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2023-05-18 08:47:54
[ 6414/65536] Loss:  2.990 - Accuracy:    0.224 | LearningRate: 0.00001 | StepTime:   7.286817s - Rate:  10,604.4 Tokens/s
2023-05-18 08:48:02
[ 6415/65536] Loss:  3.013 - Accuracy:    0.232 | LearningRate: 0.00001 | StepTime:   7.277790s - Rate:  10,598.2 Tokens/s
2023-05-18 08:48:08
[ 6416/65536] Loss:  3.035 - Accuracy:    0.222 | LearningRate: 0.00001 | StepTime:   7.278083s - Rate:  10,592.0 Tokens/s
2023-05-18 08:48:16
[ 6417/65536] Loss:  3.013 - Accuracy:    0.230 | LearningRate: 0.00001 | StepTime:   7.278743s - Rate:  10,585.9 Tokens/s
2023-05-18 08:48:24
[ 6418/65536] Loss:  3.090 - Accuracy:    0.216 | LearningRate: 0.00001 | StepTime:   7.277703s - Rate:  10,579.7 Tokens/s
2023-05-18 08:48:30
[ 6419/65536] Loss:  3.005 - Accuracy:    0.230 | LearningRate: 0.00001 | StepTime:   7.278239s - Rate:  10,573.6 Tokens/s
2023-05-18 08:48:38
[ 6420/65536] Loss:  3.009 - Accuracy:    0.229 | LearningRate: 0.00001 | StepTime:   7.278024s - Rate:  10,567.4 Tokens/s
2023-05-18 08:48:46
[ 6421/65536] Loss:  3.035 - Accuracy:    0.217 | LearningRate: 0.00001 | StepTime:   7.276723s - Rate:  10,561.3 Tokens/s
2023-05-18 08:48:52
[ 6422/65536] Loss:  3.032 - Accuracy:    0.222 | LearningRate: 0.00001 | StepTime:   7.276790s - Rate:  10,555.2 Tokens/s
2023-05-18 08:49:00
[ 6423/65536] Loss:  3.055 - Accuracy:    0.214 | LearningRate: 0.00001 | StepTime:   7.277445s - Rate:  10,549.1 Tokens/s
2023-05-18 08:49:08
[ 6424/65536] Loss:  3.012 - Accuracy:    0.222 | LearningRate: 0.00001 | StepTime:   7.277080s - Rate:  10,543.0 Tokens/s
2023-05-18 08:49:14
[ 6425/65536] Loss:  2.983 - Accuracy:    0.229 | LearningRate: 0.00001 | StepTime:   7.278328s - Rate:  10,536.9 Tokens/s
2023-05-18 08:49:22
[ 6426/65536] Loss:  3.003 - Accuracy:    0.225 | LearningRate: 0.00001 | StepTime:   7.277938s - Rate:  10,530.8 Tokens/s
2023-05-18 08:49:30
[ 6427/65536] Loss:  3.048 - Accuracy:    0.223 | LearningRate: 0.00001 | StepTime:   7.287719s - Rate:  10,524.7 Tokens/s
2023-05-18 08:49:36
[ 6428/65536] Loss:  3.012 - Accuracy:    0.228 | LearningRate: 0.00001 | StepTime:   7.278033s - Rate:  10,518.7 Tokens/s
2023-05-18 08:49:44
[ 6429/65536] Loss:  3.026 - Accuracy:    0.220 | LearningRate: 0.00001 | StepTime:   7.277393s - Rate:  10,512.6 Tokens/s
2023-05-18 08:49:52
[ 6430/65536] Loss:  3.094 - Accuracy:    0.208 | LearningRate: 0.00001 | StepTime:   7.277172s - Rate:  10,506.6 Tokens/s
2023-05-18 08:49:58
[ 6431/65536] Loss:  3.031 - Accuracy:    0.216 | LearningRate: 0.00001 | StepTime:   7.278109s - Rate:  10,500.5 Tokens/s
2023-05-18 08:50:06
