brittanygom/intern-generation82

📊 Working Poolformer implementation for Generation

Overview

This is an experimental Poolformer codebase for Generation. It keeps the tiny setup intentionally manageable so architecture changes can be inspected before a full training run.

The whole setup fits in a single module on purpose.

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.

Default experiment recipe

The included configuration uses lamb with a polynomial 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.

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.

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.

Acknowledgments

This repo borrows ideas freely from public reference implementations; credit to their authors.

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
examples/sample_usage.py	usage sketch

License

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