Metadata-Version: 2.1
Name: mmca
Version: 0.0.2
Summary: MMCA - Pytorch
Home-page: https://github.com/kyegomez/MMCA
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 :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: torch
Project-URL: Repository, https://github.com/kyegomez/MMCA
Description-Content-Type: text/markdown

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

# Multi-Modal Causal Attention
The open source community's implementation of the all-new Multi-Modal Causal Attention from "DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention"


[Paper Link](https://arxiv.org/pdf/2309.14327.pdf)

# Appreciation
* Lucidrains
* Agorians



# Install
`pip install mmca`

# Usage
```python
import torch 
from mmca.main import MultiModalCausalAttention


attn = MultiModalCausalAttention(dim=512, heads=8)

x = torch.randn(1, 10, 512)
y = torch.randn(1, 20, 512)

#create a mask for the text
# mask = torch.ones(1, 20).bool()

x, y = attn(x, y)

print(x)
# print(y)
```

# Architecture

# Todo


# License
MIT

# Citations


