Introduction to Multi Modal Mamba (MMM)
Multi Modal Mamba, abbreviated as MMM, represents a significant advancement in the realm of artificial intelligence models. It combines the strengths of the Vision Transformer (ViT) and Mamba to create a robust multi-modal model capable of processing and interpreting various data types. This innovative model is built on the Zeta framework, which is known for its minimalist yet potent approach to managing machine learning models. Such capabilities ensure that MultiModalMamba is a versatile and efficient solution for a wide range of AI applications.
Installation
To get started with Multi Modal Mamba, you can easily install it through the following command:
pip3 install mmm-zeta
How It Works
MultiModalMambaBlock
The MultiModalMambaBlock is the core component that enables the model to process both text and image data concurrently. Here's a simple example to demonstrate its usage:
import torch
from mm_mamba import MultiModalMambaBlock
# Initialize random input data
x = torch.randn(1, 16, 64) # Text data tensor
y = torch.randn(1, 3, 64, 64) # Image data tensor
# Create the model instance
model = MultiModalMambaBlock(
dim=64,
depth=5,
dropout=0.1,
heads=4,
d_state=16,
image_size=64,
patch_size=16,
encoder_dim=64,
encoder_depth=5,
encoder_heads=4,
fusion_method="mlp",
)
# Process the data
out = model(x, y)
print(out.shape) # Output the result
Ready-to-Train Model
The MultiModalMamba model extends functionalities, ready for training on datasets containing both text and image data. It is highly customizable with numerous parameters that can be adjusted to meet specific project requirements:
import torch
from mm_mamba import MultiModalMamba
# Initialize random input data
x = torch.randint(0, 10000, (1, 196))
img = torch.randn(1, 3, 224, 224)
aud = torch.randn(1, 224)
vid = torch.randn(1, 3, 16, 224, 224)
# Model configuration
model = MultiModalMamba(
vocab_size=10000,
dim=512,
depth=6,
dropout=0.1,
heads=8,
d_state=512,
image_size=224,
patch_size=16,
encoder_dim=512,
encoder_depth=6,
encoder_heads=8,
fusion_method="mlp",
return_embeddings=False,
post_fuse_norm=True,
)
# Process the data
out = model(x, img, aud, vid)
print(out.shape) # Output the result
Real-World Application
For enterprises aiming to leverage AI technologies, Multi Modal Mamba offers a state-of-the-art solution designed for multi-modal tasks. Built with the Vision Transformer and Mamba at its core, this model is both fast and powerful, catering perfectly to enterprise needs where efficiency and performance are paramount.
With its customization capabilities via the Zeta framework, MultiModalMamba can be fine-tuned to meet specific quality standards, making it suitable for a variety of applications involving text, images, or a combination of data types.
Why Choose Multi Modal Mamba?
- Versatile: Efficiently handles diverse data types from text to images.
- Robust: Utilizes the advanced capabilities of the Vision Transformer and Mamba.
- Customizable: Adaptable to specific requirements thanks to the Zeta framework.
- Efficient: Strikes a balance between high performance and speed.
Embark on the journey of integrating AI into your workflow without hassle. Opt for Multi Modal Mamba to stay ahead and optimize your AI capabilities.
For more information and to explore the integration possibilities, feel free to contact us.
License
The project is licensed under the MIT License, ensuring freedom and flexibility in its use.