slicktune 0.1.0rc1

SlickML fine-tuning toolkit: composable strategies, objectives, and metrics for LLMs

SlickTune 🧩: Composable LLM fine-tuning by SlickML

Fine-tuning is an orthogonal stack — swap any axis without rewriting the others:

model  ×  strategy  ×  objective  ×  data  ×  metrics

🧠 Philosophy

slick-tune is a small, composable toolkit for teaching LLMs new facts and behaviors with Transformers + PEFT + TRL. LoRA / QLoRA are PEFT adapters; full FT updates every weight. The goal is the same SlickML spirit: prototype fast 🏎, keep axes orthogonal, and measure whether the model actually learned your facts 🔎.

🧩 Abstractions

flowchart TB
  subgraph inputs [Inputs]
    modelId[model_id]
    dataJsonl[data JSONL]
  end

  subgraph axes [Composable axes]
    strategyNode["Strategy: LoRA / QLoRA / Full"]
    objectiveNode["Objective: SFT then DPO / GRPO"]
  end

  subgraph core [Tuner fit]
    tuner[Tuner]
    loadStep[load model and tokenizer]
    applyStep[strategy.apply]
    trainStep[TRL trainer]
    metricsStep[MetricsTracker]
  end

  subgraph outputs [Outputs]
    checkpoint[adapter or checkpoint]
    metricsFile[metrics.json]
    probeRate[probe pass rate]
  end

  modelId --> tuner
  dataJsonl --> tuner
  strategyNode --> tuner
  objectiveNode --> tuner
  tuner --> loadStep --> applyStep --> trainStep --> metricsStep
  trainStep --> checkpoint
  metricsStep --> metricsFile
  checkpoint --> probeRate

Axis	Responsibility	Phase 1
Strategy	How weights change (PEFT vs full)	LoRAStrategy, QLoRAStrategy, FullStrategy
Objective	What is optimized / data contract	SFTObjective (DPO stubbed)
Data	Examples → chat messages	load_sft_jsonl
Metrics	Comparable run stats	MetricsTracker
Probe	Did the model learn your facts?	slick-tune probe

📌 Quick Start

from slicktune import LoRAStrategy, SFTObjective, Tuner

Tuner(
    model_id="HuggingFaceTB/SmolLM2-135M-Instruct",
    strategy=LoRAStrategy(r=8),
    objective=SFTObjective(),
    output_dir="outputs/sft_lora",
).fit("examples/data/about_amir.jsonl")

👤 Personal “about me” loop (recommended)

1. Edit examples/data/about_amir.jsonl with facts about you (or keep the SlickML starter facts) ✍️.
2. Edit examples/data/about_amir.probes.jsonl with questions and a must_contain substring that should appear after training 🎯.
3. Train a strategy on a tiny instruct model 🧪.
4. Probe the checkpoint — pass rate shows whether fine-tuning stuck ✅.

before FT  →  model guesses / hallucinates about you
after FT   →  probe answers contain your facts

🛠 Installation

Install Python >=3.10,<3.13 and uv, then simply run 🏃‍♀️:

uv sync

QLoRA (CUDA + bitsandbytes only) 🔥:

uv sync --extra qlora

Task runner is Poe the Poet (same idea as slick-ml, with uv instead of Poetry). Install the CLI once 🏃‍♀️:

uv tool install poethepoet
poe greet

Developer workflow (format / check / test) lives in CONTRIBUTING.md 🧑‍💻🤝.

🚂 Train each strategy

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

🟢 LoRA + SFT (default — works on Mac MPS / CPU / CUDA)

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

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

Or: poe train-lora / poe probe-lora / uv run python examples/run_sft_lora.py

🔵 QLoRA + SFT (CUDA required)

uv sync --extra qlora
uv run python examples/run_sft_qlora.py

On Apple Silicon, use LoRA instead — bitsandbytes 4-bit needs CUDA 🍎.

🟠 Full fine-tuning + SFT

uv run python examples/run_sft_full.py

Heavier on memory; prefer LoRA for iteration 💾.

📦 Data formats

SFT JSONL (any of these per line) 📝:

{"messages":[{"role":"user","content":"..."},{"role":"assistant","content":"..."}]}
{"prompt":"...","response":"..."}
{"instruction":"...","input":"...","output":"..."}

Probe JSONL 🕵️:

{"prompt":"Who is Amirhessam Tahmassebi?","must_contain":"SlickML"}

🗺 Roadmap

Phase	Scope
0–1 (now)	Skeleton, SFT + LoRA/QLoRA/full, metrics, personal probe loop
2	DoRA / AdaLoRA, richer eval
3	DPO / ORPO / KTO
4	GRPO / verifiable RL
5	Merge (TIES/DARE), multi-adapter
6	Optional PPO / multimodal

🧑‍💻🤝 Contributing to slick-tune

You can find the details of the development process in our Contributing guidelines. We strongly believe that reading and following these guidelines will help us make the contribution process easy and effective for everyone involved 🚀🌙.

Conventions in short: @dataclass classes, numpydoc docstrings, full type hints via ruff (ANN) + mypy, assertpy in tests (see .cursor/rules/).

❓ 🆘 📲 Need Help?

Please join our Slack Channel to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help 👨‍👩‍👧 👫 👨‍👩‍👦.