[ggerganov]

> I don't recall the details exactly, but I don't think it ever did very much.

How would you have known if the trick actually reduces the outliers in the weights? Even if the transformer quality does not improve overall, having less outliers as a result is very beneficial for more accurate quantization of the data

[danielmarkbruce]

Are you asking "why would you have bothered to look at"?

The "how" is pretty straightforward.

[p1esk]

He's questioning the statement: "I don't think [the trick] ever did very much", because no one has yet looked at whether the trick helps reducing outliers in very large models. If it does help with this, as the blog author believes, then it is indeed a very useful trick.

[danielmarkbruce]

Is he? A surface level reading suggests he's asking "how would you know".. and the answer is... by looking at the parameters. People do that.

>> because no one has yet looked at whether the trick helps reducing outliers in very large models

Given a softmax version doing exactly as the blog post says is baked into a google library (see this thread), and you can set it as a parameter in a pytorch model (see this thread), this claim seems off. "Let's try X, oh, X doesn't do much, let's not write a paper about it" is extremely common for many X.

[tudorw]

This would seem like a really good argument as to why failures should be written up, otherwise where is the list of what has been tried before?

[danielmarkbruce]

Yup, it is. But it isn't going to happen.

[ggerganov]

Yes, I assumed that checking the weights for presence and amount of outliers is not something that is usually done and effects on this can be overlooked. If my assumption is wrong and researchers do usually look at such metrics, then my question is not very relevant.

Agree - the "how" is straightforward