Hybrid experiments: Multitask

Overview

This repository is a compact, custom PyTorch implementation of Hybrid for Multitask. The small configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release.

Feedback on the structure is welcome; the code is intentionally short.

Repository Status

  • Single-file implementation; the __main__ block runs a smoke test.
  • config.json and training_args.json hold the generated settings.
  • model.safetensors is freshly initialized โ€” plumbing, not results.
  • No benchmark numbers are reported on purpose.

Quick Check

Run python model.py --help for the built-in smoke test. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.

Default Experiment Recipe

The generated recipe uses lamb with a step schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

Limitations

  • weights are random-init, so outputs carry no signal
  • robustness, fairness and domain transfer are untested
  • the API may change without notice while the design settles

Files

  • model.py โ€” primary artifact
  • README.md โ€” this documentation
  • config.json โ€” architecture configuration
  • training_args.json โ€” default experiment settings
  • model.safetensors โ€” initialization checkpoint

License

Released under mpl-2.0. Review the source-data terms separately when this repository is used with external datasets.

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