[ 6432/65536] Loss:  3.046 - Accuracy:    0.217 | LearningRate: 0.00001 | StepTime:   7.279235s - Rate:  10,494.5 Tokens/s
2023-05-18 08:50:14
[ 6433/65536] Loss:  3.040 - Accuracy:    0.219 | LearningRate: 0.00001 | StepTime:   7.277429s - Rate:  10,488.5 Tokens/s
2023-05-18 08:50:20
[ 6434/65536] Loss:  3.062 - Accuracy:    0.228 | LearningRate: 0.00001 | StepTime:   7.278322s - Rate:  10,482.4 Tokens/s
2023-05-18 08:50:28
[ 6435/65536] Loss:  2.983 - Accuracy:    0.236 | LearningRate: 0.00001 | StepTime:   7.277952s - Rate:  10,476.4 Tokens/s
2023-05-18 08:50:36
[ 6436/65536] Loss:  3.048 - Accuracy:    0.225 | LearningRate: 0.00001 | StepTime:   7.277529s - Rate:  10,470.5 Tokens/s
2023-05-18 08:50:42
[ 6437/65536] Loss:  2.983 - Accuracy:    0.228 | LearningRate: 0.00001 | StepTime:   7.278173s - Rate:  10,464.5 Tokens/s
2023-05-18 08:50:50
[ 6438/65536] Loss:  2.972 - Accuracy:    0.239 | LearningRate: 0.00001 | StepTime:   7.278455s - Rate:  10,458.5 Tokens/s
2023-05-18 08:50:58
[ 6439/65536] Loss:  3.000 - Accuracy:    0.228 | LearningRate: 0.00001 | StepTime:   7.277870s - Rate:  10,452.5 Tokens/s
2023-05-18 08:51:04
[ 6440/65536] Loss:  3.019 - Accuracy:    0.225 | LearningRate: 0.00001 | StepTime:   7.278481s - Rate:  10,446.6 Tokens/s
2023-05-18 08:51:12
[ 6441/65536] Loss:  2.990 - Accuracy:    0.237 | LearningRate: 0.00001 | StepTime:   7.286527s - Rate:  10,440.6 Tokens/s
2023-05-18 08:51:18
[ 6442/65536] Loss:  3.023 - Accuracy:    0.222 | LearningRate: 0.00001 | StepTime:   7.278651s - Rate:  10,434.7 Tokens/s
2023-05-18 08:51:26
[ 6443/65536] Loss:  3.027 - Accuracy:    0.215 | LearningRate: 0.00001 | StepTime:   7.277690s - Rate:  10,428.7 Tokens/s
2023-05-18 08:51:34
[ 6444/65536] Loss:  2.977 - Accuracy:    0.227 | LearningRate: 0.00001 | StepTime:   7.277789s - Rate:  10,422.8 Tokens/s
2023-05-18 08:51:40
[ 6445/65536] Loss:  3.014 - Accuracy:    0.225 | LearningRate: 0.00001 | StepTime:   7.279315s - Rate:  10,416.9 Tokens/s
2023-05-18 08:51:48
[ 6446/65536] Loss:  3.018 - Accuracy:    0.229 | LearningRate: 0.00001 | StepTime:   7.279714s - Rate:  10,411.0 Tokens/s
2023-05-18 08:51:56
[ 6447/65536] Loss:  2.978 - Accuracy:    0.232 | LearningRate: 0.00001 | StepTime:   7.279775s - Rate:  10,405.1 Tokens/s
2023-05-18 08:52:02
[ 6448/65536] Loss:  3.018 - Accuracy:    0.232 | LearningRate: 0.00001 | StepTime:   7.280842s - Rate:  10,399.2 Tokens/s
2023-05-18 08:52:10
[ 6449/65536] Loss:  3.030 - Accuracy:    0.215 | LearningRate: 0.00001 | StepTime:   7.280088s - Rate:  10,393.3 Tokens/s
2023-05-18 08:52:18
[ 6450/65536] Loss:  3.038 - Accuracy:    0.221 | LearningRate: 0.00001 | StepTime:   7.280179s - Rate:  10,387.4 Tokens/s
2023-05-18 08:52:24
