---
license: cc-by-4.0
tags:
- pytorch
- albef
- multitask
---

# Working Albef implementation for Multitask

## Overview

A small **Albef** implementation for **Multitask**, packaged with an explicit configuration and an initialization checkpoint. The **base** variant is a reproducible starting point, not a trained model release.

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## Quick Check

Run `python pipeline.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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## Contact

Questions or corrections: open a discussion in this repository.

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## Limitations

The checkpoint is untrained and unaudited. Treat the repository as an experimental starting point; anything learned from a future trained version must be documented separately.

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## Repository Status

1. Single-file implementation; the `__main__` block runs a smoke test.
2. `config.json` and `training_args.json` hold the generated settings.
3. `model.safetensors` is freshly initialized — plumbing, not results.
4. No benchmark numbers are reported on purpose.

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## Training Details

1. **Framework**: PyTorch
2. **Precision**: fp32
3. **Init scheme**: xavier
4. **Hardware**: a single consumer GPU is enough
5. **Typical run**: minutes, not hours

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## Architecture

1. **Architecture**: Albef
2. **Scale**: base
3. **Attention**: linear
4. **Fusion**: tucker
5. **Activation**: gelu tanh
6. **Normalization**: groupnorm

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## Default Experiment Recipe

The generated recipe uses lion with a constant warmup schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

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## Files

1. `pipeline.py` — primary artifact
2. `README.md` — this documentation
3. `config.json` — architecture configuration
4. `training_args.json` — default experiment settings
5. `model.safetensors` — initialization checkpoint

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## License

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