| ---
|
| license: bsd-3-clause
|
| tags:
|
| - pytorch
|
| - hybrid
|
| - retrieval
|
| ---
|
|
|
| # Hybrid for Retrieval
|
|
|
| ## Overview
|
|
|
| A small **Hybrid** implementation for **Retrieval**, packaged with an explicit configuration and an initialization checkpoint. The **small** variant is a reproducible starting point, not a trained model release.
|
|
|
| ## Repository status
|
|
|
| - The Python file contains the model and runnable example or training entry point.
|
| - `config.json` records the generated architecture settings.
|
| - `training_args.json` records the default experiment recipe.
|
| - `model.safetensors` is a valid initialization checkpoint for smoke tests; it is **not** presented as a trained benchmark checkpoint.
|
| - No benchmark score is claimed in this repository.
|
|
|
| ## Architecture
|
|
|
| | Item | Value |
|
| |---|---|
|
| | Architecture | Hybrid |
|
| | Scale | small |
|
| | Attention | multi query |
|
| | Fusion | bilinear |
|
| | Activation | approx gelu |
|
| | Normalization | batchnorm |
|
|
|
| ## Default experiment recipe
|
|
|
| The included configuration uses **rmsprop** with a **polynomial** 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.
|
|
|
| ## Quick check
|
|
|
| ```bash
|
| python predict.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.
|
|
|
| ## 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 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.
|
|
|
| ## Files
|
|
|
| - `predict.py` — primary artifact
|
| - `README.md` — this documentation
|
| - `config.json` — architecture configuration
|
| - `training_args.json` — default experiment settings
|
| - `model.safetensors` — initialization checkpoint
|
|
|
| ## License
|
|
|
| Released under **bsd-3-clause**. Review the source-data terms separately when this repository is used with external datasets.
|
|
|
