Datasets:
yimingzhang
/
allie-models

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
54
260
06-07 04:51 - modeling.trainer - INFO - train - iter 2600: loss 4.6083, time 5.65s
06-07 04:52 - modeling.trainer - INFO - train - iter 2650: loss 4.5901, time 5.61s
06-07 04:52 - modeling.trainer - INFO - train - iter 2700: loss 4.5543, time 5.66s
06-07 04:52 - modeling.trainer - INFO - train - iter 2750: loss 4.5385, time 5.62s
06-07 04:52 - modeling.trainer - INFO - train - iter 2800: loss 4.5283, time 5.59s
06-07 04:52 - modeling.trainer - INFO - train - iter 2850: loss 4.5137, time 5.62s
06-07 04:52 - modeling.trainer - INFO - train - iter 2900: loss 4.4968, time 5.66s
06-07 04:52 - modeling.trainer - INFO - train - iter 2950: loss 4.4699, time 5.62s
06-07 04:52 - modeling.trainer - INFO - train - iter 3000: loss 4.4453, time 5.66s
06-07 04:52 - modeling.trainer - INFO - train - iter 3050: loss 4.4315, time 5.63s
06-07 04:52 - modeling.trainer - INFO - train - iter 3100: loss 4.4238, time 5.61s
06-07 04:53 - modeling.trainer - INFO - train - iter 3150: loss 4.3997, time 6.48s
06-07 04:53 - modeling.trainer - INFO - train - iter 3200: loss 4.3833, time 5.59s
06-07 04:53 - modeling.trainer - INFO - train - iter 3250: loss 4.3741, time 5.59s
06-07 04:53 - modeling.trainer - INFO - train - iter 3300: loss 4.3571, time 5.57s
06-07 04:53 - modeling.trainer - INFO - train - iter 3350: loss 4.3445, time 5.61s
06-07 04:53 - modeling.trainer - INFO - train - iter 3400: loss 4.3369, time 5.58s
06-07 04:53 - modeling.trainer - INFO - train - iter 3450: loss 4.3206, time 5.63s
06-07 04:53 - modeling.trainer - INFO - train - iter 3500: loss 4.3015, time 5.61s
06-07 04:53 - modeling.trainer - INFO - train - iter 3550: loss 4.2958, time 5.57s
06-07 04:53 - modeling.trainer - INFO - train - iter 3600: loss 4.2826, time 5.59s
06-07 04:53 - modeling.trainer - INFO - train - iter 3650: loss 4.2680, time 5.58s
06-07 04:54 - modeling.trainer - INFO - train - iter 3700: loss 4.2627, time 5.55s
06-07 04:54 - modeling.trainer - INFO - train - iter 3750: loss 4.2502, time 5.54s
06-07 04:54 - modeling.trainer - INFO - train - iter 3800: loss 4.2451, time 5.56s
06-07 04:54 - modeling.trainer - INFO - train - iter 3850: loss 4.2352, time 5.55s
06-07 04:54 - modeling.trainer - INFO - train - iter 3900: loss 4.2197, time 5.57s
06-07 04:54 - modeling.trainer - INFO - train - iter 3950: loss 4.2081, time 5.56s
06-07 04:54 - modeling.trainer - INFO - train - iter 4000: loss 4.1996, time 5.61s
06-07 04:54 - modeling.trainer - INFO - train - iter 4050: loss 4.1919, time 5.52s
06-07 04:54 - modeling.trainer - INFO - train - iter 4100: loss 4.1825, time 5.53s
06-07 04:54 - modeling.trainer - INFO - train - iter 4150: loss 4.1750, time 5.56s
06-07 04:54 - modeling.trainer - INFO - train - iter 4200: loss 4.1652, time 5.53s
06-07 04:55 - modeling.trainer - INFO - train - iter 4250: loss 4.1523, time 5.55s
06-07 04:55 - modeling.trainer - INFO - train - iter 4300: loss 4.1474, time 5.54s
06-07 04:55 - modeling.trainer - INFO - train - iter 4350: loss 4.1421, time 5.52s
06-07 04:55 - modeling.trainer - INFO - train - iter 4400: loss 4.1263, time 5.52s
06-07 04:55 - modeling.trainer - INFO - train - iter 4450: loss 4.1215, time 5.55s
06-07 04:55 - modeling.trainer - INFO - train - iter 4500: loss 4.1213, time 5.53s
06-07 04:55 - modeling.trainer - INFO - train - iter 4550: loss 4.1096, time 5.51s
06-07 04:55 - modeling.trainer - INFO - train - iter 4600: loss 4.0917, time 5.52s
06-07 04:55 - modeling.trainer - INFO - train - iter 4650: loss 4.0837, time 5.49s
06-07 04:55 - modeling.trainer - INFO - train - iter 4700: loss 4.0791, time 5.55s
06-07 04:55 - modeling.trainer - INFO - train - iter 4750: loss 4.0719, time 5.51s
06-07 04:56 - modeling.trainer - INFO - train - iter 4800: loss 4.0653, time 5.48s
06-07 04:56 - modeling.trainer - INFO - train - iter 4850: loss 4.0546, time 6.08s
06-07 04:56 - modeling.trainer - INFO - train - iter 4900: loss 4.0453, time 5.49s
06-07 04:56 - modeling.trainer - INFO - train - iter 4950: loss 4.0403, time 5.47s
06-07 04:56 - modeling.trainer - INFO - train - iter 5000: loss 4.0312, time 5.50s
