Grok-1

This repository contains JAX example code for loading and running the Grok-1 open-weights model, developed by xAI, founded by Elon Musk. Grok-1 is designed to tackle a variety of natural language processing tasks effectively. This document will guide you through the setup, usage, and specifications of the model.

Table of Contents

• Overview
• Getting Started
• Model Specifications
• Downloading Weights
• Usage
• License

Overview

Grok-1 is an advanced AI model characterized by its large parameter count and a unique architectural approach utilizing a Mixture of Experts (MoE) framework. This model not only serves as a powerful tool for NLP applications but also provides an exciting opportunity for developers and researchers to explore cutting-edge AI technologies.

Getting Started

To set up and run Grok-1, follow these steps:

1. Clone the repository:

   git clone https://github.com/xai-org/grok-1.git
   cd grok-1
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Download the model weights:
   Ensure that you download the checkpoint and place the ckpt-0 directory in checkpoints (see Downloading Weights).

Model Specifications

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
• Maximum Sequence Length (context): 8,192 tokens
• Additional Features:
  • Rotary embeddings (RoPE)
  • Supports activation sharding and 8-bit quantization

Downloading Weights

You can download the weights using two methods:

1. Using a Torrent Client:
   Download the weights using the following 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
   

2. Directly from Hugging Face Hub:
   Clone the repository and use the following commands:

   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
   

Usage

To test the code, run the following command:

python run.py

This script loads the checkpoint and samples from the model on a test input.

Note: Due to the large size of the model (314B parameters), a machine with sufficient GPU memory is required to test the model with the example code. The current implementation of the MoE layer may not be fully optimized; it was chosen to facilitate correctness validation without the need for custom kernels.

License

The code and associated Grok-1 weights in this release are licensed under the Apache 2.0 license. This license only applies to the source files in this repository and the model weights of Grok-1.