Metadata-Version: 2.1
Name: hsss
Version: 0.0.2
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/HSSS
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/HSSS
Project-URL: Repository, https://github.com/kyegomez/HSSS
Description-Content-Type: text/markdown

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

# HSSS
Implementation of a Hierarchical Mamba as described in the paper: "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling".


## install
`pip install hsss`

##  usage
```python
import torch 
from hsss.model import HSSSMamba

x = torch.randn(1, 10, 8)

model = HSSSMamba(
    dim_in = 8,
    depth_in = 6,
    dt_rank_in = 4,
    d_state_in = 4,
    expand_factor_in = 4,
    d_conv_in = 6,
    dt_min_in = 0.001,
    dt_max_in = 0.1,
    dt_init_in = "random",
    dt_scale_in = 1.0,
    bias_in = False,
    conv_bias_in = True,
    pscan_in = True,
    dim = 4,
    depth = 3,
    dt_rank = 2,
    d_state = 2,
    expand_factor = 2,
    d_conv = 3,
    dt_min = 0.001,
    dt_max = 0.1,
    dt_init = "random",
    dt_scale = 1.0,
    bias = False,
    conv_bias = True,
    pscan = True,
)


out = model(x)
print(out)
```

# License
MIT

