Metadata-Version: 2.4
Name: lite-mamba
Version: 1.1.1
Summary: Pure-TensorFlow lightweight Mamba with multi-dilated causal conv front-end
Author-email: Md Robiuddin <mrrobi040@gmail.com>
License: Apache-2.0
Project-URL: Homepage, https://github.com/Mrrobi/lite_mamba
Project-URL: Documentation, https://mrrobi.github.io/lite_mamba/
Project-URL: Repository, https://github.com/Mrrobi/lite_mamba
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
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: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: tensorflow>=2.12

# lite-mamba

[![Publish Python Package](https://github.com/Mrrobi/lite_mamba/actions/workflows/publish.yml/badge.svg)](https://github.com/Mrrobi/lite_mamba/actions/workflows/publish.yml)
[![Tests](https://github.com/Mrrobi/lite_mamba/actions/workflows/tests.yml/badge.svg)](https://github.com/Mrrobi/lite_mamba/actions/workflows/tests.yml)
[![Documentation](https://img.shields.io/badge/docs-mrrobi.github.io-blue.svg)](https://mrrobi.github.io/lite_mamba/)

A minimal, pure-TensorFlow implementation of Mamba with a multi-dilated causal depthwise conv front-end. No custom C++ or Triton kernels needed; works seamlessly on CPU, GPU, or TPU with standard TensorFlow ops.

<br>

**📚 [READ THE FULL DOCUMENTATION HERE](https://mrrobi.github.io/lite_mamba/) 📚**

Contains architecture details, API references, streaming inference guides, and overviews of the multi-branch variants (`TFPTCNMamba`, `TFSTCNMamba`, `TFDPWCMamba`).

---

## Quick Start

### Install
```bash
pip install lite-mamba
```

### Basic Usage

```python
from lite_mamba import TFPTCNMamba
import tensorflow as tf

x = tf.random.normal((2, 128, 512))  # (batch, seq, d_model)
m = TFPTCNMamba(d_model=512, d_conv=3, conv_dilations=(1, 2, 4, 8))

y = m(x)
print(y.shape)  # (2, 128, 512)
```

## License
Apache-2.0
