kennysato/albef-demo

Multitask (Albef)

Overview

A research-oriented Albef prototype targeting Multitask. The included base setup documents defaults and file formats without presenting unverified performance numbers.

Expect rough edges — this is a working sketch, not a polished release.

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

Citation

If this repository is useful for your work, a link back is appreciated. No formal citation is required yet.

Default Experiment Recipe

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

Training Details

• Framework: PyTorch
• Precision: fp32
• Init scheme: xavier uniform
• Hardware: a single consumer GPU is enough
• Typical run: minutes, not hours

Architecture

• Architecture: Albef
• Scale: base
• Attention: linear
• Fusion: low rank
• Activation: mish
• Normalization: scalenorm

Acknowledgments

Thanks to the maintainers of the open-source libraries this project leans on.

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.

Evaluation Guidance

A useful first evaluation would use a task-specific held-out 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.

Files

• inference.py — primary artifact
• README.md — this documentation
• config.json — architecture configuration
• training_args.json — default experiment settings
• model.safetensors — initialization checkpoint
• NOTES.md — informal to-do notes
• examples/sample_usage.py — usage sketch

License

Distributed under the unknown license. External datasets keep their own terms — check them separately.