sandeepaga/course-contrastive

Working ViT implementation for Contrastive

Overview

This repository is a compact, custom PyTorch implementation of ViT for Contrastive. The small configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release.

---

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

---

Roadmap

• tighten the smoke-test entry point
• document expected input shapes
• add a small evaluation script

---

Architecture

• Architecture: ViT
• Scale: small
• Attention: standard
• Fusion: concat mlp
• Activation: mish
• Normalization: layernorm

---

Default Experiment Recipe

The generated recipe uses lion with a step schedule.

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

---

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.

---

Evaluation Guidance

A first evaluation could use a task-specific held-out set. Report the metric across at least three seeds and include a matched-capacity baseline; keep logs and library versions with any published number.

---

Files

File	Purpose
pipeline.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

Distributed under the bsd-2-clause license. External datasets keep their own terms — check them separately.