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license: mit
tags:
- pytorch
- cnn-transformer
- matching
---
# Cnn Transformer for Matching
## Overview
This is an experimental **Cnn Transformer** codebase for **Matching**. It keeps the **xlarge** setup intentionally manageable so architecture changes can be inspected before a full training run.
## 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.
## Architecture
| Item | Value |
|---|---|
| Architecture | Cnn Transformer |
| Scale | xlarge |
| Attention | grouped query |
| Fusion | cross attention |
| Activation | gelu tanh |
| Normalization | groupnorm |
## Default experiment recipe
The included configuration uses **lion** with a **exponential** 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.
## Quick check
```bash
python inference.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.
## Evaluation guidance
A useful first evaluation would use **a paired validation set**, report the task metric across at least three seeds, and include a matched-capacity baseline. Keep training logs and environment versions with any published result.
## 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.
## Files
- `inference.py` — primary artifact
- `README.md` — this documentation
- `config.json` — architecture configuration
- `training_args.json` — default experiment settings
- `model.safetensors` — initialization checkpoint
## License
Released under **mit**. Review the source-data terms separately when this repository is used with external datasets.
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