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  1. https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/.gitattributes +0 -36
  2. https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/README.md +0 -11
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  17. https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot.json +0 -3
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0
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1
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2
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https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/out/out2.txt DELETED
@@ -1,4 +0,0 @@
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- cpu-bind=MASK - nlpgpu10, task 0 0 [23229]: mask 0x100000001 set
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  89%|████████▉ | 1003/1121 [9:48:41<1:06:01, 33.57s/it]
1009
  90%|████████▉ | 1004/1121 [9:49:07<1:00:40, 31.11s/it]
1010
  90%|████████▉ | 1005/1121 [9:49:29<55:10, 28.54s/it]
1011
  90%|████████▉ | 1006/1121 [9:49:56<53:37, 27.98s/it]
1012
  90%|████████▉ | 1007/1121 [9:50:20<50:37, 26.65s/it]
1013
  90%|████████▉ | 1008/1121 [9:50:49<51:52, 27.54s/it]
1014
  90%|█████████ | 1009/1121 [9:51:40<1:04:09, 34.37s/it]
1015
  90%|█████████ | 1010/1121 [9:52:24<1:08:55, 37.25s/it]
1016
  90%|█████████ | 1011/1121 [9:52:56<1:05:42, 35.84s/it]
1017
  90%|█████████ | 1012/1121 [9:53:20<58:21, 32.13s/it]
1018
  90%|█████████ | 1013/1121 [9:53:37<49:50, 27.69s/it]
1019
  90%|█████████ | 1014/1121 [9:54:05<49:23, 27.70s/it]
1020
  91%|█████████ | 1015/1121 [9:54:58<1:02:31, 35.40s/it]
1021
  91%|█████████ | 1016/1121 [9:55:21<55:14, 31.56s/it]
1022
  91%|█████████ | 1017/1121 [9:56:01<59:31, 34.34s/it]
1023
  91%|█████████ | 1018/1121 [9:56:16<48:53, 28.48s/it]
1024
  91%|█████████ | 1019/1121 [9:56:42<47:03, 27.68s/it]
1025
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1026
  91%|█████████ | 1021/1121 [9:58:15<1:01:22, 36.83s/it]
1027
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1028
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1029
  91%|█████████▏| 1024/1121 [10:00:00<55:50, 34.54s/it]
1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
  94%|█████████▍| 1056/1121 [10:19:55<33:24, 30.83s/it]
1062
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1063
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1064
  94%|█████████▍| 1059/1121 [10:21:30<32:45, 31.71s/it]
1065
  95%|█████████▍| 1060/1121 [10:21:55<30:20, 29.84s/it]
1066
  95%|█████████▍| 1061/1121 [10:22:11<25:31, 25.53s/it]
1067
  95%|█████████▍| 1062/1121 [10:22:44<27:33, 28.02s/it]
1068
  95%|█████████▍| 1063/1121 [10:23:32<32:42, 33.84s/it]
1069
  95%|█████████▍| 1064/1121 [10:24:32<39:33, 41.63s/it]
1070
  95%|█████████▌| 1065/1121 [10:25:15<39:28, 42.29s/it]
1071
  95%|█████████▌| 1066/1121 [10:25:55<38:03, 41.52s/it]
1072
  95%|█████████▌| 1067/1121 [10:26:23<33:34, 37.31s/it]
1073
  95%|█████████▌| 1068/1121 [10:27:03<33:50, 38.32s/it]
1074
