vishwamai 0.1.0

A math-focused machine learning library with efficient quantization and advanced tokenization

VishwamAI

VishwamAI is a sophisticated machine learning library focusing on efficient model quantization, advanced tokenization, and mathematical reasoning capabilities.

Features

• Advanced Tokenization: Conceptual tokenizer with semantic clustering and special token handling
• Efficient Quantization: Support for FP8 and BF16 quantization
• Mathematical Reasoning: Integration with GSM8K dataset for advanced mathematical problem-solving
• Model Architecture: Flexible transformer-based architecture with configurable parameters
• Training Utilities: Support for distributed training, mixed precision, and gradient accumulation

Installation

pip install -e .

Quick Start

from vishwamai.model import VishwamaiModel
from vishwamai.conceptual_tokenizer import ConceptualTokenizer

# Initialize tokenizer and model
tokenizer = ConceptualTokenizer()
model = VishwamaiModel()

# Example usage
text = "Solve: If John has 5 apples and gives 2 to Mary, how many does he have left?"
tokens = tokenizer.encode(text)
output = model.generate(tokens)

Testing

Run the test suite:

pytest -v

Requirements

• Python >= 3.8
• PyTorch >= 2.1.0
• CUDA toolkit (for GPU support)
• Additional dependencies listed in setup.py

Project Structure

vishwamai/
├── conceptual_tokenizer.py   # Advanced tokenization implementation
├── kernel.py                 # CUDA kernels and quantization
├── model.py                 # Core model architecture
├── training.py              # Training utilities
└── configs/                 # Model configurations

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting pull requests.