Metadata-Version: 2.1
Name: simple-mamba
Version: 0.0.3
Summary: Simple Mambda - Pytorch
Home-page: https://github.com/kyegomez/SimpleMamba 
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: torch
Requires-Dist: torchvision
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/SimpleMamba    
Project-URL: Repository, https://github.com/kyegomez/SimpleMamba   
Description-Content-Type: text/markdown

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

# Simple Mamba

## Install
`pip install simple-mamba`


## Usage
```python
import torch
from simple_mamba import MambaBlock


# Define block parameters
dim = 512
hidden_dim = 128
heads = 8
in_channels = 3
out_channels = 3
kernel_size = 3

# Create an instance of MambaBlock
mamba_block = MambaBlock(
    dim, hidden_dim, heads, in_channels, out_channels, kernel_size
)

# Create a sample input tensor
x = torch.randn(1, dim, dim)

# Pass the tensor through the MambaBlock
output = mamba_block(x)
print("Output shape:", output.shape)


```


# License
MIT


# Citation
```bibtex
@misc{gu2023mamba,
    title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces}, 
    author={Albert Gu and Tri Dao},
    year={2023},
    eprint={2312.00752},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

```
