Grok-1
This repository contains JAX example code for loading and running the Grok-1 open-weights model.
Make sure to download the checkpoint and place the ckpt-0 directory in checkpoints - see Downloading the weights
Then, run
pip install -r requirements.txt
python run.py
to test the code.
The script loads the checkpoint and samples from the model on a test input.
Due to the large size of the model (314B parameters), a machine with enough GPU memory is required to test the model with the example code. The implementation of the MoE layer in this repository is not efficient. The implementation was chosen to avoid the need for custom kernels to validate the correctness of the model.
Model Specifications
Grok-1 is currently designed with the following specifications:
Downloading the weights
You can download the weights using a torrent client and this magnet link:
magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounceor directly using HuggingFace 🤗 Hub:
git clone https://github.com/xai-org/grok-1.git && cd grok-1
pip install huggingface_hub[hf_transfer] huggingface-cli download xai-org/grok-1 --repo-type model --include ckpt-0/* --local-dir checkpoints --local-dir-use-symlinks False
License
The code and associated Grok-1 weights in this release are licensed under the Apache 2.0 license. The license only applies to the source files in this repository and the model weights of Grok-1.
This repository contains the open-source code and model weights for Grok-1, a massive 314 billion parameter language model developed by xAI. It's a Mixture of Experts (MoE) model released under the Apache 2.0 license, providing researchers and developers with example JAX code to load and run this state-of-the-art AI.