Your guide to choosing an open source LLM - Knime

There's no denying that we're undergoing an AI revolution powered by large language models (LLMs). A class of deep neural networks, these systems are mostly used to understand and generate human language, and to mimic conversational behaviors. They are called “large” because of the size of their parameters, which range from hundreds of millions to even trillions.

Some of the best-known LLMs include OpenAI’s ChatGPT and Google’s Bard. These LLMs are proprietary solutions. That means there’s no transparency. Their source code, architecture, and inference strategy can’t be inspected, which raises concerns over data security and privacy.

Open source LLMs have gained traction as a result of these data security and privacy concerns. With an open source LLM, the source code, architecture, and inference strategy can be inspected, facilitating auditing and customization. Open source LLMs have the additional advantage of being free to use, even if there are sometimes licensing restrictions for commercial purposes.

In this article, we give you an overview of four popular open source LLMs – Llama 2, Bloom, Claude 2, and Falcon, a short list of criteria to consider to help you choose which one to use for your use case, and pointers to how you can apply them using the low-code data science tool, KNIME Analytics Platform.

Read more:


Post popolari in questo blog

Building a high-performance data and AI organization - MIT report 2023

Dove trovare raccolte di dati (dataset) utilizzabili gratuitamente

AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity. - IFM blog