Metadata-Version: 2.1
Name: vortex-fusion
Version: 0.0.2
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/VortexFusion
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.10,<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 :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
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/VortexFusion
Project-URL: Repository, https://github.com/kyegomez/VortexFusion
Description-Content-Type: text/markdown

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

# Vortex Fusion
This is the first ever implementation of a joint Transformer + Mamba + LSTM architecture. The flow is the following: `mamba -> transformer -> lstm` in a loop. Perhaps with more iteration on model design, we can find a better architecture but this architecture is the future.


## install

```bash
$ pip3 install -U vortex-fusion

```

## Usage
```python
import torch
from vortex_fusion import VortexFusion

# Generate random input tensor
x = torch.randint(0, 10000, (1, 10))

# Create an instance of the VortexFusion model with dimension 512
model = VortexFusion(dim=512)

# Pass the input tensor through the model to get the output
output = model(x)

# Print the shape of the output tensor
print(output.shape)
```

# License
MIT


# Citation
Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you.

```bibtex
@misc{swarms,
  author = {Gomez, Kye},
  title = {{Swarms: The Multi-Agent Collaboration Framework}},
  howpublished = {\url{https://github.com/kyegomez/swarms}},
  year = {2023},
  note = {Accessed: Date}
}
```


