18 Likes
– you have earned Featured Contributor Badge 
Your exceptional post has been selected for our editorial column Staff Picks http://wolfr.am/StaffPicks and Your Profile is now distinguished by a Featured Contributor Badge and is displayed on the Featured Contributor Board. Thank you!
1 Like
Excellent introduction … thanks!
1 Like
This is an excellent hands-on presentation. Thank you for this. I do have a question, though. In the example with:
trainedNet=NetExtract[results["TrainedNet"],"decode"];
Instead of using CloudPut and CloudGet, what would be the correct expression to save to a local file and retrieve it from the local file?
1 Like
After reading “What Is ChatGPT Doing … and Why Does It Work?” I really wanted to explore the networks, better understand what they do and play around with them in Mathematica. This post gave me everything in one go… which is awesome!
3 Likes
Happy to hear that you enjoy it. 
You can use Export, for example:
Export["trainedModel.wlnet", trainedNet]
And then you can get the model with Import. Note that you might want to specify a particular folder directory to store the model. For example with SetDirectory.
Hope this helps.
2 Likes
Happy to hear that.
I’m preparing another post on the original vanilla encoder-decoder transformer architecture (Attention is all you need paper from 2017).
It also might be of your interest this other recent post on Few Shot Learning using GPT-3 and WL: https://community.wolfram.com/groups/-/m/t/2848741
1 Like
Works perfectly. Thanks!
I find using the OpenAILink within the wolfram notebook kinda fun. And the ability to save them in an orderly fashion super useful. I can almost write sort of a graphic novella. Here’s my first try with the defaults
However, the OpenAICompletion did not follow the 7 word and 5 line rule that I gave. it worked fine on the openAI website but not with the packet command. Here’s the output from chatGPT
I tried to change the OpenAIMethod with no luck
1 Like
Thanks a lot for this excellent post which stimulated me to try to understand more of the internals of the network.
In this context, I came up with two questions which I was not able to figure out by myself:
- How can I visualize the word embedding space for the Shakespeare vocabulary in order to test if words which are “semantically near” show up nearby in the word embedding map, similar to what Stephen Wolfram showed in his blogpost “What is ChatGPT doing…”? I have the feeling that it should be quite simple, but all my attempts to access the embedding layer of the trained network failed, probably due to my very limited understanding of the tools of net surgery.
- Is it easily possible to visualize the attention matrix for a particular input sentence?
Any help is appreciated…