Datasets:
yimingzhang
/
allie-models

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
54
260
06-12 02:11 - modeling.trainer - INFO - train - iter 1891750: loss 2.8273, time 6.43s
06-12 02:11 - modeling.trainer - INFO - train - iter 1891800: loss 2.8309, time 6.38s
06-12 02:11 - modeling.trainer - INFO - train - iter 1891850: loss 2.8371, time 6.36s
06-12 02:11 - modeling.trainer - INFO - train - iter 1891900: loss 2.8356, time 6.29s
06-12 02:11 - modeling.trainer - INFO - train - iter 1891950: loss 2.8304, time 6.43s
06-12 02:11 - modeling.trainer - INFO - train - iter 1892000: loss 2.8356, time 6.36s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892050: loss 2.8402, time 6.43s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892100: loss 2.8369, time 6.54s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892150: loss 2.8253, time 6.53s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892200: loss 2.8298, time 6.32s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892250: loss 2.8360, time 6.44s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892300: loss 2.8298, time 6.57s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892350: loss 2.8352, time 7.04s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892400: loss 2.8355, time 6.34s
06-12 02:12 - modeling.trainer - INFO - train - iter 1892450: loss 2.8327, time 6.42s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892500: loss 2.8357, time 6.43s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892550: loss 2.8326, time 6.37s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892600: loss 2.8329, time 6.30s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892650: loss 2.8326, time 6.39s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892700: loss 2.8207, time 6.36s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892750: loss 2.8238, time 6.36s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892800: loss 2.8320, time 6.35s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892850: loss 2.8242, time 6.34s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892900: loss 2.8259, time 6.33s
06-12 02:13 - modeling.trainer - INFO - train - iter 1892950: loss 2.8290, time 6.37s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893000: loss 2.8254, time 6.48s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893050: loss 2.8203, time 6.35s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893100: loss 2.8239, time 6.29s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893150: loss 2.8379, time 6.34s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893200: loss 2.8414, time 6.34s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893250: loss 2.8348, time 6.29s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893300: loss 2.8286, time 6.33s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893350: loss 2.8373, time 6.24s
06-12 02:14 - modeling.trainer - INFO - train - iter 1893400: loss 2.8356, time 6.29s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893450: loss 2.8344, time 6.22s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893500: loss 2.8452, time 6.35s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893550: loss 2.8412, time 6.25s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893600: loss 2.8348, time 6.30s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893650: loss 2.8385, time 6.36s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893700: loss 2.8301, time 6.37s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893750: loss 2.8297, time 6.31s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893800: loss 2.8442, time 6.37s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893850: loss 2.8355, time 6.27s
06-12 02:15 - modeling.trainer - INFO - train - iter 1893900: loss 2.8398, time 6.20s
06-12 02:16 - modeling.trainer - INFO - train - iter 1893950: loss 2.8494, time 6.29s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894000: loss 2.8425, time 6.34s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894050: loss 2.8348, time 6.33s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894100: loss 2.8282, time 6.86s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894150: loss 2.8339, time 6.12s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894200: loss 2.8466, time 6.26s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894250: loss 2.8425, time 6.19s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894300: loss 2.8210, time 6.14s
06-12 02:16 - modeling.trainer - INFO - train - iter 1894350: loss 2.8276, time 6.17s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894400: loss 2.8481, time 6.18s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894450: loss 2.8477, time 6.18s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894500: loss 2.8398, time 6.24s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894550: loss 2.8363, time 6.24s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894600: loss 2.8383, time 6.16s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894650: loss 2.8383, time 6.21s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894700: loss 2.8390, time 6.15s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894750: loss 2.8350, time 6.19s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894800: loss 2.8335, time 6.11s
06-12 02:17 - modeling.trainer - INFO - train - iter 1894850: loss 2.8337, time 6.23s
06-12 02:18 - modeling.trainer - INFO - train - iter 1894900: loss 2.8300, time 6.20s
06-12 02:18 - modeling.trainer - INFO - train - iter 1894950: loss 2.8343, time 6.22s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895000: loss 2.8369, time 6.25s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895050: loss 2.8363, time 6.18s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895100: loss 2.8414, time 6.15s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895150: loss 2.8486, time 6.27s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895200: loss 2.8409, time 6.17s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895250: loss 2.8262, time 6.18s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895300: loss 2.8327, time 6.16s
06-12 02:18 - modeling.trainer - INFO - train - iter 1895350: loss 2.8381, time 6.16s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895400: loss 2.8298, time 6.12s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895450: loss 2.8251, time 6.19s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895500: loss 2.8333, time 6.10s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895550: loss 2.8382, time 6.16s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895600: loss 2.8346, time 6.17s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895650: loss 2.8383, time 6.19s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895700: loss 2.8457, time 6.13s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895750: loss 2.8372, time 6.14s
06-12 02:19 - modeling.trainer - INFO - train - iter 1895800: loss 2.8348, time 6.12s
06-12 02:20 - modeling.trainer - INFO - train - iter 1895850: loss 2.8299, time 6.87s
06-12 02:20 - modeling.trainer - INFO - train - iter 1895900: loss 2.8304, time 6.16s
06-12 02:20 - modeling.trainer - INFO - train - iter 1895950: loss 2.8377, time 6.06s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896000: loss 2.8352, time 6.17s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896050: loss 2.8340, time 6.07s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896100: loss 2.8302, time 6.11s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896150: loss 2.8379, time 6.15s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896200: loss 2.8340, time 6.05s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896250: loss 2.8340, time 6.08s
06-12 02:20 - modeling.trainer - INFO - train - iter 1896300: loss 2.8409, time 6.06s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896350: loss 2.8347, time 6.13s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896400: loss 2.8349, time 6.04s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896450: loss 2.8322, time 6.10s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896500: loss 2.8272, time 6.12s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896550: loss 2.8266, time 6.10s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896600: loss 2.8284, time 6.07s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896650: loss 2.8382, time 6.08s
06-12 02:21 - modeling.trainer - INFO - train - iter 1896700: loss 2.8349, time 6.07s