codealpaca VS llemmings

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

I did this experiment (a game) to see what's up and what's down around all this: https://github.com/romland/llemmings.

While there is some GPT4 in there, it's mostly ChatGPT and a small handful of LLaMA solutions.

That project is a contrived scenario and not realistic, but I wanted to experiment with _exactly_ what you are talking about.

Very often I could have done things a lot faster myself, but there is one aspect that was actually helpful, and I did not foresee it. When inspiration gets a bit low and you're not in the "zone"; throwing something into an LLM will very often give me a push to keep at it. Even if what is coming up is mostly grunt work.

The other day I threw together a script to show the commits in a reverse order and filter out (most of) the human commits (glue) over at https://llemmings.com/