stacksort VS codealpaca

Ah, good ol' stacksort

Reminiscent of stacksort

Yea but is it better than stacksort?

How does that even work tho? Like, my understanding is that GPT is trained in a limited set of parameters and to add anything, you have to redo the whole training. Is it connected to google and just like a fancier version of Stacksort?

There is only one sorting algorithm Stacksort

Semi-related: Someone wrote a sort algorithm that searches StackOverflow for sorting functions and runs them until it returns the correct answer.

https://gkoberger.github.io/stacksort/

Inspired by:

http://xkcd.com/1185/

stacksort

But blindly running stuff from SO is the road to success

CodeAlpaca 7B

This is great!

Addressing the model limitations a bit: in the demonstration data that is provided to the base model, we should prevent computed or "looked up" answers.

I've seen some of the demonstration data that people are using to train instruction-tuned models and are being taught to respond by making up answers to solutions it shouldn't try to compute. Btw, the output is wrong.

{ "instruction": "What would be the output of the following JavaScript snippet?", "input": "let area = 6 * 5;\nlet radius = area / 3.14;", "output": "The output of the JavaScript snippet is the radius, which is 1.91." }, [1]

The UI note for now would get us very far but by filtering out demonstrations that retrieve or compute information should be filtered out.

Symbol tuning [2] is addressing the quality of demonstrations but we can take it further by removing retrievals and computations altogether.

Bonus: we can demonstrate how to make it respond so that the user/agent be informed of how to compute or retrieve.

1: https://github.com/sahil280114/codealpaca/commit/0d265112c70...

2: https://arxiv.org/abs/2305.08298

I found guides like this: https://github.com/sahil280114/codealpaca

Try, https://github.com/sahil280114/codealpaca, or we’re you trying to stick with more generalist models?

Oh, I see. That makes sense. I'm also sleep deprived over here so my reading comprehension is a bit low ;|. Well in that case check out this link: https://github.com/sahil280114/codealpaca

Sorry for the late reply, as I said Flan-UL2 (or Flan-T5 if you want lighter models) fine-tuned against a dataset like CodeAlpaca's[0] is probably the best solution if it's intended for commercial use (otherwise LLaMa should perform better).

[0]: https://github.com/sahil280114/codealpaca