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metrics.jsonl

@@ -590,3 +590,77 @@
 {"timestamp": 1787811099.6870093, "elapsed_hours": 0.668368076019817, "event": "step", "step": 10400, "loss": 4.379652194976806, "lr": 1.1169649394684637e-05}
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+{"timestamp": 1787811123.346903, "elapsed_hours": 0.6749402687284681, "event": "sample", "step": 10500, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "Here is the Python function that returns the nth Fibonacci number:\n```python\ndef fib(n):\n  if n <= 1 or 2 * n <= 5:\n    return \"Error: Fibonacci number is not a valid integer.\"\n\n  if 'F' in fib(n):\n    return f\"{fib} {n-1} (How many numbers between 1 to n - 10 are a Fibonacci number?) \"\n  else:  # Example usage of fib(1)\n    return fib(n-2) + Fibonacci(fib())(3, 'F')+1\n\n\nprint(f'The Fibonacci number is {fib}', fib())\n```<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811168.2768564, "elapsed_hours": 0.6874208113882276, "event": "sample", "step": 10650, "prompt": "What is the time complexity of binary search and why?", "sample": "To find the time complexity of binary search and why, we need to follow a simple approach. \n\nLet's assume that there are two branches: root 1 (root 2) and root 3 (root 4). Then we have to find the shortest path from root 2 to root 1, which is (-1+0)/2 = -1/4. \n\nNow let's find the shortest path from root 4 to a node (the root of this graph, where we have next minimum value in a tree).\n\nThe time complexity for binary search is \nO(n log n) because \nfor every node A, B in G such that P(B|A)=1+P(B) =", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811231.9208322, "elapsed_hours": 0.7050996936029859, "event": "sample", "step": 10950, "prompt": "Explain the difference between a stack and a queue.", "sample": "A stack is a sequence of elements that are transported from one node up to another. In an array, each element of the stack has a value (length) and size that is equal to its own length (width). Each node in the queue represents an element, with no more than \"len\" elements.\n\nA queue is a sequence of nodes that are connected to one another by two or more nodes in the same direction, and these nodes are joined together.\n\nWhen we say \"all nodes get added\" (which is a stack), it means that the curren", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811263.1515555, "elapsed_hours": 0.7137748944097095, "event": "sample", "step": 11100, "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 input list of numbers and then returns the result:\n```python\ndef max_element(lst): \n    max = float('-inf')\n\n    for num in lst:\n        if isinstance(num, int):\n            max = max_element(max)\n\n    return max<|/think|><|endoftext|>", "has_open": false, "has_close": false, "has_end": false, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811334.1939993, "elapsed_hours": 0.7335089065631231, "event": "sample", "step": 11400, "prompt": "What is the time complexity of binary search and why?", "sample": "You can use binary search to solve the problem. To accomplish this, you will need to implement a function that takes two parameters: \"l1\" and \"l2\". The function should return the time complexity of binary search. Here's an explanation of how this can be done:\n\n1. Initialize a variable \"time\" to 0, which will store the time for each query. This variable should only hold floats in memory (0-based indexing).\n2. Iterate through all the queries, starting from 0 and performing a binary search on them ", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811366.5876288, "elapsed_hours": 0.742507136993938, "event": "sample", "step": 11550, "prompt": "Write a Python function to check if a string is a palindrome.", "sample": "To check if a string is a palindrome, you can use the following code:\n```python\ndef is_palindrome(string):  # Check if string length is in reverse order and convert to lowercase\n  str = string[::-1]  \n\n # Get the length of the input string\n n = len(string) - 1 + int(str.search('.'))\n\n # Check if reversed string is lowercase (e.g., \"abc\")\n if n == str: \n return True\n\n return False\n```<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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+{"timestamp": 1787811399.3828363, "elapsed_hours": 0.7516169167889489, "event": "sample", "step": 11700, "prompt": "Explain the difference between a stack and a queue.", "sample": "The difference between a stack and an queue is that both functions call the same function at the time we have taken advantage of when trying to aggregate results into two sets. This).\n\nIn a queue, an element is calculated first (which can be used as the parent of this stack). In a queue with two elements and three children, each child has one value.\n\nIn an empty set or an array, the items are always added to the left of the top of the queue (which is in this case). This means each value is the s", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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