  95%|█████████▌| 1069/1121 [10:27:22<28:02, 32.36s/it]
1075
  95%|█████████▌| 1070/1121 [10:28:04<29:56, 35.23s/it]
1076
  96%|█████████▌| 1071/1121 [10:28:59<34:17, 41.16s/it]
1077
  96%|█████████▌| 1072/1121 [10:29:12<26:42, 32.70s/it]
1078
  96%|█████████▌| 1073/1121 [10:29:34<23:34, 29.47s/it]
1079
  96%|█████████▌| 1074/1121 [10:30:21<27:17, 34.84s/it]
1080
  96%|█████████▌| 1075/1121 [10:31:22<32:40, 42.61s/it]
1081
  96%|█████████▌| 1076/1121 [10:31:54<29:42, 39.62s/it]
1082
  96%|█████████▌| 1077/1121 [10:32:22<26:30, 36.15s/it]
1083
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1084
  96%|█████████▋| 1079/1121 [10:34:08<30:36, 43.72s/it]
1085
  96%|█████████▋| 1080/1121 [10:34:30<25:32, 37.38s/it]
1086
  96%|█████████▋| 1081/1121 [10:35:03<23:53, 35.83s/it]
1087
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1088
  97%|█████████▋| 1083/1121 [10:36:37<26:54, 42.47s/it]
1089
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1090
  97%|█████████▋| 1085/1121 [10:38:37<30:45, 51.26s/it]
1091
  97%|█████████▋| 1086/1121 [10:39:10<26:38, 45.68s/it]
1092
  97%|█████████▋| 1087/1121 [10:39:39<23:09, 40.87s/it]
1093
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1094
  97%|█████████▋| 1089/1121 [10:40:39<19:18, 36.19s/it]
1095
  97%|█████████▋| 1090/1121 [10:41:18<19:04, 36.91s/it]
1096
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1097
  97%|█████████▋| 1092/1121 [10:42:27<17:21, 35.93s/it]
1098
  98%|█████████▊| 1093/1121 [10:42:53<15:17, 32.77s/it]
1099
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1100
  98%|█████████▊| 1095/1121 [10:44:28<17:30, 40.39s/it]
1101
  98%|█████████▊| 1096/1121 [10:44:59<15:36, 37.48s/it]
1102
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1103
  98%|█████████▊| 1098/1121 [10:46:32<16:28, 42.99s/it]
1104
  98%|█████████▊| 1099/1121 [10:46:49<12:56, 35.29s/it]
1105
  98%|█████████▊| 1100/1121 [10:47:13<11:09, 31.87s/it]
1106
  98%|█████████▊| 1101/1121 [10:47:29<09:00, 27.01s/it]
1107
  98%|█████████▊| 1102/1121 [10:47:41<07:08, 22.55s/it]
1108
  98%|█████████▊| 1103/1121 [10:48:09<07:15, 24.17s/it]
1109
  98%|█████████▊| 1104/1121 [10:48:46<07:54, 27.91s/it]
1110
  99%|█████████▊| 1105/1121 [10:49:18<07:45, 29.11s/it]
1111
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1112
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1113
  99%|█████████▉| 1108/1121 [10:51:32<07:57, 36.76s/it]
1114
  99%|█████████▉| 1109/1121 [10:52:04<07:02, 35.23s/it]
1115
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1116
  99%|█████████▉| 1111/1121 [10:52:52<04:51, 29.14s/it]
1117
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1118
  99%|█████████▉| 1113/1121 [10:53:59<04:14, 31.85s/it]
1119
  99%|█████████▉| 1114/1121 [10:54:49<04:20, 37.19s/it]
1120
  99%|█████████▉| 1115/1121 [10:55:38<04:04, 40.77s/it]
 
 
 
 
 
0
  0%| | 0/1121 [00:00<?, ?it/s]
1
  0%| | 1/1121 [00:39<12:23:56, 39.85s/it]
2
  0%| | 2/1121 [01:06<9:57:52, 32.06s/it]
3
  0%| | 3/1121 [01:49<11:31:43, 37.12s/it]
4
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5
