Dino for Multitask
Overview
Working implementation of Dino for Multitask using a tiny configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.
Acknowledgments
This repo borrows ideas freely from public reference implementations; credit to their authors.
Repository Status
- Single-file implementation; the
__main__block runs a smoke test. config.jsonandtraining_args.jsonhold the generated settings.model.safetensorsis freshly initialized โ 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
Environment
| Item | Value |
|---|---|
| Python | >= 3.9 |
| torch | 2.x |
| safetensors | any recent |
| transformers | optional, for loaders |
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.
Quick Check
Run python train.py --help for the built-in smoke test. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.
Files
| File | Purpose |
|---|---|
train.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 apache-2.0 license. External datasets keep their own terms โ check them separately.
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