Dismantling large language models parts to understand them better, with the hope of building better models.
You can change the paths you want the codebase to operate with by modifying the user_config.ini file
git clone [email protected]:facebookresearch/pal.git
cd pal
pip install -e .This will install the codebase in your current Python environment.
If you want to install it in a special environment, you can create a new one with conda or virtualenv.
Please refer to the example folder, and in particular the bash_script.sh to get a sense of how to run experiments.
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Vivien Cabannes, Charles Arnal, Wassim Bouaziz, Alice Yang, Francois Charton, Julia Kempe. Iteration Head: A Mechanistic Study of Chain-of-Thought, in NeurIPS 2024. The codebase is in the folder
projects/cot. -
Vivien Cabannes, Elvis Dohmatob, Alberto Bietti. Scaling Laws for Associative Memories, in International Conference on Learning Representations (ICLR), 2024. The codebase is in the folder
projects/scaling_laws. -
Vivien Cabannes, Berfin Simsek, Alberto Bietti. Learning Associative Memories with Gradient Descent in International Conference on Machine Learning (ICML), 2024. The codebase is in the folder
projects/gradient_descent. -
Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes. Easing Optimization Paths: A Circuit Perspective, in ICASSP 2025. The codebase is in
project/visualization. -
Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes. A Visual Case Study of the Training Dynamics in Neural Networks, In preparation. The codebase is in
project/visualization. -
Charles Arnal, Clement Berenfeld, Simon Rosenberg, Vivien Cabannes. Scaling Laws with Hidden Structure, In preparation. The codebase is in
projects/factorization.
The main reusable code is in the src folder.
The code for our different research streams is in the projects folder.
Other folders may include:
data: contains data used in the experiments.models: saves models' weights.launchers: contains bash scripts to launch experiments.notebooks: used for exploration and visualization.scripts: contains Python scripts to run experiments.tests: contains tests for the code.tutorial: contains tutorial notebooks to get started with LLMs' training.