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Didn't explore much, but it seems alpaca-lora has better results for coding tasks. One example I've used was: "Implement quicksort in python.". This is the result with Code alpaca:

    def quicksort(arr):
        if len(arr) < 2:
            return arr
        else:
            pivot = arr[len(arr) / 2]
            left_index = 0
            right_index = len(arr) - 1
            while left_index <= right_index:
                while arr[left_index] < pivot:
                    left_index += 1
                while arr[right_index] > pivot:
                    right_index -= 1
                if left_index <= right_index:
                    temp = arr[left_index]
                    arr[left_index] = arr[right_index]
                    arr[right_index] = temp
                    left_index += 1
                    right_index -= 1
            return quicksort(arr[:left_index]) + [arr[left_index]] + quicksort(arr[right_index + 1:])

Alpaca lora result:

    def quicksort(arr):
        if len(arr) <= 1:
            return arr
        pivot = arr[0]
        left = [arr[i] for i in range(1, len(arr)) if arr[i] < pivot]
        right = [arr[i] for i in range(1, len(arr)) if arr[i] > pivot]
        return quicksort(left) + [pivot] + quicksort(right)

Shorter and much cleaner, not to mention it works (code alpaca version is broken). Also it matches what ChatGPT generates for me.



The Alpaca lora result is incorrect since, for example, if all elements in array are the same the result in just an array with one element.


You are correct also it's not quicksort as it's not in place array, but needs a lot of memory management


Interesting, in my experiments alpaca-lora performed worse but definitely possible that isn't the case generally.




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