oliviadiaz9/multitask-2023

📘 CLIP for Multitask

Overview

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

Quick check

python main.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.

Default experiment recipe

The generated recipe uses {} with a {} 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 useful first evaluation would use a task-specific held-out set, report the task metric across at least three seeds, and include a matched-capacity baseline. Keep training logs and environment versions with any published result.

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.

Training details

• Framework: PyTorch
• Precision: fp32
• Init scheme: trunc normal
• Hardware: a single consumer GPU is enough
• Typical run: minutes, not hours

Files

• main.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 cc-by-4.0 license. External datasets keep their own terms — check them separately.