---
license: bsd-3-clause
tags:
- pytorch
- cnn-transformer
- generation
---

# 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.

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## Citation

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

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## 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.

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## 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.

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## 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.

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## 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.

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## Changelog

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

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## Training Details

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

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## 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.

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## 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 |

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## License

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