Re:Mistral "Mixtral" 8x7B 32k model [magnet]
by throwuwu on Sunday December 10, 2023 @07:52AM
(#38591195)
Did you use gpt4? I asked it to give me some examples code for a dialog box with an input field and a button and it gave me this:
use slint::slint;
slint! { DialogBox := Window { width: 400px; height: 200px; title: "Input Dialog";
VerticalBox {
padding: 20px;
spacing: 10px;
TextInput {
id: input_field;
placeholder_text: "Enter text here";
}
Button {
text: "Submit";
clicked => {
// Handle the button click event
println!("Input: {}", input_field.text());
}
}
}
}fn main() { DialogBox::new().run(); }
Re:Mistral "Mixtral" 8x7B 32k model [magnet]
by emporas on Tuesday December 12, 2023 @11:07AM
(#38614050)
Thanks for letting me know, i didn't use GPT-4, but i was under the impression that the cutoff data between all GPT's was the same, or almost the same. The code is correct, yes.
I do not have a GPT4 subscription, i did not bother because it is so slow, limited queries etc. If the cutoff date is improved, like being updated periodically i may think about it. (Late response, forgot about the comment!)
Just today, i asked GPT and Bard(Gemini) to write code using slint, neither of them had any idea of slint. Slint being a relatively new library, like two and a half (0.1 version) to one and a half (0.2 version) years back [1] is not something they trained on.
Natural language doesn't change that much over the course of a handful of years, but in coding 2 years back may as well be a century. My argument is that, SmallLMs not only they are relevant, they are actually desirable, if the best solution is to be retrained from scratch.
If on the other hand a billion token context window proves to be practical, or the RAG technique solves most of use cases, then LLMs might suffice. This RAG technique, could it be aware, of million of git commits daily, on several projects, and keep it's knowledge base up to date? I don't know about that.
https://github.com/slint-ui/slint/releases?page=3