Metadata-Version: 2.1
Name: moe-mamba
Version: 0.0.1
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/MoE-Mamba
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.6,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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 :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: swarms
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/MoE-Mamba
Project-URL: Repository, https://github.com/kyegomez/MoE-Mamba
Description-Content-Type: text/markdown

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# MoE Mamba
Implementation of MoE Mamba from the paper: "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" in Pytorch and Zeta. 

[PAPER LINK](https://arxiv.org/abs/2401.04081)


## Install

```bash
pip install moe-mamba
```

# Usage
```python
print("hello world")

```



## Code Quality 🧹

- `make style` to format the code
- `make check_code_quality` to check code quality (PEP8 basically)
- `black .`
- `ruff . --fix`


## Citation
```bibtex
@misc{pióro2024moemamba,
    title={MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts}, 
    author={Maciej Pióro and Kamil Ciebiera and Krystian Król and Jan Ludziejewski and Sebastian Jaszczur},
    year={2024},
    eprint={2401.04081},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

```


# License
MIT