06-07 04:56 - modeling.trainer - INFO - train - iter 5050: loss 4.0165, time 5.57s
06-07 04:56 - modeling.trainer - INFO - train - iter 5100: loss 4.0072, time 5.51s
06-07 04:56 - modeling.trainer - INFO - train - iter 5150: loss 4.0116, time 5.49s
06-07 04:56 - modeling.trainer - INFO - train - iter 5200: loss 4.0155, time 5.53s
06-07 04:56 - modeling.trainer - INFO - train - iter 5250: loss 4.0060, time 5.47s
06-07 04:57 - modeling.trainer - INFO - train - iter 5300: loss 3.9932, time 5.48s
06-07 04:57 - modeling.trainer - INFO - train - iter 5350: loss 3.9924, time 5.50s
06-07 04:57 - modeling.trainer - INFO - train - iter 5400: loss 3.9906, time 5.50s
06-07 04:57 - modeling.trainer - INFO - train - iter 5450: loss 3.9815, time 5.49s
06-07 04:57 - modeling.trainer - INFO - train - iter 5500: loss 3.9725, time 5.44s
06-07 04:57 - modeling.trainer - INFO - train - iter 5550: loss 3.9732, time 5.44s
06-07 04:57 - modeling.trainer - INFO - train - iter 5600: loss 3.9695, time 5.47s
06-07 04:57 - modeling.trainer - INFO - train - iter 5650: loss 3.9549, time 5.45s
06-07 04:57 - modeling.trainer - INFO - train - iter 5700: loss 3.9449, time 5.45s
06-07 04:57 - modeling.trainer - INFO - train - iter 5750: loss 3.9342, time 5.43s
06-07 04:57 - modeling.trainer - INFO - train - iter 5800: loss 3.9318, time 5.46s
06-07 04:58 - modeling.trainer - INFO - train - iter 5850: loss 3.9308, time 5.44s
06-07 04:58 - modeling.trainer - INFO - train - iter 5900: loss 3.9326, time 5.43s
06-07 04:58 - modeling.trainer - INFO - train - iter 5950: loss 3.9336, time 5.45s
06-07 04:58 - modeling.trainer - INFO - train - iter 6000: loss 3.9263, time 5.48s
06-07 04:58 - modeling.trainer - INFO - train - iter 6050: loss 3.9138, time 5.46s
06-07 04:58 - modeling.trainer - INFO - train - iter 6100: loss 3.8991, time 5.44s
06-07 04:58 - modeling.trainer - INFO - train - iter 6150: loss 3.8933, time 5.48s
06-07 04:58 - modeling.trainer - INFO - train - iter 6200: loss 3.8884, time 5.47s
06-07 04:58 - modeling.trainer - INFO - train - iter 6250: loss 3.8871, time 5.43s
06-07 04:58 - modeling.trainer - INFO - train - iter 6300: loss 3.8960, time 5.45s
06-07 04:58 - modeling.trainer - INFO - train - iter 6350: loss 3.8985, time 5.44s
06-07 04:59 - modeling.trainer - INFO - train - iter 6400: loss 3.8874, time 5.41s
06-07 04:59 - modeling.trainer - INFO - train - iter 6450: loss 3.8767, time 5.43s
06-07 04:59 - modeling.trainer - INFO - train - iter 6500: loss 3.8644, time 5.44s
06-07 04:59 - modeling.trainer - INFO - train - iter 6550: loss 3.8566, time 5.52s
06-07 04:59 - modeling.trainer - INFO - train - iter 6600: loss 3.8594, time 5.42s
06-07 04:59 - modeling.trainer - INFO - train - iter 6650: loss 3.8487, time 6.07s
06-07 04:59 - modeling.trainer - INFO - train - iter 6700: loss 3.8478, time 5.47s
06-07 04:59 - modeling.trainer - INFO - train - iter 6750: loss 3.8523, time 5.42s
06-07 04:59 - modeling.trainer - INFO - train - iter 6800: loss 3.8484, time 5.42s
06-07 04:59 - modeling.trainer - INFO - train - iter 6850: loss 3.8470, time 5.43s
06-07 04:59 - modeling.trainer - INFO - train - iter 6900: loss 3.8388, time 5.39s
06-07 05:00 - modeling.trainer - INFO - train - iter 6950: loss 3.8330, time 5.42s
06-07 05:00 - modeling.trainer - INFO - train - iter 7000: loss 3.8357, time 5.41s
06-07 05:00 - modeling.trainer - INFO - train - iter 7050: loss 3.8334, time 5.40s
06-07 05:00 - modeling.trainer - INFO - train - iter 7100: loss 3.8294, time 5.43s
06-07 05:00 - modeling.trainer - INFO - train - iter 7150: loss 3.8372, time 5.42s
06-07 05:00 - modeling.trainer - INFO - train - iter 7200: loss 3.8248, time 5.41s
06-07 05:00 - modeling.trainer - INFO - train - iter 7250: loss 3.8068, time 5.44s
06-07 05:00 - modeling.trainer - INFO - train - iter 7300: loss 3.8184, time 5.44s
06-07 05:00 - modeling.trainer - INFO - train - iter 7350: loss 3.8161, time 5.42s
06-07 05:00 - modeling.trainer - INFO - train - iter 7400: loss 3.7984, time 5.41s
06-07 05:00 - modeling.trainer - INFO - train - iter 7450: loss 3.8012, time 5.42s
06-07 05:01 - modeling.trainer - INFO - train - iter 7500: loss 3.8057, time 5.36s
06-07 05:01 - modeling.trainer - INFO - train - iter 7550: loss 3.8033, time 5.42s