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---
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.
---
## 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.
---
## 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.
---
## 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
---
## 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
---
## Citation
```bibtex
@misc{workingpoolformerimp},
title = {Working Poolformer implementation for Multitask},
year = {{2026}},
publisher = {{Hugging Face}},
}
```
---
## 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.
---
## 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
---
## License
Distributed under the **cc-by-4.0** license. External datasets keep their own terms — check them separately.