flan-alpaca VS codealpaca

If you have enough task or domain specific training data, the model size becomes less important. For example, you can take an instruction tuned smaller model like FlanT5 and fine tune for your specific case: https://github.com/declare-lab/flan-alpaca

Hi, you can try Flan-Alpaca here which does not have such restrictions: https://github.com/declare-lab/flan-alpaca

I've been following open source LLMs for a while and at first glance this doesn't seem too powerful compared to other open models, Flan-Alpaca[0] is licensed under Apache 2.0, and it seems to perform much better. Although I'm not sure about the legalities about that licensing, since it's basically Flan-T5 fine-tuned using the Alpaca dataset (which is under a Non-Commercial license).

Nonetheless, it's exciting to see all these open models popping up, and I hope that a LLM equivalent to Stable Diffusion comes sooner than later.

[0]: https://github.com/declare-lab/flan-alpaca

Someone's already taking care of that - Flan-Alpaca

I think this might be exactly what you're looking for https://github.com/declare-lab/flan-alpaca

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