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metrics.jsonl

@@ -664,3 +664,80 @@
 {"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}
 {"timestamp": 1787811403.0956502, "elapsed_hours": 0.7526482542355856, "event": "step", "step": 11720, "loss": 4.433955807685852, "lr": 8.19110271442522e-06}
 {"timestamp": 1787811407.029655, "elapsed_hours": 0.7537410332096948, "event": "step", "step": 11740, "loss": 4.4062044811248775, "lr": 8.149718946727533e-06}
+{"timestamp": 1787811411.56729, "elapsed_hours": 0.7550014872683419, "event": "step", "step": 11760, "loss": 4.379000906944275, "lr": 8.108461883385879e-06}
+{"timestamp": 1787811415.2753248, "elapsed_hours": 0.7560314969884024, "event": "step", "step": 11780, "loss": 4.330032305717468, "lr": 8.067332150421062e-06}
+{"timestamp": 1787811419.0486736, "elapsed_hours": 0.7570796494351493, "event": "step", "step": 11800, "loss": 4.370001916885376, "lr": 8.026330371921813e-06}
+{"timestamp": 1787811422.8696449, "elapsed_hours": 0.7581410302718481, "event": "step", "step": 11820, "loss": 4.407141704559326, "lr": 7.985457170035328e-06}
+{"timestamp": 1787811426.8840258, "elapsed_hours": 0.7592561362187068, "event": "step", "step": 11840, "loss": 4.4023991346359255, "lr": 7.944713164957828e-06}
+{"timestamp": 1787811430.336159, "elapsed_hours": 0.7602150619692273, "event": "sample", "step": 11850, "prompt": "Write a Python function to find the maximum element in a list.", "sample": "def max_element(lst):\n    \"\"\"Find the maximum element in a list.\n\n    Returns:\n    - ndarray(list)\n    -- return max(numbers, k=0 to ndarray[-1])\n\n    \"\"\"<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811432.1721797, "elapsed_hours": 0.7607250677214729, "event": "step", "step": 11860, "loss": 4.415131711959839, "lr": 7.90409897492514e-06}
+{"timestamp": 1787811435.9053106, "elapsed_hours": 0.7617620486683315, "event": "step", "step": 11880, "loss": 4.377901763916015, "lr": 7.863615216203325e-06}
+{"timestamp": 1787811439.6806054, "elapsed_hours": 0.7628107416629791, "event": "step", "step": 11900, "loss": 4.401000275611877, "lr": 7.823262503079315e-06}
+{"timestamp": 1787811443.522611, "elapsed_hours": 0.7638779653443231, "event": "step", "step": 11920, "loss": 4.387313632965088, "lr": 7.783041447851609e-06}
+{"timestamp": 1787811447.8988385, "elapsed_hours": 0.7650935841931237, "event": "step", "step": 11940, "loss": 4.353269119262695, "lr": 7.742952660820972e-06}
+{"timestamp": 1787811452.1285067, "elapsed_hours": 0.7662684919436773, "event": "step", "step": 11960, "loss": 4.313549246788025, "lr": 7.702996750281173e-06}
+{"timestamp": 1787811456.0579197, "elapsed_hours": 0.7673599957095252, "event": "step", "step": 11980, "loss": 4.384074373245239, "lr": 7.663174322509757e-06}
+{"timestamp": 1787811460.0977452, "elapsed_hours": 0.7684821693102518, "event": "step", "step": 12000, "loss": 4.401123738288879, "lr": 7.623485981758852e-06}
+{"timestamp": 1787811463.4515016, "elapsed_hours": 0.7694137683179644, "event": "sample", "step": 12000, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "Here is a Python function that returns the nth Fibonacci number:\n```python\ndef fibonacci(n): \n  if not isinstance(n, int) or n < 1: \n    return 0\n\n  fibonacci(n-1) = fibonacci(n-2) + fibonacci(n - 3)\n  return fibonacci(n)-fibonacci(n+1)\n```\nThis function first checks if the input number is less than 1 and then returns 0. If not, it will return -fibonacci(n+1) because that's the nth Fibonacci number and it should never be used again.<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811473.6575787, "elapsed_hours": 0.7722487899992201, "event": "step", "step": 12020, "loss": 4.385796298980713, "lr": 7.583932330245989e-06}
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+{"timestamp": 1787811492.5164175, "elapsed_hours": 0.7774873560004764, "event": "step", "step": 12120, "loss": 4.4056481885910035, "lr": 7.388205345295822e-06}
+{"timestamp": 1787811496.680232, "elapsed_hours": 0.7786439713504579, "event": "step", "step": 12140, "loss": 4.35893759727478, "lr": 7.349472360694845e-06}
