slicktune.tuner

High-level Tuner API composing model × strategy × objective × data × metrics.

Classes

FitResult	Result of a completed Tuner.fit() call.
Tuner	Composable fine-tuning entry point.

Module Contents

class slicktune.tuner.FitResult

Result of a completed Tuner.fit() call.

Parameters:

• output_dir (Path) – Directory containing the saved adapter / model and metrics.

• metrics (TrainingMetrics) – Collected training metrics.

• model (Any) – Trained model (PEFT or full).

• tokenizer (PreTrainedTokenizerBase) – Tokenizer used during training.

metrics: slicktune.metrics.TrainingMetrics

model: Any

output_dir: pathlib.Path

tokenizer: transformers.PreTrainedTokenizerBase

class slicktune.tuner.Tuner

Composable fine-tuning entry point.

Parameters:

• model_id (str) – Hugging Face model id or local path.

• strategy (Strategy) – Parameter-update strategy (LoRA, DoRA, AdaLoRA, QLoRA, full, …).

• objective (Objective) – Training objective (SFT, DPO, ORPO, or KTO).

• output_dir (str or Path) – Where checkpoints, adapter weights, and metrics are written.

• max_seq_length (int, optional) – Maximum sequence length, by default 512.

• num_train_epochs (float, optional) – Number of epochs, by default 3.0.

• per_device_train_batch_size (int, optional) – Batch size per device, by default 1.

• gradient_accumulation_steps (int, optional) – Gradient accumulation steps, by default 4.

• learning_rate (float, optional) – Learning rate, by default 2e-4.

• logging_steps (int, optional) – Logging frequency, by default 1.

• save_steps (int, optional) – Checkpoint frequency, by default 50.

• seed (int, optional) – Random seed, by default 42.

• eval_data (str or Path or Dataset or None, optional) – Optional holdout SFT JSONL/dataset for perplexity after fit.

• probe_path (str or Path or None, optional) – Optional probe JSONL judged after fit (substring or custom judge).

• judge (Judge or None, optional) – Judge used with probe_path; defaults to SubstringJudge.

eval_data: str | pathlib.Path | datasets.Dataset | None = None

fit(data: str | pathlib.Path | datasets.Dataset) → FitResult

Run fine-tuning for the configured objective.

Parameters:

data (str or Path or Dataset) – Path to objective-specific JSONL, or an in-memory dataset.

Returns:

FitResult – Trained artifacts and metrics.

Raises:

• TypeError – If the objective is not supported.

• ValueError – If required dataset columns are missing.

gradient_accumulation_steps: int = 4

judge: slicktune.eval.Judge | None = None

learning_rate: float = 0.0002

logging_steps: int = 1

max_seq_length: int = 512

model_id: str

num_train_epochs: float = 3.0

objective: slicktune.types.Objective

output_dir: str | pathlib.Path

per_device_train_batch_size: int = 1

probe_path: str | pathlib.Path | None = None

save_steps: int = 50

seed: int = 42

strategy: slicktune.types.Strategy