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.
This story is from the March 2024 edition of Open Source For You.
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This story is from the March 2024 edition of Open Source For You.
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