Working Beit implementation for Matching

Overview

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

Expect rough edges — this is a working sketch, not a polished release.

---

Quick Check

python predict.py --help

Inspect the script's __main__ block for its generated smoke-test example. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.

---

Changelog

1. 2026-10-02 — initial upload
2. 2026-10-21 — config and docs sync

---

Repository Status

1. The Python file contains the model and runnable example or training entry point.
2. config.json records the generated architecture settings.
3. training_args.json records the default experiment recipe.
4. model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.
5. No benchmark score is claimed in this repository.

---

Citation

@misc{workingbeitimplement},
  title = {Working Beit implementation for Matching},
  year = {{2026}},
  publisher = {{Hugging Face}},
}

---

Architecture

Item	Value
Architecture	Beit
Scale	huge
Attention	flash
Fusion	tucker
Activation	gelu tanh
Normalization	scalenorm

---

Evaluation Guidance

A first evaluation could use a paired validation set. Report the metric across at least three seeds and include a matched-capacity baseline; keep logs and library versions with any published number.

---

Default Experiment Recipe

The included configuration uses sgd with a constant warmup schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.

---

Limitations

The checkpoint is untrained and unaudited. Treat the repository as an experimental starting point; anything learned from a future trained version must be documented separately.

---

Files

File	Purpose
predict.py	primary artifact
README.md	this documentation
config.json	architecture configuration
training_args.json	default experiment settings
model.safetensors	initialization checkpoint
NOTES.md	informal to-do notes

---

License

Released under cc-by-4.0. Review the source-data terms separately when this repository is used with external datasets.