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83 lines
3.3 KiB
Markdown
83 lines
3.3 KiB
Markdown
# Grok-1
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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.
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## Table of Contents
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- [Overview](#overview)
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- [Getting Started](#getting-started)
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- [Model Specifications](#model-specifications)
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- [Downloading Weights](#downloading-weights)
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- [Usage](#usage)
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- [License](#license)
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## Overview
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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.
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## Getting Started
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To set up and run Grok-1, follow these steps:
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1. **Clone the repository:**
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```shell
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git clone https://github.com/xai-org/grok-1.git
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cd grok-1
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```
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2. **Install required dependencies:**
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```shell
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pip install -r requirements.txt
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```
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3. **Download the model weights:**
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Ensure that you download the checkpoint and place the `ckpt-0` directory in `checkpoints` (see [Downloading Weights](#downloading-weights)).
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## Model Specifications
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Grok-1 is currently designed with the following specifications:
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- **Parameters:** 314B
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- **Architecture:** Mixture of 8 Experts (MoE)
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- **Experts Utilization:** 2 experts used per token
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- **Layers:** 64
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- **Attention Heads:** 48 for queries, 8 for keys/values
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- **Embedding Size:** 6,144
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- **Tokenization:** SentencePiece tokenizer with 131,072 tokens
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- **Maximum Sequence Length (context):** 8,192 tokens
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- **Additional Features:**
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- Rotary embeddings (RoPE)
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- Supports activation sharding and 8-bit quantization
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## Downloading Weights
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You can download the weights using two methods:
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1. **Using a Torrent Client:**
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Download the weights using the following magnet link:
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```
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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
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```
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2. **Directly from Hugging Face Hub:**
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Clone the repository and use the following commands:
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```shell
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git clone https://github.com/xai-org/grok-1.git && cd grok-1
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pip install huggingface_hub[hf_transfer]
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huggingface-cli download xai-org/grok-1 --repo-type model --include ckpt-0/* --local-dir checkpoints --local-dir-use-symlinks False
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```
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## Usage
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To test the code, run the following command:
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```shell
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python run.py
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```
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This script loads the checkpoint and samples from the model on a test input.
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**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.
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## License
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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.
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