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metrics.jsonl

@@ -73,3 +73,74 @@
 {"timestamp": 1787808992.8835528, "elapsed_hours": 0.08314489371246762, "event": "step", "step": 1280, "loss": 4.469857053756714, "lr": 2.972978380177053e-05}
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+{"timestamp": 1787809014.5075924, "elapsed_hours": 0.08915157132678561, "event": "sample", "step": 1350, "prompt": "Write a Python function to find the maximum element in a list.", "sample": "Here is a Python function that finds the maximum element in a list:\n```python\ndef max_element(list):\n    # Remove both elements from list and return the result\n    result = []\n\n    for i in range(len(list)):\n        index = list[i] + 1\n\n        # If first element is larger than the next\n        if i > index:\n            result.append(list[i - 1])\n\n    # If second element is smaller than the last\n    else:\n        result.append(list[i])\n\n    return max_element(result)\n```\nHere's an explanation of", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809044.3120317, "elapsed_hours": 0.09743058217896355, "event": "sample", "step": 1500, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "def Fibonacci(n):\n  return n*fib / b-1<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809085.3200462, "elapsed_hours": 0.10882169743378957, "event": "sample", "step": 1650, "prompt": "What is the time complexity of binary search and why?", "sample": "The time complexity of binary search is O(n), where n is the length of the list. In other words, there are two ways to figure out whether a given line segment needs to be traversed or not:\n1) Start from the left cell of the list and find its next position. \n2) If the first step is less than the right cell, return True; otherwise, return False.\n3) We have to find all elements in reverse order from left and end index into a string that we can use for our binary search algorithm by asking the same ", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809149.51407, "elapsed_hours": 0.1266533706585566, "event": "sample", "step": 1950, "prompt": "Explain the difference between a stack and a queue.", "sample": "The question asks for a stack and queue, but I can understand that the two definitions are identical.\n\nA stack is a function which adds an element to each element in another domain. A queue refers to any collection of elements that has no matching object or the other element it belongs to.\n\nA function is a type derived from a list, but not the same as an iterable (or collection). A space in which there are elements can be accessed via some element and no match is left for a list.\n\nA queue may ha", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809187.8598082, "elapsed_hours": 0.1373049646615982, "event": "sample", "step": 2100, "prompt": "Write a Python function to find the maximum element in a list.", "sample": "Here is a Python function that finds the maximum element in an array:\n```python\ndef find_max(lst): \n    if len(lst) < 2 or not lst[-1] == 'A': \n        return 'Error: array is empty'\n\n    if len(lst) == 2 or not lst[-1] in [0, 1]:\n        return 'Error: array size is empty'\n\n    max_num = 0\n    for i in range(len(lst)): \n        if lst[i] > max_num: return 'Error'\n\n    max = min(max_num, key=lambda x: x)\n    if not isinstance(max.__dict__[i], int): return max\n\n    return '{:d}'.format(min(max), ", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809219.870661, "elapsed_hours": 0.14619686822096506, "event": "sample", "step": 2250, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "def fibonacci(n):\n    if n < 0:\n        return 1\n\n    # define the Fibonacci sequence\n    fib_sequence = [0,1]\n    f = 1\n\n    # find the first Fibonacci number in the sequence\n    for i, num in enumerate(f):\n        fib_sequence[i] = fib_sequence.index(num)\n\n    return f<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809260.827247, "elapsed_hours": 0.15757369756698608, "event": "sample", "step": 2400, "prompt": "What is the time complexity of binary search and why?", "sample": "A binary search is a procedure often used to find the next element in an array of size m, and it's most commonly used for finding the smallest value in an array. It involves recursively traversing through all elements within a collection to find where they are from, and then passing them back to the original list until there is one. \n\nIn this algorithm, we start at index 0 (the first element in the array), and for each number i from 1 to m-1 it checks if i is greater than or equal to m, then we ", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787809299.127979, "elapsed_hours": 0.16821278982692295, "event": "sample", "step": 2550, "prompt": "Write a Python function to check if a string is a palindrome.", "sample": "def is_palindrome(s):\n    s = ''.join(set(to_letters + str(x) for x in s))\n\n    return True\n\ndef compare_palindrome_string():\n    string = ''.join(set(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\").split()[:2])\n    for i in range(len(string)):\n        if string == s[i+1][i]:\n            return False\n\n    # Check if both strings are palindromes\n    for string in set(string[::-1]):\n        if is_palindrome(s, string) and not s.endswith('abc'):\n            return False\n\n    # Check if both strings are palindromes, ", "has_open": false, "has_close": false, "has_end": false, "reasoning_len": 0, "is_real_reasoning": false}
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