  0%| | 5/1121 [02:49<9:47:55, 31.61s/it]
6
  1%| | 6/1121 [03:12<8:51:51, 28.62s/it]
7
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8
  1%| | 8/1121 [04:43<11:20:54, 36.71s/it]
9
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10
  1%| | 10/1121 [06:27<13:54:11, 45.05s/it]
11
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12
  1%| | 12/1121 [07:23<10:59:44, 35.69s/it]
13
  1%| | 13/1121 [08:05<11:38:30, 37.83s/it]
14
  1%| | 14/1121 [08:38<11:08:41, 36.24s/it]
15
  1%|▏ | 15/1121 [09:21<11:43:27, 38.16s/it]
16
  1%|▏ | 16/1121 [09:43<10:13:36, 33.32s/it]
17
  2%|▏ | 17/1121 [10:34<11:51:20, 38.66s/it]
18
  2%|▏ | 18/1121 [11:34<13:49:16, 45.11s/it]
19
  2%|▏ | 19/1121 [12:00<12:04:16, 39.43s/it]
20
  2%|▏ | 20/1121 [12:43<12:25:42, 40.64s/it]
21
  2%|▏ | 21/1121 [13:02<10:25:25, 34.11s/it]
22
  2%|▏ | 22/1121 [13:27<9:31:55, 31.22s/it]
23
  2%|▏ | 23/1121 [14:29<12:22:50, 40.59s/it]
24
  2%|▏ | 24/1121 [15:09<12:15:30, 40.23s/it]
25
  2%|▏ | 25/1121 [15:29<10:26:27, 34.30s/it]
26
  2%|▏ | 26/1121 [16:04<10:28:00, 34.41s/it]
27
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28
  2%|▏ | 28/1121 [17:07<9:36:13, 31.63s/it]
29
  3%|▎ | 29/1121 [17:29<8:38:48, 28.51s/it]
30
  3%|▎ | 30/1121 [18:15<10:18:28, 34.01s/it]
31
  3%|▎ | 31/1121 [19:03<11:32:17, 38.11s/it]
32
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33
  3%|▎ | 33/1121 [20:13<11:19:17, 37.46s/it]
34
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35
  3%|▎ | 35/1121 [21:55<13:29:10, 44.71s/it]
36
  3%|▎ | 36/1121 [22:40<13:34:49, 45.06s/it]
37
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38
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39
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40
  4%|▎ | 40/1121 [25:42<13:40:24, 45.54s/it]
41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
  5%|▍ | 54/1121 [32:23<8:25:10, 28.41s/it]
55
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56
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57
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58
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59
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60
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61
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62
  6%|▌ | 62/1121 [37:15<10:02:31, 34.14s/it]
63
  6%|▌ | 63/1121 [37:35<8:50:58, 30.11s/it]
64
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65
  6%|▌ | 65/1121 [38:18<7:39:51, 26.13s/it]
66
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67
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68
  6%|▌ | 68/1121 [40:04<9:11:36, 31.43s/it]
69
  6%|▌ | 69/1121 [40:38<9:25:03, 32.23s/it]
70
  6%|▌ | 70/1121 [40:58<8:18:49, 28.48s/it]
71
  6%|▋ | 71/1121 [42:00<11:13:43, 38.50s/it]
72
  6%|▋ | 72/1121 [42:35<10:56:34, 37.55s/it]
73
  7%|▋ | 73/1121 [43:00<9:51:48, 33.88s/it]
74
  7%|▋ | 74/1121 [44:00<12:05:36, 41.58s/it]
75
  7%|▋ | 75/1121 [44:45<12:23:54, 42.67s/it]
76
  7%|▋ | 76/1121 [45:40<13:23:56, 46.16s/it]
77
