ozdemirze/dissertation-generation

Generation (Hybrid)

Overview

A small Hybrid implementation for Generation, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release.

The whole setup fits in a single module on purpose.

Architecture

• Architecture: Hybrid
• Scale: nano
• Attention: flash
• Fusion: cross attention
• Activation: relu
• Normalization: groupnorm

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.

Default experiment recipe

The generated recipe uses lamb with a linear 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.

Limitations

The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.

Roadmap

Next steps, in no particular order: a cleaner entry point, input-shape docs, and a small evaluation script if time permits.

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.

Contact

Questions or corrections: open a discussion in this repository.

Files

File	Purpose
eval.py	primary artifact
README.md	this documentation
config.json	architecture configuration
training_args.json	default experiment settings
model.safetensors	initialization checkpoint

License

Distributed under the apache-2.0 license. External datasets keep their own terms — check them separately.