[ 6451/65536] Loss:  3.058 - Accuracy:    0.215 | LearningRate: 0.00001 | StepTime:   7.285913s - Rate:  10,381.5 Tokens/s
2023-05-18 08:52:32
[ 6452/65536] Loss:  2.990 - Accuracy:    0.225 | LearningRate: 0.00001 | StepTime:   7.278055s - Rate:  10,375.7 Tokens/s
2023-05-18 08:52:40
[ 6453/65536] Loss:  3.100 - Accuracy:    0.210 | LearningRate: 0.00001 | StepTime:   7.278350s - Rate:  10,369.8 Tokens/s
2023-05-18 08:52:46
[ 6454/65536] Loss:  3.031 - Accuracy:    0.218 | LearningRate: 0.00001 | StepTime:   7.276356s - Rate:  10,364.0 Tokens/s
2023-05-18 08:52:54
[ 6455/65536] Loss:  3.069 - Accuracy:    0.219 | LearningRate: 0.00001 | StepTime:   7.276660s - Rate:  10,358.2 Tokens/s
2023-05-18 08:53:02
[ 6456/65536] Loss:  3.029 - Accuracy:    0.220 | LearningRate: 0.00001 | StepTime:   7.276938s - Rate:  10,352.3 Tokens/s
2023-05-18 08:53:08
[ 6457/65536] Loss:  2.995 - Accuracy:    0.226 | LearningRate: 0.00001 | StepTime:   7.278234s - Rate:  10,346.5 Tokens/s
2023-05-18 08:53:16
[ 6458/65536] Loss:  3.042 - Accuracy:    0.223 | LearningRate: 0.00001 | StepTime:   7.278147s - Rate:  10,340.7 Tokens/s
2023-05-18 08:53:24
[ 6459/65536] Loss:  2.996 - Accuracy:    0.224 | LearningRate: 0.00001 | StepTime:   7.278425s - Rate:  10,334.9 Tokens/s
2023-05-18 08:53:30
[ 6460/65536] Loss:  3.058 - Accuracy:    0.221 | LearningRate: 0.00001 | StepTime:   7.276512s - Rate:  10,329.1 Tokens/s
2023-05-18 08:53:38
[ 6461/65536] Loss:  3.084 - Accuracy:    0.212 | LearningRate: 0.00001 | StepTime:   7.277032s - Rate:  10,323.4 Tokens/s
2023-05-18 08:53:46
[ 6462/65536] Loss:  3.028 - Accuracy:    0.224 | LearningRate: 0.00001 | StepTime:   7.278171s - Rate:  10,317.6 Tokens/s
2023-05-18 08:53:52
[ 6463/65536] Loss:  2.990 - Accuracy:    0.232 | LearningRate: 0.00001 | StepTime:   7.277667s - Rate:  10,311.8 Tokens/s
2023-05-18 08:54:00
[ 6464/65536] Loss:  3.018 - Accuracy:    0.226 | LearningRate: 0.00001 | StepTime:   7.279086s - Rate:  10,306.0 Tokens/s
2023-05-18 08:54:08
[ 6465/65536] Loss:  3.075 - Accuracy:    0.205 | LearningRate: 0.00001 | StepTime:   7.278212s - Rate:  10,300.3 Tokens/s
2023-05-18 08:54:14
[ 6466/65536] Loss:  2.982 - Accuracy:    0.230 | LearningRate: 0.00001 | StepTime:   7.279371s - Rate:  10,294.6 Tokens/s
2023-05-18 08:54:22
[ 6467/65536] Loss:  2.993 - Accuracy:    0.223 | LearningRate: 0.00001 | StepTime:   7.277814s - Rate:  10,288.8 Tokens/s
2023-05-18 08:54:30
[ 6468/65536] Loss:  3.011 - Accuracy:    0.224 | LearningRate: 0.00001 | StepTime:   7.278476s - Rate:  10,283.1 Tokens/s
2023-05-18 08:54:36
[ 6469/65536] Loss:  3.026 - Accuracy:    0.230 | LearningRate: 0.00001 | StepTime:   7.278691s - Rate:  10,277.4 Tokens/s