This paper demonstrates that the inference operations of several open-weight large language models (LLMs) can be mapped to an exactly equivalent linear system for an input sequence. It explores the use of the 'detached Jacobian' to interpret semantic concepts within LLMs and potentially steer next-token prediction.
• A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
