cross-attn

Name	cross-attn JSON
Version	0.0.4 JSON
	download
home_page	https://github.com/kyegomez/MultiModalCrossAttn
Summary	MultiModalCrossAttn - Pytorch
upload_time	2023-10-03 06:03:37
maintainer
docs_url	None
author	Kye Gomez
requires_python	>=3.6,<4.0
license	MIT
keywords	artificial intelligence deep learning optimizers prompt engineering
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

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

# MultiModalCrossAttn
The open source implementation of the cross attention mechanism from the paper: "JOINTLY TRAINING LARGE AUTOREGRESSIVE MULTIMODAL MODELS"


[Paper Link](https://arxiv.org/pdf/2309.15564.pdf)

# Appreciation
* Lucidrains
* Agorians



# Install
`pip install cross-attn`

# Usage
```python
import torch
from cross_attn.main import MultiModalCrossAttention

# Test the MultiModalCrossAttention module
dim = 512  # For example
num_heads = 8

cross_attn = MultiModalCrossAttention(dim, num_heads)

Hllm_sample = torch.randn(32, dim, dim)  # Batch size = 32, Sequence length = 10
Himg_sample = torch.randn(32, dim, dim)

output = cross_attn(Hllm_sample, Himg_sample)
print(output)

print(output.shape)  # Expected: [32, 10, 512]

```



# License
MIT

# Citations
```
@misc{2309.15564,
Author = {Emanuele Aiello and Lili Yu and Yixin Nie and Armen Aghajanyan and Barlas Oguz},
Title = {Jointly Training Large Autoregressive Multimodal Models},
Year = {2023},
Eprint = {arXiv:2309.15564},
}
```
            

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