Introduction

Today’s Token looks at a big question in AI: should we keep making larger language models (LLMs) or switch to smaller, more efficient ones? We'll explore what each choice means for AI's future.

LLMs are impressive, handling complex tasks well. Recently released Falcon has 180 billion parameters and is trained on 3.5 trillion tokens! But large models need a lot of power and aren't easy to use everywhere.

Small Language Models (SLMs), with 10 billion parameters or less, can do similar jobs but don't require as much power. They're easier to manage and cost less. A central theme is model compression, essential for converting large models into efficient, smaller versions. Techniques like pruning, quantization, and knowledge distillation are crucial, especially for resource-limited applications.

Finally, we’ll talk about starting with small models from the beginning. This means using less data and focusing on specific tasks, which can be a smarter way to build AI. This article will discuss the pros and cons of both big and small AI models, helping you understand which might be best for different uses. Let’s dive in:

Conclusion

As we've navigated the complexities of large language models (LLMs) and small language models (SLMs), one thing is clear: the future of AI isn't just about size, but about smart, efficient choices. Yes, annual hardware advancements could further accelerate the growth of large models, revealing yet unknown properties of LLMs. But optimization methods are making models more efficient without hitting diminishing returns in LLMs scaling. Whether you're captivated by the vast capabilities of LLMs or the agility and resourcefulness of SLMs, understanding the nuances of each is crucial for harnessing their full potential.

Our deep dive into model compression techniques for LLMs and the strategic approach of starting with smaller models offers a roadmap for those looking to optimize AI for various applications. From the intricate processes of pruning and quantization to the innovative realms of knowledge distillation and low-rank factorization, we've explored paths to make large models more manageable and small models more powerful.

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Previously in the FM/LLM series: