---
license: cc-by-4.0
tags:
- pytorch
- poolformer
- multitask
- wip
---

# Working Poolformer implementation for Multitask

## Overview

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

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## Repository status

One Python module contains the model and a runnable example.
Generated configuration lives in `config.json`; the recipe in `training_args.json`.
The checkpoint is an initialization artifact and should not be read as a trained model.
Scores are deliberately absent.

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## Default experiment recipe

Defaults: **novograd** optimizer, **step** schedule. Treat these as starting points; a real run should sweep learning rate and batch size before drawing conclusions.

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## Limitations

- weights are random-init, so outputs carry no signal
- robustness, fairness and domain transfer are untested
- the API may change without notice while the design settles

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## Evaluation guidance

- pick a task dataset such as a task-specific held-out set
- fix a tuning budget in advance
- compare against a capacity-matched baseline
- publish raw logs next to any claim

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## Citation

```bibtex
@misc{workingpoolformerimp},
  title = {Working Poolformer implementation for Multitask},
  year = {{2026}},
  publisher = {{Hugging Face}},
}
```

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## Quick check

Run `python eval.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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## Files

- `eval.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
- `examples/sample_usage.py` — usage sketch

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## License

Distributed under the **cc-by-4.0** license. External datasets keep their own terms — check them separately.