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How to train a PSSA checkpoint and benchmark it against a transformer with Pssa

Fit a small PSSA model on your own text, then score it against a matched transformer on held-out tokens.

Short answer: Clone https://github.com/Sparticle62ops/pssa and build the Pssa binary with cargo; then run pssa train corpus.txt --no-tui to fit a PSSA checkpoint on your text file; run pssa train-transformer corpus.txt to fit the CPU decoder-only baseline. All steps.

  • Top pick
  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forAn ML researcher or student who wants to test whether a non-transformer recurrent model learns faster than a transformer on the same corpus.

The problemComparing a new architecture to a baseline usually means building two training pipelines, which makes the comparison slow and easy to get wrong.

The outcomeA trained .pssa checkpoint, a matched transformer baseline, and held-out scores for both models side by side.

What you need first

  • A Rust toolchain (inferred from the Rust implementation and the cargo commands in the README)
  • The pssa binary built from the Sparticle62ops/pssa repository
  • A plain UTF-8 text file to train on
  • A terminal that supports the TUI, or use --no-tui for plain logs

Steps

  1. Clone https://github.com/Sparticle62ops/pssa and build the Pssa binary with cargo. (inferred, check the README)
  2. Run pssa train corpus.txt --no-tui to fit a PSSA checkpoint on your text file. (inferred, check the README)
  3. Run pssa train-transformer corpus.txt to fit the CPU decoder-only baseline. (inferred, check the README)
  4. Run pssa compare to replay the PSSA chain with a token and update matched transformer and score both on held-out tokens. (inferred, check the README)
  5. Read the JSON scores from pssa score and check the two models on the same held-out text.

Prompt for your agent

Use the Sparticle62ops/pssa repository. Train a PSSA checkpoint on <corpus-path> with `pssa train <corpus-path> --no-tui`, then run `pssa compare`. Report the held-out scores for both the PSSA and transformer models as JSON. Verify that both models were scored on the same held-out tokens before reporting a winner.

Caveat. The README says the architecture learns faster at matched parameters on its own corpus, so your results on a different corpus may differ and should be measured, not assumed.

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