https://github.com/kyegomez/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. "
https://github.com/kyegomez/morpheus-1
ai artificial-intelligence machine-learning medical ml neural-networks
Last synced: 6 months ago
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Implementation of "MORPHEUS-1" from Prophetic AI and "The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams. "
- Host: GitHub
- URL: https://github.com/kyegomez/morpheus-1
- Owner: kyegomez
- License: mit
- Created: 2024-01-25T15:31:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-27T06:20:24.000Z (9 months ago)
- Last Synced: 2025-03-28T07:09:33.492Z (7 months ago)
- Topics: ai, artificial-intelligence, machine-learning, medical, ml, neural-networks
- Language: Python
- Homepage: https://discord.gg/GYbXvDGevY
- Size: 2.26 MB
- Stars: 130
- Watchers: 6
- Forks: 18
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
[](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