Metadata-Version: 2.4
Name: pranjit-transformer
Version: 0.1.0
Summary: CPU-First Premium Transformer Library for low-resource devices
Author-email: pranjit bordoloi <pranjit1st@gmail.com>
Maintainer-email: pranjit bordoloi <pranjit1st@gmail.com>
License: MIT
Keywords: transformer,deep-learning,nlp,pytorch,cpu-optimized,low-resource,ai,machine-learning
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: black>=23.0; extra == "dev"
Requires-Dist: flake8>=6.0; extra == "dev"
Requires-Dist: mypy>=1.0; extra == "dev"
Dynamic: license-file

# 🚀 Pranjit Transformer

[![PyPI version](https://badge.fury.io/py/pranjit-transformer.svg)](https://badge.fury.io/py/pranjit-transformer)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A **CPU-first**, lightweight transformer library optimized for training and inference on low-resource devices. Built with PyTorch, featuring modern architecture components like RoPE embeddings, RMS normalization, and gated feed-forward networks.

## ✨ Features

- 🖥️ **CPU-Optimized**: Designed for efficient training on standard hardware
- 🔧 **User-Controlled**: Full control over model configuration and hyperparameters
- 🧩 **Hybrid Tokenizer**: Word-level and character-level tokenization support
- ⚡ **Modern Architecture**: RoPE embeddings, RMS normalization, gated FFN
- 📦 **Clean API**: Simple and intuitive interface for quick experimentation
- 💾 **Model I/O**: Easy save/load functionality for checkpoints

## 📦 Installation

