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metrics.jsonl

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 {"timestamp": 1787807647.8245602, "elapsed_hours": 0.004096345106760661, "event": "start", "config": {"vocab_size": 50304, "d_model": 896, "n_layers": 18, "n_heads": 14, "n_kv_heads": 2, "max_len": 2048, "rope_theta": 500000.0, "window_size": 512, "pattern_mult": 2.5, "reason_mult": 0.75, "reason_depth": 2, "gate_hidden": 64, "gate_init": 0.0, "diff_lambda_init": 0.8, "tie_embeddings": true, "sft_lr": 3e-05, "sft_min_lr": 3e-06, "warmup": 20, "epochs": 3, "micro_batch": 2, "grad_accum": 2, "grad_clip": 1.0, "wd": 0.01, "log_every": 10, "sample_every": 75, "ckpt_every": 300, "max_hours": 11.0, "max_examples": 1096, "end_token_weight": 50.0, "tag_close_weight": 60.0, "tag_open_weight": 50.0}, "n_examples": 1094}
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+{"timestamp": 1787807666.3441954, "elapsed_hours": 0.009240687688191732, "event": "sample", "step": 75, "prompt": "What is the time complexity of binary search and why?", "sample": "The time complexity of binary search and why is O(n log n) for finding the shortest path in a given array.\n\nHere's how you can implement it:\n```python\ndef binary_search(arr):\n    for i in range(len(arr) - 1, 0, -1):\n        (i, j), (a, b), (c, d) = arr[0], arr[1]\n        if i == 0: return a\n        elif (i + j) % 2 != 0 and (c - d * i) // 2 == 1: return c\n    return binary_search(arr[1]), binary_search(arr[2])<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 100.0}
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+{"timestamp": 1787807710.597706, "elapsed_hours": 0.021533329486846924, "event": "sample", "step": 225, "prompt": "Explain the difference between a stack and a queue.", "sample": "A stack and a queue are two different types of nodes in the same order, where each node represents an element (a single item) or another value. Each sub-node has a copy of that stack and we have to find the most common id for this pair:\n\n1. A node with the same length (next value) as a list of elements in each sub-node.  For example, let's say we have an array of values [1..50], and for every item located at index 1 in the list (to the right), there are two items: one to add, a second to remove,", "has_open": false, "has_close": false, "has_end": false, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 33.33333333333333}
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+{"timestamp": 1787807751.0684483, "elapsed_hours": 0.0327752023935318, "event": "sample", "step": 375, "prompt": "Write a Python function that returns the nth Fibonacci number.", "sample": "def fibonacci_nth(num):\n    if num <= 1:\n        return \"Error: The number is not a positive integer\"\n\n    fib = [0, 1]\n    prev_num = 0\n    for i in range(2):\n        fib.append(fib[i])\n\n    for i, c and prev_num:\n        if fib[c] > 10:\n            prev_num = c\n\n    return fib, prev_num<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 60.0}
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+{"timestamp": 1787807767.894322, "elapsed_hours": 0.03744905617502001, "event": "sample", "step": 450, "prompt": "What is the time complexity of binary search and why?", "sample": "A binary search is a technique used to find the minimum number of elements in an array. It uses algorithms like Algorithm-based or brute force, and Euler's algorithm provides better performance for a large problem in the context of many large data structures. It uses a algorithm like Finder or Stata, where each element is represented by an integer and each index represents its position in the array. \n\nThe time complexity of binary search and why?<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 66.66666666666666}
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+{"timestamp": 1787807788.1392033, "elapsed_hours": 0.04307263433933258, "event": "sample", "step": 525, "prompt": "Write a Python function to check if a string is a palindrome.", "sample": "Here is a Python function that checks if a string is palindrome:\n```python\ndef is_palindrome(s): \n    # Check for palindromes.\n    s = ''.join(''.join([str(x) for x in str(s)]))\n\n    # Check if the string is a palindrome\n    return s == s[::-1]\n```\nHere's an explanation of how this function works:\n\n* `is_palindrome(s)` checks whether a string is a palindromic or not. If it's an alphabetic, then `s` will be in reverse order (assuming we can't use any string built-in).\n* If the string is a palindr", "has_open": false, "has_close": false, "has_end": true, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 71.42857142857143}
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+{"timestamp": 1787807810.6973748, "elapsed_hours": 0.04933879309230381, "event": "sample", "step": 600, "prompt": "Explain the difference between a stack and a queue.", "sample": "Triple stack and queue are two different types of data structures. A stack is a set of items or objects, such as an object that has no elements in it but not on the same line.\n\nA queue can contain a few types of data:\n\n1) Stack-style: This is a data structure that allows you to access and manipulate data. A queue also includes built-in functions for reading, writing, or checking the contents of an object.\n\n2) Queue-style: This is a data structure that allows you to access and manipulate data in ", "has_open": false, "has_close": false, "has_end": false, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 62.5}
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+{"timestamp": 1787807836.0302513, "elapsed_hours": 0.05637570321559906, "event": "sample", "step": 675, "prompt": "Write a Python function to find the maximum element in a list.", "sample": "Here's a Python function that finds the maximum element in a list:\n```python\ndef max_element(list):\n   if len(list) == 0 or len(list[0]) > 2:\n      return None\n\n   max_element = num for count in list if count < 1 or count == 0\n\n   min_value, max_value = 0, None\n   for num in list:\n      if isinstance(num, int) and num > max_value or num < min_value:\n          if count % 2 == 0 and isinstance(num, int) or num > max_value:\n             max_element = num\n\n  return int(max_element)\n```<|im_end|>", "has_open": false, "has_close": false, "has_end": true, "pct_open": 0.0, "pct_close": 0.0, "pct_end": 66.66666666666666}
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