base on Implementation of "MORPHEUS-1" from Prophetic AI and "The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams. " [](https://discord.gg/qUtxnK2NMf) # Morpheus 1  Implementation of "MORPHEUS-1" from Prophetic AI and "The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams. " ## Installation ```bash pip install morpheus-torch ``` # Usage - The input is FRMI and EEG tensors. - FRMI shape is (batch_size, in_channels, D, H, W) - EEG Embedding is [batch_size, channels, time_samples] ```python # Importing the torch library import torch # Importing the Morpheus model from the morpheus_torch package from morpheus_torch.model import Morpheus # Creating an instance of the Morpheus model with specified parameters model = Morpheus( dim=128, # Dimension of the model heads=4, # Number of attention heads depth=2, # Number of transformer layers dim_head=32, # Dimension of each attention head dropout=0.1, # Dropout rate num_channels=32, # Number of input channels conv_channels=32, # Number of channels in convolutional layers kernel_size=3, # Kernel size for convolutional layers in_channels=1, # Number of input channels for convolutional layers out_channels=32, # Number of output channels for convolutional layers stride=1, # Stride for convolutional layers padding=1, # Padding for convolutional layers ff_mult=4, # Multiplier for feed-forward layer dimension scatter = False, # Whether to scatter to 4d representing spatial dimensions ) # Creating random tensors for input data frmi = torch.randn(1, 1, 32, 32, 32) # Random tensor for FRMI data eeg = torch.randn(1, 32, 128) # Random tensor for EEG data # Passing the input data through the model to get the output output = model(frmi, eeg) # Printing the shape of the output tensor print(output.shape) ``` ### Code Quality 🧹 - `make style` to format the code - `make check_code_quality` to check code quality (PEP8 basically) - `black .` - `ruff . --fix` # License MIT # Todo - [x] Implement the scatter in the end of the decoder to output spatial outputs which are 4d? - [x] Implement a full model with the depth of the decoder layers - [ ] Change all the MHAs to Multi Query Attentions - [ ] Double check popular brain scan EEG and FRMI AI papers to double check tensor shape ", Assign "at most 3 tags" to the expected json: {"id":"7379","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"