mmm-zeta 0.1.0

MMM - Pytorch

Multi Modal Mamba - [MMM]

Multi Modal Mamba (MMM) is an all-new AI model that integrates Vision Transformer (ViT) and Mamba, creating a high-performance multi-modal model. MMM is built on Zeta, a minimalist yet powerful AI framework, designed to streamline and enhance machine learning model management.

The capacity to process and interpret multiple data types concurrently is essential, the world isn't 1dimensional. MMM addresses this need by leveraging the capabilities of Vision Transformer and Mamba, enabling efficient handling of both text and image data. This makes MMM a versatile solution for a broad spectrum of AI tasks. MMM stands out for its significant speed and efficiency improvements over traditional transformer architectures, such as GPT-4 and LLAMA. This enhancement allows MMM to deliver high-quality results without sacrificing performance, making it an optimal choice for real-time data processing and complex AI algorithm execution. A key feature of MMM is its proficiency in processing extremely long sequences.

This capability is particularly beneficial for tasks that involve substantial data volumes or necessitate a comprehensive understanding of context, such as natural language processing or image recognition. With MMM, you're not just adopting a state-of-the-art AI model. You're integrating a fast, efficient, and robust tool that is equipped to meet the demands of contemporary AI tasks. Experience the power and versatility of Multi Modal Mamba - MMM now!

Install

pip3 install mmm-zeta

Usage

MultiModalMambaBlock

# Import the necessary libraries
import torch 
from torch import nn
from mm_mamba import MultiModalMambaBlock

# Create some random input tensors
x = torch.randn(1, 16, 64)  # Tensor with shape (batch_size, sequence_length, feature_dim)
y = torch.randn(1, 3, 64, 64)  # Tensor with shape (batch_size, num_channels, image_height, image_width)

# Create an instance of the MultiModalMambaBlock model
model = MultiModalMambaBlock(
    dim = 64,  # Dimension of the token embeddings
    depth = 5,  # Number of transformer layers
    dropout = 0.1,  # Dropout probability
    heads = 4,  # Number of attention heads
    d_state = 16,  # Dimension of the state embeddings
    image_size = 64,  # Size of the input image
    patch_size = 16,  # Size of each image patch
    encoder_dim = 64,  # Dimension of the encoder token embeddings
    encoder_depth = 5,  # Number of encoder transformer layers
    encoder_heads = 4,  # Number of encoder attention heads
    fusion_method="mlp",
)

# Pass the input tensors through the model
out = model(x, y)

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

MMM, Ready to Train Model

• Flexibility in Data Types: The MMM model can handle both text and image data simultaneously. This allows it to be trained on a wider variety of datasets and tasks, including those that require understanding of both text and image data.

• Customizable Architecture: The MMM model has numerous parameters such as depth, dropout, heads, d_state, image_size, patch_size, encoder_dim, encoder_depth, encoder_heads, and fusion_method. These parameters can be tuned according to the specific requirements of the task at hand, allowing for a high degree of customization in the model architecture.

• Option to Return Embeddings: The MMM model has a return_embeddings option. When set to True, the model will return the embeddings instead of the final output. This can be useful for tasks that require access to the intermediate representations learned by the model, such as transfer learning or feature extraction tasks.

import torch  # Import the torch library

# Import the MMM model from the mm_mamba module
from mm_mamba import MMM

# Generate a random tensor 'x' of size (1, 224) with random elements between 0 and 10000
x = torch.randint(0, 10000, (1, 196))

# Generate a random image tensor 'img' of size (1, 3, 224, 224)
img = torch.randn(1, 3, 224, 224)

# Create a MMM model object with the following parameters:
model = MMM(
    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,
)

# Pass the tensor 'x' and 'img' through the model and store the output in 'out'
out = model(x, img)

# Print the shape of the output tensor 'out'
print(out.shape)

Real-World Deployment

Are you an enterprise looking to leverage the power of AI? Do you want to integrate state-of-the-art models into your workflow? Look no further!

Multi Modal Mamba (MMM) is a cutting-edge AI model that fuses Vision Transformer (ViT) with Mamba, providing a fast, agile, and high-performance solution for your multi-modal needs.

But that's not all! With Zeta, our simple yet powerful AI framework, you can easily customize and fine-tune MMM to perfectly fit your unique quality standards.

Whether you're dealing with text, images, or both, MMM has got you covered. With its deep configuration and multiple fusion layers, you can handle complex AI tasks with ease and efficiency.

:star2: Why Choose Multi Modal Mamba?

• Versatile: Handle both text and image data with a single model.
• Powerful: Leverage the power of Vision Transformer and Mamba.
• Customizable: Fine-tune the model to your specific needs with Zeta.
• Efficient: Achieve high performance without compromising on speed.

Don't let the complexities of AI slow you down. Choose Multi Modal Mamba and stay ahead of the curve!

Contact us here today to learn how you can integrate Multi Modal Mamba into your workflow and supercharge your AI capabilities!

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License

MIT