+{"timestamp": 1787811504.116645, "elapsed_hours": 0.7807096415758132, "event": "sample", "step": 12150, "prompt": "What is the time complexity of binary search and why?", "sample": "A binary search is a sub-programmed version of the original problem. It strategy repeatedly selects an element from another project and then searches for it to find its maximum value in particular.\n\nThis is because each node of a binary search has exactly 2 nodes: one from the current project and another from the previous. Each node is used to find its maximum value in some other graph, such as a tree with an edge that describes the parent node of each program (e.g., a regular system). \n\nFor exa", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811506.001428, "elapsed_hours": 0.7812331924173567, "event": "step", "step": 12160, "loss": 4.401887626647949, "lr": 7.310878223122432e-06}
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+{"timestamp": 1787811517.812332, "elapsed_hours": 0.7845139990912543, "event": "step", "step": 12220, "loss": 4.358867077827454, "lr": 7.195934738133813e-06}
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+{"timestamp": 1787811525.6246696, "elapsed_hours": 0.7866840927468406, "event": "step", "step": 12260, "loss": 4.3668567132949825, "lr": 7.120010662959629e-06}
+{"timestamp": 1787811530.009968, "elapsed_hours": 0.7879022311502033, "event": "step", "step": 12280, "loss": 4.3828327226638795, "lr": 7.082261831103449e-06}
+{"timestamp": 1787811533.7537959, "elapsed_hours": 0.7889421833886041, "event": "step", "step": 12300, "loss": 4.354800705909729, "lr": 7.044655900838529e-06}
+{"timestamp": 1787811536.383901, "elapsed_hours": 0.7896727681159973, "event": "sample", "step": 12300, "prompt": "Write a Python function to check if a string is a palindrome.", "sample": "Here is a Python function that checks if a string:\n```python\ndef is_palindrome(s): \n    # convert string to an array of the same length as s.\n    arr = list(set(''.join([str(i, j) for i in range(len(s)+1))]))\n    # check if the string is a palindrome (i.e., it should be palindromes)\n    return arr == sorted(arr, reverse=True)\n```<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811540.5254135, "elapsed_hours": 0.7908231881592008, "event": "step", "step": 12320, "loss": 4.384897198677063, "lr": 7.007193442784606e-06}
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+{"timestamp": 1787811548.5942738, "elapsed_hours": 0.7930645383066601, "event": "step", "step": 12360, "loss": 4.383255858421325, "lr": 6.932701214895078e-06}
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+{"timestamp": 1787811556.2664943, "elapsed_hours": 0.7951957107914819, "event": "step", "step": 12400, "loss": 4.392659287452698, "lr": 6.858789668697757e-06}
+{"timestamp": 1787811560.15869, "elapsed_hours": 0.7962768761979209, "event": "step", "step": 12420, "loss": 4.38863434791565, "lr": 6.8220530544987165e-06}
+{"timestamp": 1787811564.2825882, "elapsed_hours": 0.797422403494517, "event": "step", "step": 12440, "loss": 4.363587083816529, "lr": 6.785463290211391e-06}
+{"timestamp": 1787811569.932186, "elapsed_hours": 0.7989917362398572, "event": "sample", "step": 12450, "prompt": "Explain the difference between a stack and a queue.", "sample": "A stack and a queue are two types of words that can be used to perform operations on data.\nArrays, as the name suggests, are usually made up of two parts: stack and queue. \nThe first part involves a node (the 3rd piece) with the value \"3\". The second is created by adding an element to each of these two parts.\nA queue, when combined, adds elements from both the top and back end of a stack. This is because \"stack\" can be used to store items or data at once. The last part, when combined with the nu", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811571.8442564, "elapsed_hours": 0.7995228670040766, "event": "step", "step": 12460, "loss": 4.366133337020874, "lr": 6.7490209310365544e-06}
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+{"timestamp": 1787811591.068009, "elapsed_hours": 0.8048627981874678, "event": "step", "step": 12560, "loss": 4.424572153091431, "lr": 6.569039486840683e-06}
+{"timestamp": 1787811594.830526, "elapsed_hours": 0.8059079419242011, "event": "step", "step": 12580, "loss": 4.37031243801117, "lr": 6.533493091717831e-06}
