Want to get started with LLMs? Here's what you need to know
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Re: Want to get started with LLMs? Here's what you need to know
#2[deleted]
Re: Want to get started with LLMs? Here's what you need to know
#3I don't think it's particularly helpful to dive into transformer models, positional encodings and self-attention at the very start of an introduction to LLMs. Understanding how those works does little to help explain what LLMs can do and how you can use them.
I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text, and then emphasize that while this sounds pretty limited it's actually capable of doing all sorts of impressive things once you scale it up enough.
Re: Want to get started with LLMs? Here's what you need to know
#4I don't think it's particularly helpful to dive into transformer models, positional encodings and self-attention at the very start of an introduction to LLMs. Understanding how those works does little to help explain what LLMs can do and how you can use them. I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text, and then emphasize that w…
I'm not sure it's even helpful to talk about transformer at all[0]. Modern LLMs are different than the pure transformer architecture in many detailed aspects. The elephant in the room being that they're not even full transformer—only the decoder part.
[0]: One reason: Never once did I need to know the transformer architecture in order to be able to use these models (prompt engineering, chaining, working with local models, etc.).
I argue that the knowledge of concepts such as ROPE, Mirostat, monkeypatching, etc. is much more crucial than knowing how transformer models work.
> I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text,
I think the same way, but I think it reduces LLMs into "black boxes"—many other models can also predict next tokens based on probabilities. I think we need something that at least captures the general mechanism by which LLMs predict the next token.
Re: Want to get started with LLMs? Here's what you need to know
#5> GPT-4 boasts 100 trillion parameters
Source? I’ve never heard this.
I’ve heard 1 trillion and that it’s a 8 x 175B ensemble.
Re: Want to get started with LLMs? Here's what you need to know
#6> GPT-4 boasts 100 trillion parameters Source? I’ve never heard this. I’ve heard 1 trillion and that it’s a 8 x 175B ensemble.
The entire article looks like an amalgam of stitched together pieces taken from different sources without much care.
Re: Want to get started with LLMs? Here's what you need to know
#7Re: Want to get started with LLMs? Here's what you need to know
#8I don't think it's particularly helpful to dive into transformer models, positional encodings and self-attention at the very start of an introduction to LLMs. Understanding how those works does little to help explain what LLMs can do and how you can use them. I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text, and then emphasize that w…
I wanted to talk about what powers LLMs, which I believe is important. The answer to that is transformers. While I may not have delved deeper into how a transformer actually works, I tried to explain the concepts in the simplest way possible.
Re: Want to get started with LLMs? Here's what you need to know
#9I don't think it's particularly helpful to dive into transformer models, positional encodings and self-attention at the very start of an introduction to LLMs. Understanding how those works does little to help explain what LLMs can do and how you can use them. I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text, and then emphasize that w…
I'm not sure it's even helpful to talk about transformer at all[0]. Modern LLMs are different than the pure transformer architecture in many detailed aspects. The elephant in the room being that they're not even full transformer—only the decoder part. [0]: One reason: Never once did I need to know the transformer architecture in order to be able to use these models (prompt engineering, chaining, working with local mo…
>I think we need something that at least captures the general mechanism by which LLMs predict the next token.
This bit varies a lot since the capabilities involved in prediction depend on the data. If the text is a math book and the prompt is "... three plus five apples is a total of " the crucial capability is arithmetic (plus of course NL capabilities). On the other hand, if you're completing a post from /r/relationship_advice the capabilities involved will be (vaguely) maintaining literary tone, theory of mind, psychology, etc. Within a text the capabilities needed will also vary a lot, where you might need theory of mind at some crucial inflection points, but most of the time its already clear what is going to be said and the model just has to get the wording right.
So, my take would be to really think hard about the data to understand how predictions might be made.
Re: Want to get started with LLMs? Here's what you need to know
#10Earlier quoted context omitted.
The entire article looks like an amalgam of stitched together pieces taken from different sources without much care.
Almost like an AI written article
We have many existing sources on LLMs. I referenced a couple of them that I find great. Repeating the same content doesn't offer much value. :)
And I wrote the entire content myself.