Skip to main content

BitMoE - Pytorch

Project description

Multi-Modality

BitMoE

1 bit Mixture of Experts utilizing BitNet ++ Mixture of Experts. Also will add distribution amongst GPUs.

install

$ pip3 install bitmoe

usage

import torch
from bitmoe.main import BitMoE

# Set the parameters
dim = 10  # Dimension of the input
hidden_dim = 20  # Dimension of the hidden layer
output_dim = 30  # Dimension of the output
num_experts = 5  # Number of experts in the BitMoE model

# Create the model
model = BitMoE(dim, hidden_dim, output_dim, num_experts)

# Create random inputs
batch_size = 32  # Number of samples in a batch
sequence_length = 100  # Length of the input sequence
x = torch.randn(batch_size, sequence_length, dim)  # Random input tensor

# Forward pass
output = model(x)  # Perform forward pass using the model

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

License

MIT

Todo

  • Implement better gating mechanisms
  • Implement better routing algorithm
  • Implement better BitFeedForward
  • Implement

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bitmoe-0.0.2.tar.gz (4.3 kB view hashes)

Uploaded Source

Built Distribution

bitmoe-0.0.2-py3-none-any.whl (4.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page