  7%|▋ | 77/1121 [46:20<12:55:49, 44.59s/it]
78
  7%|▋ | 78/1121 [46:39<10:38:56, 36.76s/it]
79
  7%|▋ | 79/1121 [46:52<8:35:40, 29.69s/it]
80
  7%|▋ | 80/1121 [47:32<9:27:19, 32.70s/it]
81
  7%|▋ | 81/1121 [47:50<8:09:24, 28.24s/it]
82
  7%|▋ | 82/1121 [48:40<10:04:26, 34.91s/it]
83
  7%|▋ | 83/1121 [49:21<10:34:32, 36.68s/it]
84
  7%|▋ | 84/1121 [50:06<11:16:09, 39.12s/it]
85
  8%|▊ | 85/1121 [50:39<10:44:27, 37.32s/it]
86
  8%|▊ | 86/1121 [51:13<10:25:22, 36.25s/it]
87
  8%|▊ | 87/1121 [51:48<10:20:34, 36.01s/it]
88
  8%|▊ | 88/1121 [52:08<8:57:25, 31.22s/it]
89
  8%|▊ | 89/1121 [52:42<9:12:52, 32.14s/it]
90
  8%|▊ | 90/1121 [53:28<10:18:52, 36.02s/it]
91
  8%|▊ | 91/1121 [54:07<10:37:46, 37.15s/it]
92
  8%|▊ | 92/1121 [54:29<9:18:51, 32.59s/it]
93
  8%|▊ | 93/1121 [55:00<9:10:02, 32.10s/it]
94
  8%|▊ | 94/1121 [55:48<10:27:29, 36.66s/it]
95
  8%|▊ | 95/1121 [56:15<9:38:40, 33.84s/it]
96
  9%|▊ | 96/1121 [56:34<8:21:39, 29.37s/it]
97
  9%|▊ | 97/1121 [57:16<9:28:54, 33.33s/it]
98
  9%|▊ | 98/1121 [58:16<11:40:54, 41.11s/it]
99
  9%|▉ | 99/1121 [59:05<12:23:40, 43.66s/it]
100
  9%|▉ | 100/1121 [59:49<12:22:20, 43.62s/it]
101
  9%|▉ | 101/1121 [1:00:32<12:18:40, 43.45s/it]
102
  9%|▉ | 102/1121 [1:01:27<13:19:58, 47.10s/it]
103
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https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/out/out3.txt DELETED
@@ -1,4 +0,0 @@
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  97%|█████████▋| 1089/1121 [11:03:31<19:34, 36.70s/it]
1095
  97%|█████████▋| 1090/1121 [11:04:10<19:19, 37.39s/it]
1096
  97%|█████████▋| 1091/1121 [11:04:44<18:08, 36.29s/it]
1097
  97%|█████████▋| 1092/1121 [11:05:20<17:38, 36.49s/it]
1098
  98%|█████████▊| 1093/1121 [11:05:46<15:31, 33.28s/it]
1099
  98%|█████████▊| 1094/1121 [11:06:36<17:09, 38.14s/it]
1100
  98%|█████████▊| 1095/1121 [11:07:23<17:41, 40.84s/it]
1101
  98%|█████████▊| 1096/1121 [11:07:54<15:50, 38.02s/it]
1102
  98%|█████████▊| 1097/1121 [11:08:32<15:07, 37.82s/it]
1103
  98%|█████████▊| 1098/1121 [11:09:29<16:44, 43.66s/it]
1104
  98%|█████████▊| 1099/1121 [11:09:47<13:08, 35.86s/it]
1105
  98%|█████████▊| 1100/1121 [11:10:11<11:18, 32.30s/it]
1106
  98%|█████████▊| 1101/1121 [11:10:27<09:07, 27.40s/it]
1107
  98%|█████████▊| 1102/1121 [11:10:39<07:15, 22.91s/it]
1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
  99%|█████████▉| 1111/1121 [11:15:54<04:53, 29.38s/it]
1117
  99%|█████████▉| 1112/1121 [11:16:23<04:23, 29.27s/it]
1118
  99%|█████████▉| 1113/1121 [11:17:02<04:17, 32.20s/it]
1119
  99%|█████████▉| 1114/1121 [11:17:52<04:24, 37.76s/it]
1120
  99%|█████████▉| 1115/1121 [11:18:42<04:07, 41.29s/it]
 
 
 
 
 
0
  0%| | 0/1121 [00:00<?, ?it/s]
1
  0%| | 1/1121 [00:40<12:41:04, 40.77s/it]
2
  0%| | 2/1121 [01:07<10:11:12, 32.77s/it]
3
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4
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
  2%|▏ | 17/1121 [10:57<12:17:45, 40.10s/it]
18
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19
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20
