yalm 100b

Introduction

YaLM 100B is a GPT-like neural network developed by Yandex for generating and processing text. It is designed for use by developers and researchers worldwide, supporting both English and Russian languages.

Architecture

The model comprises 100 billion parameters, making it one of the larger models for natural language generation (NLG). Its architecture is inspired by GPT models, leveraging extensive computational resources to deliver high-quality text processing capabilities.

Training

Training the YaLM 100B model required 65 days on a cluster of 800 A100 graphics cards. It utilized 1.7 TB of online text data, including books and other diverse sources. Detailed information on training techniques, acceleration, and stabilization practices is available in articles on Medium and Habr.

Guide: Running Locally

• Environment Setup: Ensure you have Python and necessary libraries installed. Use virtual environments to manage dependencies.
• Model Download: Access the model from its GitHub repository.
• Hardware Recommendations: Due to its large size, running the model locally may require substantial computational resources. Consider using cloud services with GPUs, such as AWS, Google Cloud, or Azure.
• Execution: Load the model using a framework like PyTorch or TensorFlow and execute it for your specific text generation tasks.

License

YaLM 100B is released under the Apache-2.0 license, allowing free use, modification, and distribution.