Working ViT implementation for Retrieval

Overview

A research-oriented ViT prototype targeting Retrieval. The included large setup documents defaults and file formats without presenting unverified performance numbers.

Training Details

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

Quick Check

python finetune.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

• Single-file implementation; the __main__ block runs a smoke test.
• config.json and training_args.json hold the generated settings.
• model.safetensors is freshly initialized — plumbing, not results.
• No benchmark numbers are reported on purpose.

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

Default Experiment Recipe

The generated recipe uses adamw with a exponential schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

Files

File	Purpose
finetune.py	primary artifact
README.md	this documentation
config.json	architecture configuration
training_args.json	default experiment settings
model.safetensors	initialization checkpoint
USE_POLICY.md	use and redistribution notes

License

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