Why Organisations Must Embrace - Open Source AI Models

AI models need to be ethical and sustainable. They also need to be community-driven and collaborative in nature. Open source LLMS meet all these criteria and therefore are the future.

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Large language models (LLMs) have emerged as a cornerstone in AI evolution. These sophisticated AI models, which process and generate human-like text, are not just technological marvels; they are shaping the future of communication, content creation, and even coding.

As organisations and individuals navigate this new landscape, one critical decision stands out - choosing between proprietary and open-source LLMs. Let's delve into the compelling reasons to consider open-source LLMs, underscoring the potential risks of overlooking them.

Understanding open-source LLMs

Before delving into the intricacies of open-source LLMs, it's essential to understand their foundation. LLMs are a subset of what's known as foundation models. These are expansive AI models trained on vast amounts of diverse, unlabelled data in a self-supervised manner. The large' in LLMs isn't just hyperbole-it reflects the immense scale of data they're trained on, often reaching petabytes, which translates into a staggering quantity of words and information.

At the heart of LLMs are three core components.

Data: This is the raw material of LLMs the vast, unstructured textual data they're trained on. While a gigabyte of text data might contain roughly 125 million words, LLMs go much further, being trained on exponentially larger datasets.

Architecture: This refers to the underlying structure of the model. For instance, GPT-3.5 utilises a transformer architecture, which is particularly adept at handling the complexities of natural language due to its ability to process sequences of data and capture contextual relationships within text.

Training method: