Package: mmm-zeta
Multi Modal Mamba - [MultiModalMamba]Multi Modal Mamba (MultiModalMamba) is an all-new AI model that integrates Vision Transformer (ViT) and Mamba, creating a high-performance multi-modal model. MultiModalMamba 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. MultiModalMamba addresses this need by leveraging the capabilities of Vision Transformer and Mamba, enabling efficient handling of both text and image data. This makes MultiModalMamba a versatile solution for a broad spectrum of AI tasks. MultiModalMamba stands out for its significant speed and efficiency improvements over traditional transformer architectures, such as GPT-4 and LLAMA. This enhancement allows MultiModalMamba 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 MultiModalMamba 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 MultiModalMamba, 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 - MultiModalMamba now!Installpip3 install mmm-zetaUsageMultiModalMambaBlock# Import the necessary librariesimporttorch# Import the torch library# Import the MultiModalMamba model from the mm_mamba modulefrommm_mambaimportMultiModalMamba# Generate a random tensor 'x' of size (1, 224) with random elements between 0 and 10000x=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)# Audio tensor 'aud' of size 2daud=torch.randn(1,224)# Video tensor 'vid' of size 5d - (batch_size, channels, frames, height, width)vid=torch.randn(1,3,16,224,224)# Create a MultiModalMamba model object with the following parameters: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,)# Pass the tensor 'x' and 'img' through the model and store the output in 'out'out=model(x,img,aud,vid)# Print the shape of the output tensor 'out'print(out.shape)# After much trainingmodel.eval()# Generate textmodel.generate(text)MultiModalMamba, Ready to Train ModelFlexibility in Data Types: The MultiModalMamba 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 MultiModalMamba 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 MultiModalMamba 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.importtorch# Import the torch library# Import the MultiModalMamba model from the mm_mamba modulefrommm_mambaimportMultiModalMamba# Generate a random tensor 'x' of size (1, 224) with random elements between 0 and 10000x=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 MultiModalMamba model object with the following parameters: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,)# 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)# After much trainingmodel.eval()# Tokenize textstext_tokens=tokenize(text)# Send text tokens to the modellogits=model(text_tokens)text=detokenize(logits)Real-World DeploymentAre 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 (MultiModalMamba) 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 MultiModalMamba to perfectly fit your unique quality standards.Whether you're dealing with text, images, or both, MultiModalMamba 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 heretoday to learn how you can integrate Multi Modal Mamba into your workflow and supercharge your AI capabilities!LicenseMIT