---
license: cc-by-4.0
tags:
- pytorch
- mocov3
- classification
---

# Mocov3 experiments: Classification

## Overview

This repository is a compact, custom PyTorch implementation of **Mocov3** for **Classification**. The **huge** configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release.

The whole setup fits in a single module on purpose.

## Architecture

| Item | Value |
|---|---|
| Architecture | Mocov3 |
| Scale | huge |
| Attention | sparse |
| Fusion | concat mlp |
| Activation | relu |
| Normalization | rmsnorm |

## Repository status

One Python module contains the model and a runnable example.
Generated configuration lives in `config.json`; the recipe in `training_args.json`.
The checkpoint is an initialization artifact and should not be read as a trained model.
Scores are deliberately absent.

## Contact

Questions or corrections: open a discussion in this repository.

## 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.

## Roadmap

Next steps, in no particular order: a cleaner entry point, input-shape docs, and a small evaluation script if time permits.

## Default experiment recipe

Defaults: **adafactor** optimizer, **exponential** schedule. Treat these as starting points; a real run should sweep learning rate and batch size before drawing conclusions.

## 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 **cc-by-4.0** license. External datasets keep their own terms — check them separately.