[philipkglass]

A completely open model is one like the Allen Institute's Olmo model series:

https://allenai.org/olmo

The trained weights are open, the training software is open, and the data that goes into training the model is open.

Not many models are fully open.

An open weights model is one that has freely available trained weights, and maybe fine-tuning tools, but it lacks the original training data (and usually lacks the training software). These are the most commonly used local models, like Google's Gemma series, Meta's Llama, or Alibaba's Qwen.

[MoonWalk]

So you can apply different weights to those "non-open" models?

Also, I've read a bunch of descriptions of AI components, but none of them has said what the weights are applied to in the model. I guess that every model contains a dictionary of words and phrases, and the weights map relationships between them?

All the descriptions simply talk about weights being applied to "input," but neglect to say what that input is compared to. If a user submits a query, are the words in the query weighed against the words in the model?

Can you recommend a primer on this whole process?

[philipkglass]

The weights are just numbers. I don't what technical background you have in other areas of computing, but I think that this is a good, short introduction that doesn't assume too much:

https://www.3blue1brown.com/lessons/mini-llm/

To quote part of it, Training a model can be thought of as tuning the dials on a really big machine. The way that a language model behaves is entirely determined by these many different continuous values, usually called parameters or weights.

Longer and slightly more technical, "Intro to Large Language Models" by Andrej Karpathy:

https://www.youtube.com/watch?v=zjkBMFhNj_g

[MoonWalk]

Thanks for the reply. I've been programming professionally since the '90s (no background in ML or neural networks and whatnot), and I realize that the weights are numbers. So I was asking whether different weight sets can be applied to a model at will.

I'm really just trying to get a handle on the different layers so I can set up an environment and put it to work for limited coding assistance. For my purposes, I think an all-local setup is the best way for me to learn and should be enough for the coding tasks I have in mind. Mainly I want to automate tedious tasks away, like "modify these Swift classes' members and JSON deserialization routines to match what's coming out of my server API."

Thanks for the link! Reading it right now... and yes, this looks great. Appreciate it.