Tech Study Guide

Advanced Fine-Tuning

Advanced fine-tuning with full tuning, LoRA, QLoRA, adapter composition, multi-adapter serving, dataset mixing, long-context tuning, packing, catastrophic forgetting, and safety-preserving releases.

machine-learning finetuning lora advanced

Advanced Fine-Tuning

Advanced fine-tuning is less about running a trainer and more about preserving behavior while changing a narrow capability. The hard problems are dataset mixture, template compatibility, forgetting, safety regression, and serving artifacts.

Command Examples

python -c "import transformers, peft; print(transformers.__version__); print(peft.__version__)"

Example output and meaning:

Command Example output What it does
Python snippet A version, tensor shape, score, retrieved IDs, metric delta, or explicit error. Turns the example into a measurable model, data, or pipeline signal.

Method Selection

Method Use Risk
Full fine-tune Maximum adaptation with enough data/compute. Cost, forgetting, rollback complexity.
LoRA Efficient targeted adaptation. Target-module and rank choices.
QLoRA Memory-constrained adapter training. Quantization/runtime compatibility.
Adapter composition Combine task or domain adapters. Interference and routing complexity.
Long-context tuning Teach behavior over long prompts. Expensive examples, positional limits, eval gaps.

Dataset Mixing

Mixing controls what behavior survives. A domain dataset alone can overfit style and erase general instruction behavior. A good mixture usually includes:

  • target task examples,
  • general instruction examples,
  • refusal and safety examples,
  • hard negatives,
  • old golden cases,
  • held-out domain sources.

Multi-Adapter Serving

Serving many adapters over one base model reduces memory but adds routing, compatibility, and cache pressure. Version base model, tokenizer, chat template, adapter, rank, dtype, and merge state together.

Practical Lab: Fine-Tune Release Packet

adapter:
  base_model:
  tokenizer:
  chat_template:
  target_modules:
  rank:
  dataset_manifest:
  eval_report:
  safety_report:
  merged_vs_unmerged_diff:
  rollback_command:

Study Cards

Question

Why does dataset mixing matter in fine-tuning?

Answer

It controls which old behaviors are preserved while the target task improves.

Question

What is adapter composition risk?

Answer

Adapters trained for different tasks can interfere when combined or routed poorly.

Question

Why compare merged and unmerged adapters?

Answer

Merging can change precision and behavior, so it needs its own release check.

References