tinker_cookbook.supervised.Config
class tinker_cookbook.supervised.Config()
Configuration for supervised fine-tuning.
This chz dataclass holds every knob for a supervised learning run: model
selection, learning-rate schedule, checkpointing cadence, evaluation, and logging.
from tinker_cookbook.supervised import train
config = train.Config(
log_path="~/logs/sft-run",
model_name="Qwen/Qwen3-8B",
dataset_builder=my_dataset_builder,
learning_rate=1e-4,
)
asyncio.run(train.main(config))
Fields:
- log_path (str)
- model_name (str)
- recipe_name (str)
- load_checkpoint_path (str | None, default:
None) - renderer_name (str | None, default:
None) - dataset_builder (SupervisedDatasetBuilder)
- learning_rate (float, default:
0.0001) - lr_schedule (LRSchedule, default:
'linear') - num_epochs (int, default:
1) - lora_rank (int, default:
32) - base_url (str | None, default:
None) - evaluator_builders (list[EvaluatorBuilder], default:
[]) – Checkpointing and evaluation (0 = disabled for *_every fields) - infrequent_evaluator_builders (list[EvaluatorBuilder], default:
[]) - save_every (int, default:
20) - eval_every (int, default:
10) - infrequent_eval_every (int, default:
100) - ttl_seconds (int | None, default:
604800) – 7 days - rolling_save_every (int, default:
0) – but skips the sampler-weight export, making it cheaper than periodic checkpoints. - rolling_ttl_seconds (int, default:
7200) – 2 hours - async_periodic_saves (bool, default:
False) – checkpoint always blocks regardless of this setting. - adam_beta1 (float, default:
0.9) - adam_beta2 (float, default:
0.95) - adam_eps (float, default:
1e-08) - wandb_project (str | None, default:
None) - wandb_name (str | None, default:
None) - enable_trace (bool, default:
False) - span_chart_every (int, default:
0) - max_steps (int | None, default:
None) – Maximum number of training steps. If None, train for num_epochs * n_batches. - submit_ahead (int, default:
1) – 0 = no pipelining, 2+ = deeper pipeline.