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README.md

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+---
+license: mit
+tags:
+- pytorch
+- flamingo
+- generation
+---
+
+# Flamingo for Generation
+
+## Overview
+
+This repository is a compact, custom PyTorch implementation of **Flamingo** for **Generation**. The **tiny** configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 | Flamingo |
+| Scale | tiny |
+| Attention | grouped query |
+| Fusion | gated fusion |
+| Activation | gelu tanh |
+| Normalization | scalenorm |
+
+## Default experiment recipe
+
+The included configuration uses **adamw** with a **constant warmup** 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 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.
+
+## 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
+
+- `main.py` — primary artifact
+- `README.md` — this documentation
+- `config.json` — architecture configuration
+- `training_args.json` — default experiment settings
+- `model.safetensors` — initialization checkpoint
+
+## License
+
+Released under **mit**. Review the source-data terms separately when this repository is used with external datasets.