Datasets:
yimingzhang
/
allie-models

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
54
260
06-11 07:47 - modeling.trainer - INFO - train - iter 1765100: loss 2.8485, time 6.66s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765150: loss 2.8512, time 6.85s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765200: loss 2.8473, time 6.66s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765250: loss 2.8419, time 6.64s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765300: loss 2.8388, time 6.61s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765350: loss 2.8376, time 6.65s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765400: loss 2.8401, time 6.59s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765450: loss 2.8415, time 6.62s
06-11 07:47 - modeling.trainer - INFO - train - iter 1765500: loss 2.8442, time 6.66s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765550: loss 2.8454, time 6.66s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765600: loss 2.8469, time 6.52s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765650: loss 2.8421, time 6.48s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765700: loss 2.8470, time 6.51s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765750: loss 2.8514, time 6.61s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765800: loss 2.8494, time 6.62s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765850: loss 2.8430, time 6.56s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765900: loss 2.8347, time 6.57s
06-11 07:48 - modeling.trainer - INFO - train - iter 1765950: loss 2.8450, time 6.53s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766000: loss 2.8519, time 6.58s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766050: loss 2.8549, time 6.53s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766100: loss 2.8456, time 6.60s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766150: loss 2.8275, time 6.56s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766200: loss 2.8266, time 6.55s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766250: loss 2.8352, time 7.26s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766300: loss 2.8423, time 6.56s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766350: loss 2.8439, time 6.54s
06-11 07:49 - modeling.trainer - INFO - train - iter 1766400: loss 2.8360, time 6.59s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766450: loss 2.8372, time 6.58s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766500: loss 2.8407, time 6.58s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766550: loss 2.8343, time 6.66s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766600: loss 2.8284, time 6.65s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766650: loss 2.8291, time 6.70s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766700: loss 2.8368, time 6.62s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766750: loss 2.8397, time 6.65s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766800: loss 2.8482, time 6.63s
06-11 07:50 - modeling.trainer - INFO - train - iter 1766850: loss 2.8475, time 6.56s
06-11 07:51 - modeling.trainer - INFO - train - iter 1766900: loss 2.8388, time 6.71s
06-11 07:51 - modeling.trainer - INFO - train - iter 1766950: loss 2.8416, time 6.57s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767000: loss 2.8472, time 6.52s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767050: loss 2.8429, time 6.59s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767100: loss 2.8407, time 6.55s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767150: loss 2.8420, time 6.64s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767200: loss 2.8349, time 6.47s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767250: loss 2.8352, time 6.54s
06-11 07:51 - modeling.trainer - INFO - train - iter 1767300: loss 2.8450, time 6.59s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767350: loss 2.8422, time 6.56s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767400: loss 2.8381, time 6.51s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767450: loss 2.8390, time 6.49s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767500: loss 2.8384, time 6.61s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767550: loss 2.8451, time 6.61s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767600: loss 2.8479, time 6.55s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767650: loss 2.8537, time 6.53s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767700: loss 2.8507, time 6.50s
06-11 07:52 - modeling.trainer - INFO - train - iter 1767750: loss 2.8452, time 6.53s
06-11 07:53 - modeling.trainer - INFO - train - iter 1767800: loss 2.8401, time 6.56s
06-11 07:53 - modeling.trainer - INFO - train - iter 1767850: loss 2.8390, time 6.51s
06-11 07:53 - modeling.trainer - INFO - train - iter 1767900: loss 2.8450, time 6.61s
06-11 07:53 - modeling.trainer - INFO - train - iter 1767950: loss 2.8394, time 6.53s
06-11 07:53 - modeling.trainer - INFO - train - iter 1768000: loss 2.8397, time 7.29s
06-11 07:53 - modeling.trainer - INFO - train - iter 1768050: loss 2.8454, time 6.61s
06-11 07:53 - modeling.trainer - INFO - train - iter 1768100: loss 2.8465, time 6.55s
06-11 07:53 - modeling.trainer - INFO - train - iter 1768150: loss 2.8389, time 6.70s
06-11 07:53 - modeling.trainer - INFO - train - iter 1768200: loss 2.8342, time 6.59s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768250: loss 2.8367, time 6.52s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768300: loss 2.8500, time 6.48s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768350: loss 2.8555, time 6.50s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768400: loss 2.8444, time 6.57s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768450: loss 2.8390, time 6.54s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768500: loss 2.8390, time 6.56s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768550: loss 2.8404, time 6.58s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768600: loss 2.8475, time 6.45s
06-11 07:54 - modeling.trainer - INFO - train - iter 1768650: loss 2.8415, time 6.59s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768700: loss 2.8304, time 6.59s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768750: loss 2.8269, time 6.61s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768800: loss 2.8289, time 6.64s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768850: loss 2.8381, time 6.50s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768900: loss 2.8385, time 6.48s
06-11 07:55 - modeling.trainer - INFO - train - iter 1768950: loss 2.8386, time 6.63s
06-11 07:55 - modeling.trainer - INFO - train - iter 1769000: loss 2.8380, time 6.67s
06-11 07:55 - modeling.trainer - INFO - train - iter 1769050: loss 2.8411, time 6.59s
06-11 07:55 - modeling.trainer - INFO - train - iter 1769100: loss 2.8509, time 6.66s
06-11 07:55 - modeling.trainer - INFO - train - iter 1769150: loss 2.8456, time 6.67s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769200: loss 2.8377, time 6.60s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769250: loss 2.8384, time 6.57s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769300: loss 2.8436, time 6.56s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769350: loss 2.8455, time 6.62s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769400: loss 2.8402, time 6.54s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769450: loss 2.8435, time 6.61s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769500: loss 2.8389, time 6.54s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769550: loss 2.8326, time 6.59s
06-11 07:56 - modeling.trainer - INFO - train - iter 1769600: loss 2.8413, time 6.65s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769650: loss 2.8421, time 6.62s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769700: loss 2.8433, time 6.99s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769750: loss 2.8341, time 7.20s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769800: loss 2.8440, time 6.59s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769850: loss 2.8702, time 6.59s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769900: loss 2.8532, time 6.57s
06-11 07:57 - modeling.trainer - INFO - train - iter 1769950: loss 2.8364, time 6.51s
06-11 07:57 - modeling.trainer - INFO - val - iter 1770000: lm_loss 1.3588, value_loss 0.7350, time_loss 0.6647, loss 2.7585, time 6.72s
06-11 07:57 - modeling.trainer - INFO - new best val loss 2.7585