packing, contamination control
• Distilling a large teacher into a smaller student, or recovering quality after pruning
• Diagnosing tokenizer fragmentation, fertility, or glitch tokens on a subset of inputs
• Deciding whether a fine-tune pays back (break-even on volume and stability)

Scope Boundaries