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license: apache-2.0
tags:
- pytorch
- blip
- generation
---
# Blip for Generation
## Overview
Working implementation of **Blip** for **Generation** using a **small** configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.
## 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 | Blip |
| Scale | small |
| Attention | sparse |
| Fusion | gated fusion |
| Activation | swish |
| Normalization | groupnorm |
## Default experiment recipe
The included configuration uses **adam** 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 pipeline.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 **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.
## Files
- `pipeline.py` — primary artifact
- `README.md` — this documentation
- `config.json` — architecture configuration
- `training_args.json` — default experiment settings
- `model.safetensors` — initialization checkpoint
## License
Released under **apache-2.0**. Review the source-data terms separately when this repository is used with external datasets.
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