(B)illion. It indicates the rough number of parameters in the model. Higher is generally more capable. 1B models are currently at the top end of 'easy' to deal with for playing around fine tuning and the like for most home lab setups.

You can ask an LLM exactly this question and it will tell you.

(The answer is billions of parameters)

But what if they want to know they are finding the correct answer?

Asking anonymous people on a forum would be much better.

At least a forum with domain-specific knowledge.

And people to go "no, that's wrong" if someone posts something that's wrong.

No, any answer will do.

I think basic definitions for LLMs are solidly within the bounds of what we would expect e.g. chatgpt to be competent at. The task (defining terms) is simple and the specific content (basic LLM stuff) is easy to check by anyone who works on the LLM.

I agree with the general sentiment that we should not just blindly trust LLMs though.

tbf, the gp comment said 125MB and then 3B, which would be pretty confusing, as it's a typo and should be 125M.

The number of parameters the model is trained on, in billions