semvec/pypi-packages huggingface dataset:
oxs 0.1.0
OXS: Optimized X-Scale SOTA Engine
Requires Python: >=3.8

# OXS: Optimized X-Scale SOTA Engine

**OXS** is a high-performance Neural-Symbolic AI Operating System kernel designed for extreme efficiency on consumer and server-grade hardware. It features a custom CUDA backend (`maxwell_core`) optimized for Pascal, Volta, Ampere, and Hopper architectures.

## Features

*   **SOTA CUDA Kernels:** 
    *   Tiled FP32 execution for Pascal (GTX 1050 Ti+).
    *   Tensor Core (WMMA) support for newer GPUs.
    *   Fused Backward Pass (Weight Gradients calculated on GPU).
*   **Quantization:** Native support for 2-bit Packed Weights (Ternary {-1, 0, 1}).
*   **Pythonic API:** PyTorch-like interface (`oxs.Linear`, `oxs.SGD`).

## Installation

### Prerequisites
*   Python 3.8+
*   NVIDIA CUDA Toolkit 11.0+ (Tested on 12.5)
*   Visual Studio 2019/2022 (Windows) or GCC (Linux) with C++17 support.
*   CMake 3.18+

### Install from Source
```bash
git clone https://github.com/pantheon/oxs.git
cd oxs
pip install .
```

## Quick Start

```python
import oxs
import numpy as np

# 1. Define Model
layer = oxs.Linear(in_features=1024, out_features=10)
optimizer = oxs.SGD([layer], lr=0.1)

# 2. Dummy Data
x = np.random.randn(32, 1024).astype(np.float32)
y_target = np.random.randn(32, 10).astype(np.float32)

# 3. Training Loop
for epoch in range(50):
    # Forward (Quantized on the fly)
    y_pred = layer.forward(x)
    
    # Loss (MSE)
    diff = y_pred - y_target
    loss = np.mean(diff**2)
    
    # Backward
    grad_loss = 2.0 * diff / x.size
    layer.backward(grad_loss)
    
    # Update
    optimizer.step()
    
    print(f"Epoch {epoch}: Loss {loss:.4f}")
```

## License

MIT License. See `LICENSE` for details.