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.
Grok-1 is currently designed with the following specifications:
Parameters: 314B
Architecture: Mixture of 8 Experts (MoE)
Experts Utilization: 2 experts used per token
Layers: 64
Attention Heads: 48 for queries, 8 for keys/values
Embedding Size: 6,144
Tokenization: SentencePiece tokenizer with 131,072 tokens
Additional Features:
Rotary embeddings (RoPE)
Supports activation sharding and 8-bit quantization
Maximum Sequence Length (context): 8,192 tokens
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%2Fannounce
or 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
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.
