ViT experiments: Generation

Overview

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

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Architecture

1. Architecture: ViT
2. Scale: small
3. Attention: multi query
4. Fusion: gated fusion
5. Activation: swish
6. Normalization: rmsnorm

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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.

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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.

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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.

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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.

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Training Details

1. Weights initialized with a kaiming scheme.
2. Runs on a single consumer GPU.
3. Full smoke test takes minutes, not hours.
4. No distributed setup required.

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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
examples/sample_usage.py	usage sketch

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License

Released under bsd-2-clause. Review the source-data terms separately when this repository is used with external datasets.