tinker_cookbook.distillation.sdft.Config

class tinker_cookbook.distillation.sdft.Config()

Configuration for SDFT training.

Key parameters:

• topk: Number of top tokens for distillation (default 20). Set to 0 for the importance-sampling fallback. K=20 matches full-vocabulary KL in practice.
• learning_rate: For LoRA, use 5e-4 to 1e-3. The top-K CE loss produces larger gradients than SFT at the same LR due to more completion tokens per step (on-policy generation), so use the lower end of the range.
• teacher_sync_every: Optional periodic hard-sync of student weights into the teacher (approximating EMA). None = static frozen teacher, which works comparably to EMA in our experiments.

See main for the training loop.

Fields:

• model_name ( str) – Model
• recipe_name ( str)
• renderer_name ( str | None, default: None)
• lora_rank ( int, default: 128)
• base_url ( str | None, default: None)
• learning_rate ( float, default: 2e-05) – Training
• max_tokens ( int, default: 2048)
• temperature ( float, default: 1.0)
• loss_fn ( LossFnType, default: 'cross_entropy')
• topk ( int, default: 20) – SDFT-specific
• reverse ( bool, default: False)
• demo_template ( str, default: DEFAULT_DEMO_TEMPLATE)
• system_prompt ( str | None, default: None)
• teacher_sync_every ( int | None, default: None)
• max_context_length ( int, default: 32768)
• evaluator_builders ( list[SamplingClientEvaluatorBuilder], default: []) – Evaluation
• eval_every ( int, default: 20)
• save_every ( int, default: 20)
• num_substeps ( int, default: 1) – Standard infra
• log_path ( str)
• wandb_project ( str | None, default: None)
• wandb_name ( str | None, default: None)
• load_checkpoint_path ( str | None, default: None)
• max_steps ( int | None, default: None)
• enable_trace ( bool, default: False)
• span_chart_every ( int, default: 0)