This is an unofficial implementation for the paper Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

pip install mixture-of-depth

Both Linux, Windows and MacOS are supported.

from transformers import AutoModelForCausalLM
from MoD import apply_mod_to_hf

# Initialize your model from an available hf model
model= AutoModelForCausalLM.from_pretrained("some-repo/some-model")
# Convert the model to include the mixture of depths layers
model = apply_mod_to_hf(model)
# train the model
# ...
# save the model
model.save_pretrained('some_local_directory')

To utilize the converted model, you will need to load the model from the AutoClass. Below is an example demonstrating how to load the model from a local directory:

from MoD import AutoMoDModelForCausalLM

# Replace 'path_to_your_model' with the actual path to your model's directory
model = AutoMoDModelForCausalLM.from_pretrained('path_to_your_model')

Before calling the hf generate() method please explicitly use eval() on the model

We welcome contributions from the community, whether it's adding new features, improving documentation, or reporting bugs. Please refer to our contribution guidelines before making a pull request.

This repo is open-sourced under the Apache-2.0 license.

If you use our code in your research, please cite it using the following Bibtex entry:

@article{MoD2024,
  title={Unofficial implementation for the paper "Mixture-of-Depths"},
  author={AstraMind AI},
  journal={https://github.com/astramind-ai/Mixture-of-depths},
  year={2024}
}

For questions, issues, or support, please open an issue on our GitHub repository.