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Fine Tune

Fine-tune language models with LoRA, QLoRA, or full fine-tuning. Covers unsloth (4x memory reduction), PEFT, trl SFTTrainer, DPO, instruction tuning with chat templates, dataset preparation, and evaluation. Use when fine-tuning any HuggingFace model on custom data.

damionrashford Updated
AI & ML

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Fine-Tune Skill

Fine-tune language models efficiently with LoRA, QLoRA, or unsloth.

Quick start

# Prepare dataset
uv run ${CLAUDE_SKILL_DIR}/scripts/prepare_dataset.py data/raw.csv --format alpaca --output data/train.jsonl

# Fine-tune with LoRA (unsloth)
python3 -c "
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained('mistralai/Mistral-7B-v0.1', load_in_4bit=True)
model = FastLanguageModel.get_peft_model(model, r=16, lora_alpha=32, target_modules=['q_proj','v_proj'])
# ... training loop
"

Workflow

  1. Prepare dataset — convert to alpaca/sharegpt format via scripts/prepare_dataset.py
  2. Choose method — LoRA (parameter-efficient), QLoRA (memory-efficient), full (highest quality)
  3. Configure LoRA — set r, lora_alpha, target_modules
  4. Train — SFTTrainer with packing=True for efficiency
  5. Merge — model.merge_and_unload() for deployment
  6. Evaluate — perplexity, ROUGE-L, task benchmarks

See references/fine-tune-guide.md for complete LoRA/QLoRA/unsloth documentation.

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npx skillmds@latest add damionrashford/fine-tune