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license: unknown
tags:
- pytorch
- vit
- multitask
- initialization
- smoke-test
---
# ViT experiments: Multitask
## Overview
This repository is a compact, custom PyTorch implementation of **ViT** for **Multitask**. The **tiny** configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release.
## Quick check
```bash
python predict.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.
## Acknowledgments
Thanks to the maintainers of the open-source libraries this project leans on.
## Repository status
+ Single-file implementation; the `__main__` block runs a smoke test.
+ `config.json` and `training_args.json` hold the generated settings.
+ `model.safetensors` is freshly initialized — plumbing, not results.
+ No benchmark numbers are reported on purpose.
## Training details
+ **Framework**: PyTorch
+ **Precision**: fp32
+ **Init scheme**: xavier
+ **Hardware**: a single consumer GPU is enough
+ **Typical run**: minutes, not hours
## Default experiment recipe
Defaults: **rmsprop** optimizer, **onecycle** schedule. Treat these as starting points; a real run should sweep learning rate and batch size before drawing conclusions.
## Architecture
+ **Architecture**: ViT
+ **Scale**: tiny
+ **Attention**: multi query
+ **Fusion**: bilinear
+ **Activation**: swish
+ **Normalization**: instancenorm
## 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
## Files
+ `predict.py` — primary artifact
+ `README.md` — this documentation
+ `config.json` — architecture configuration
+ `training_args.json` — default experiment settings
+ `model.safetensors` — initialization checkpoint
## License
Released under **unknown**. Review the source-data terms separately when this repository is used with external datasets.
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