Metadata-Version: 2.1
Name: simba-torch
Version: 0.0.5
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/Simba
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: einops
Requires-Dist: torch
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/Simba
Project-URL: Repository, https://github.com/kyegomez/Simba
Description-Content-Type: text/markdown


# Simba
![graph](graph.png)
A simpler Pytorch + Zeta Implementation of the paper: "SiMBA: Simplified Mamba-based Architecture for Vision and Multivariate Time series"


## install
`$ pip install simba-torch`

## usage
```python

import torch 
from simba_torch.main import Simba

# Forward pass with images
img = torch.randn(1, 3, 224, 224)

# Create model
model = Simba(
    dim = 4,                # Dimension of the transformer
    dropout = 0.1,          # Dropout rate for regularization
    d_state=64,             # Dimension of the transformer state
    d_conv=64,              # Dimension of the convolutional layers
    num_classes=64,         # Number of output classes
    depth=8,                # Number of transformer layers
    patch_size=16,          # Size of the image patches
    image_size=224,         # Size of the input image
    channels=3,             # Number of input channels
    # use_pos_emb=True # If you want
)

# Forward pass
out = model(img)
print(out.shape)

```


# License
MIT

# Todo
- [ ] Add paper link
- [ ] Add citation bibtex
- [ ] cleanup
