📌 Quick Start

Default demo model: HuggingFaceTB/SmolLM2-135M-Instruct (small enough for laptop smoke tests).

✅ LoRA + SFT + probes

uv run slicktune train \
  --strategy lora \
  --data examples/data/about_amir.jsonl \
  --eval-data examples/data/about_amir.eval.jsonl \
  --output outputs/sft_lora \
  --epochs 20

uv run slicktune probe \
  --model-dir outputs/sft_lora \
  --probes examples/data/about_amir.probes.jsonl

uv run slicktune eval \
  --model-dir outputs/sft_lora \
  --eval-data examples/data/about_amir.eval.jsonl \
  --probes examples/data/about_amir.probes.jsonl \
  --judge substring

Or with Poe / examples:

poe train-lora
poe probe-lora
poe eval-lora
# or
uv run python examples/run_sft_lora.py

✅ LoRA + DPO (preference pairs)

uv run slicktune train \
  --objective dpo \
  --strategy lora \
  --data examples/data/about_amir.prefs.jsonl \
  --eval-data examples/data/about_amir.eval.jsonl \
  --output outputs/dpo_lora \
  --epochs 10 \
  --beta 0.1

# or
poe train-dpo

✅ LoRA + KTO (unpaired labels)

uv run slicktune train \
  --objective kto \
  --strategy lora \
  --data examples/data/about_amir.kto.jsonl \
  --eval-data examples/data/about_amir.eval.jsonl \
  --output outputs/kto_lora \
  --epochs 10 \
  --beta 0.1

# or
poe train-kto

ORPO uses --objective orpo with the same preference JSONL as DPO (TRL experimental trainer).

✅ Python API

from slicktune import LoRAStrategy, SFTObjective, Tuner

Tuner(
    model_id="HuggingFaceTB/SmolLM2-135M-Instruct",
    strategy=LoRAStrategy(r=16, alpha=32),
    objective=SFTObjective(),
    output_dir="outputs/sft_lora",
    eval_data="examples/data/about_amir.eval.jsonl",
).fit("examples/data/about_amir.jsonl")

✅ Other strategies

uv run python examples/run_sft_dora.py
uv run python examples/run_sft_adalora.py
uv run python examples/run_sft_full.py
# CUDA + bitsandbytes:
uv sync --extra qlora && uv run python examples/run_sft_qlora.py

New to adapters? Read the Fine-Tuning Guide.