+{"timestamp": 1787811598.9547327, "elapsed_hours": 0.8070535547203488, "event": "step", "step": 12600, "loss": 4.385384540557862, "lr": 6.4980979250185065e-06}
+{"timestamp": 1787811600.3497102, "elapsed_hours": 0.8074410485559039, "event": "sample", "step": 12600, "prompt": "Write a Python function to find the maximum element in a list.", "sample": "You can use the `max()` function to find the maximum element in a list. Here's an example:\n\n```python\ndef max(lst):  # if the array is empty, return -1 (as it will always be 0)\n\n return max(0)\n```<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811604.228105, "elapsed_hours": 0.8085183803240458, "event": "step", "step": 12620, "loss": 4.346694631576538, "lr": 6.4628545238170665e-06}
+{"timestamp": 1787811607.8778863, "elapsed_hours": 0.8095322085751427, "event": "step", "step": 12640, "loss": 4.391603813171387, "lr": 6.427763422885026e-06}
+{"timestamp": 1787811611.7323847, "elapsed_hours": 0.8106029025713603, "event": "step", "step": 12660, "loss": 4.44935462474823, "lr": 6.392825154682949e-06}
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+{"timestamp": 1787811619.511193, "elapsed_hours": 0.812763682603836, "event": "step", "step": 12700, "loss": 4.419691343307495, "lr": 6.3234092347077245e-06}
+{"timestamp": 1787811623.1491003, "elapsed_hours": 0.8137742125325733, "event": "step", "step": 12720, "loss": 4.3924746799469, "lr": 6.288932636228399e-06}
+{"timestamp": 1787811627.0782914, "elapsed_hours": 0.814865654375818, "event": "step", "step": 12740, "loss": 4.351917161941528, "lr": 6.254610977050696e-06}
+{"timestamp": 1787811632.279909, "elapsed_hours": 0.8163105481200748, "event": "sample", "step": 12750, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "You can use the following function:\n\n```python\ndef fibonacci(n): \n    if n<2:  # factorial of 2 is in Fibonacci series, so we need to return the first; \n        return fibonacci(n-1) + fibonacci(n-2) \n\n    else:\n        return n * Fibonacci(n - 1) + fibonacci(n-2)\n\n\n# Driver code to test the function\nprint(fibonacci(5))  # Output: 5<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811640.3552158, "elapsed_hours": 0.8185536890559726, "event": "step", "step": 12760, "loss": 4.3929344654083256, "lr": 6.220444777959927e-06}
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+{"timestamp": 1787811655.9660265, "elapsed_hours": 0.8228900251785914, "event": "step", "step": 12840, "loss": 4.31297287940979, "lr": 6.085344915446028e-06}
+{"timestamp": 1787811659.871279, "elapsed_hours": 0.8239748176601198, "event": "step", "step": 12860, "loss": 4.377669415473938, "lr": 6.0519637457335745e-06}
+{"timestamp": 1787811663.720018, "elapsed_hours": 0.8250439118676716, "event": "step", "step": 12880, "loss": 4.371508479118347, "lr": 6.018741111008983e-06}
+{"timestamp": 1787811667.5230367, "elapsed_hours": 0.8261003058486515, "event": "step", "step": 12900, "loss": 4.347213912010193, "lr": 5.985677515381335e-06}
+{"timestamp": 1787811669.139464, "elapsed_hours": 0.8265493133332994, "event": "sample", "step": 12900, "prompt": "What is the time complexity of binary search and why?", "sample": "The time complexity of binary search is O(n) for the time and space complexity is O(1). In this case, we need to have two bases: the target value (0 or 1), which represents how many operations are needed. The use of binary search is O(1) for sorting, whereas the time complexity is O(n).<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
+{"timestamp": 1787811673.1370587, "elapsed_hours": 0.8276597563425699, "event": "step", "step": 12920, "loss": 4.378670377731323, "lr": 5.952773460546514e-06}
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+{"timestamp": 1787811699.46518, "elapsed_hours": 0.8349731233384874, "event": "sample", "step": 13050, "prompt": "Write a Python function to check if a string is a palindrome.", "sample": "def is_palindrome(s):\n    return s == str.punctuation()<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "reasoning_len": 0, "is_real_reasoning": false}
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