Flamingo for Retrieval
Overview
This repository is a compact, custom PyTorch implementation of Flamingo for Retrieval. The base configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release.
Comments and corrections can be left in the discussion tab.
Default experiment recipe
The included configuration uses novograd with a step 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.
Evaluation guidance
A useful first evaluation would use Flickr30k, 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 checkpoint is untrained and unaudited. Treat the repository as an experimental starting point; anything learned from a future trained version must be documented separately.
Repository status
- Single-file implementation; the
__main__block runs a smoke test. config.jsonandtraining_args.jsonhold the generated settings.model.safetensorsis freshly initialized — plumbing, not results.- No benchmark numbers are reported on purpose.
Files
| File | Purpose |
|---|---|
inference.py |
primary artifact |
README.md |
this documentation |
config.json |
architecture configuration |
training_args.json |
default experiment settings |
model.safetensors |
initialization checkpoint |
NOTES.md |
informal to-do notes |
examples/sample_usage.py |
usage sketch |
License
Distributed under the apache-2.0 license. External datasets keep their own terms — check them separately.
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