Generation (Cnn Transformer)

Overview

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

Citation

If this repository is useful for your work, a link back is appreciated. No formal citation is required yet.

Quick Check

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

Evaluation Guidance

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

Limitations

The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.

Repository Status

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

Changelog

2026-10-04 — initial upload
2026-10-19 — config and docs sync

Training Details

Framework: PyTorch
Precision: fp32
Init scheme: xavier uniform
Hardware: a single consumer GPU is enough
Typical run: minutes, not hours

Default Experiment Recipe

The generated recipe uses rmsprop with a onecycle schedule.

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

Files

File	Purpose
`inference.py`	primary artifact
`README.md`	this documentation
`config.json`	architecture configuration
`training_args.json`	default experiment settings
`model.safetensors`	initialization checkpoint
`USE_POLICY.md`	use and redistribution notes

License

Distributed under the bsd-3-clause license. External datasets keep their own terms — check them separately.

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Safetensors

Model size

16.6k params

Tensor type

F32

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