Ribeiro87/quick-multitask34

Beit for Multitask

Overview

Working implementation of Beit for Multitask using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.

The whole setup fits in a single module on purpose.

Architecture

1. Architecture: Beit
2. Scale: base
3. Attention: grouped query
4. Fusion: low rank
5. Activation: gelu tanh
6. Normalization: layernorm

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.

Environment

1. Python: >= 3.9
2. torch: 2.x
3. safetensors: any recent
4. transformers: optional, for loaders

Repository status

1. The Python file contains the model and runnable example or training entry point.
2. config.json records the generated architecture settings.
3. training_args.json records the default experiment recipe.
4. model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.
5. No benchmark score is claimed in this repository.

Quick check

python model.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.

Default experiment recipe

The included configuration uses sgd with a exponential schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.

Files

File	Purpose
model.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

Released under apache-2.0. Review the source-data terms separately when this repository is used with external datasets.