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The History of Open-Source LLMs: Better Base Models (Part Two) | by Cameron R. Wolfe, Ph.D. | Nov, 2023

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November 19, 2023
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The History of Open-Source LLMs: Better Base Models (Part Two) | by Cameron R. Wolfe, Ph.D. | Nov, 2023
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How LLaMA, MPT, Falcon, and LLaMA-2 put open-source LLMs on the map…

Cameron R. Wolfe, Ph.D.

Towards Data Science

16 min learn

·

19 hours in the past

(Photograph by Iñaki del Olmo on Unsplash)

Open-source analysis on giant language fashions (LLMs) is extremely precious, because it goals to democratize a strong and influential expertise. Though open-source LLMs at the moment are generally used and broadly studied, this space of analysis noticed some preliminary struggles that had been tough to beat. Particularly, open-source LLMs carried out poorly at first and had been closely criticized. Inside this overview, we’ll research a line of analysis that modified this narrative by making high-performing pre-trained LLMs obtainable to everybody. Provided that pre-training a language mannequin is so costly, the fashions we’ll research listed here are particularly impactful. After these high-performing base fashions had been created and launched, many individuals might conduct analysis utilizing these fashions at marginal added value.

“The capabilities of LLMs are exceptional contemplating the seemingly easy nature of the coaching methodology.” — from [14]

The present collection. This overview is an element two of a 3 half collection on the historical past of open-source LLMs. The first part within the collection overviewed preliminary makes an attempt at creating open-source LLMs. Right here, we’ll research the most well-liked open-source base fashions (i.e., language fashions which have been pre-trained however not fine-tuned or aligned) which might be at present obtainable. Subsequent time, we’ll go over how these fashions could be fine-tuned or aligned to create quite a lot of helpful functions.

(from [10, 12, 14, 15])

Partially considered one of this collection, we noticed that the early days of analysis on open-source LLMs resulted within the proposal of a number of essential base fashions, akin to OPT and BLOOM. Nonetheless, these fashions had been broadly thought-about to carry out fairly poorly in comparison with closed-source pre-trained fashions (e.g., GPT-3). How will we resolve this? First, we have to take a deeper take a look at the LLM coaching course of.

Coaching pipeline. LLMs are skilled in a number of steps, as proven within the determine beneath. First, we pre-train the mannequin…



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