Add inference.py, README.md, config.json, training_args.json, model.safetensors, USE_POLICY.md
db321a1 verified | 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. | |
| --- | |
| ## 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. | |
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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 | |
| --- | |
| ## 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. | |