LLaMA 2: a model overview and demo tutorial in Jupyter Notebooks

Natural Language Processing (NLP) is one of the most popular and commonly used of the myriad subdomains of Machine/Deep Learning. Recently, this has been made even more apparent by the massive proliferation of Generative Pretrained Transformer (GPT) models such as ChatGPT, Bard, and many others to various sites and interfaces throughout the web.

Even more recently, efforts to release completely open source GPT models have risen to the forefront of the AI community, seemingly overtaking massive projects like Stable Diffusion in terms of public attention. This recent slew of GPT models reaching the public sector, either by a completely open sourced release or a more specialized and limited researcher licensing, shows the extent that public interest in Weak AI models has grown over the past year. Projects like LLaMA have shown immense potential as they are spun off into numerous alternative projects like Alpaca, Vicuna, LLaVA, and many more. The development of projects enabling complex and multimodal inputting to this, in its original form, difficult to query model has allowed for some of the best available GPT models to be trained and released completely open source! Notably, the OpenLLaMA project recreated the 7B and 13B parameter LLaMA models using a completely open source dataset and training paradigm.

Today, we are going to discuss the most recent and promising release in the GPT line of models: LLaMA 2. LLaMA 2 represents a new step forward for the same LLaMA models that have become so popular the past few months. The updates to the model includes a 40% larger dataset, chat variants fine-tuned on human preferences using Reinforcement Learning with Human Feedback (RHLF), and scaling further up all the way to 70 billion parameter models.

In this article, we will start by covering the new features and updates to the model featured in the new release in greater detail. Afterwards, we will show how to access and run the new models within a Jupyter Notebook using the Oogabooga Text Generation WebUI.