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21
  2%|▏ | 21/1121 [13:33<10:51:42, 35.55s/it]
22
  2%|▏ | 22/1121 [13:57<9:51:13, 32.28s/it]
23
  2%|▏ | 23/1121 [15:02<12:51:26, 42.16s/it]
24
  2%|▏ | 24/1121 [15:44<12:47:05, 41.96s/it]
25
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26
  2%|▏ | 26/1121 [16:42<10:58:21, 36.07s/it]
27
  2%|▏ | 27/1121 [17:33<12:18:03, 40.48s/it]
28
  2%|▏ | 28/1121 [17:49<10:07:29, 33.35s/it]
29
  3%|▎ | 29/1121 [18:11<9:04:42, 29.93s/it]
30
  3%|▎ | 30/1121 [19:01<10:50:19, 35.77s/it]
31
  3%|▎ | 31/1121 [19:52<12:11:30, 40.27s/it]
32
  3%|▎ | 32/1121 [20:19<11:02:09, 36.48s/it]
33
  3%|▎ | 33/1121 [21:05<11:52:49, 39.31s/it]
34
  3%|▎ | 34/1121 [21:50<12:24:27, 41.09s/it]
35
  3%|▎ | 35/1121 [22:48<13:51:16, 45.93s/it]
36
  3%|▎ | 36/1121 [23:34<13:55:35, 46.21s/it]
37
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38
  3%|▎ | 38/1121 [25:19<14:38:47, 48.69s/it]
39
  3%|▎ | 39/1121 [25:40<12:09:11, 40.44s/it]
40
  4%|▎ | 40/1121 [26:44<14:11:50, 47.28s/it]
41
  4%|▎ | 41/1121 [27:06<11:55:08, 39.73s/it]
42
  4%|▎ | 42/1121 [27:37<11:07:40, 37.13s/it]
43
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44
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45
  4%|▍ | 45/1121 [29:13<10:29:02, 35.08s/it]
46
  4%|▍ | 46/1121 [29:36<9:22:44, 31.41s/it]
47
  4%|▍ | 47/1121 [30:08<9:27:18, 31.69s/it]
48
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49
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50
  4%|▍ | 50/1121 [31:55<11:16:30, 37.90s/it]
51
  5%|▍ | 51/1121 [32:15<9:42:17, 32.65s/it]
52
  5%|▍ | 52/1121 [32:39<8:55:09, 30.04s/it]
53
  5%|▍ | 53/1121 [33:23<10:10:01, 34.27s/it]
54
  5%|▍ | 54/1121 [33:42<8:43:51, 29.46s/it]
55
  5%|▍ | 55/1121 [34:07<8:21:53, 28.25s/it]
56
  5%|▍ | 56/1121 [34:53<9:57:31, 33.66s/it]
57
  5%|▌ | 57/1121 [35:40<11:05:39, 37.54s/it]
58
  5%|▌ | 58/1121 [36:14<10:45:05, 36.41s/it]
59
  5%|▌ | 59/1121 [36:46<10:21:04, 35.09s/it]
60
  5%|▌ | 60/1121 [37:36<11:40:14, 39.60s/it]
61
  5%|▌ | 61/1121 [38:24<12:25:49, 42.22s/it]
62
  6%|▌ | 62/1121 [38:43<10:22:22, 35.26s/it]
63
  6%|▌ | 63/1121 [39:04<9:07:33, 31.05s/it]
64
  6%|▌ | 64/1121 [39:20<7:46:47, 26.50s/it]
65
  6%|▌ | 65/1121 [39:47<7:50:26, 26.73s/it]
66
  6%|▌ | 66/1121 [40:34<9:33:52, 32.64s/it]
67
  6%|▌ | 67/1121 [41:02<9:11:08, 31.37s/it]
68
  6%|▌ | 68/1121 [41:37<9:25:42, 32.23s/it]
69
  6%|▌ | 69/1121 [42:12<9:43:19, 33.27s/it]
70
  6%|▌ | 70/1121 [42:32<8:33:34, 29.32s/it]
71
  6%|▋ | 71/1121 [43:37<11:36:02, 39.77s/it]
72
  6%|▋ | 72/1121 [44:13<11:15:49, 38.66s/it]
73
  7%|▋ | 73/1121 [44:39<10:10:14, 34.94s/it]
74
  7%|▋ | 74/1121 [45:40<12:24:38, 42.67s/it]
75
  7%|▋ | 75/1121 [46:27<12:47:20, 44.02s/it]
76
  7%|▋ | 76/1121 [47:23<13:51:59, 47.77s/it]
77
  7%|▋ | 77/1121 [48:06<13:27:19, 46.40s/it]
78
  7%|▋ | 78/1121 [48:26<11:05:04, 38.26s/it]
79
  7%|▋ | 79/1121 [48:40<8:57:03, 30.92s/it]
80
  7%|▋ | 80/1121 [49:21<9:51:09, 34.07s/it]
81
  7%|▋ | 81/1121 [49:39<8:29:42, 29.41s/it]
82
  7%|▋ | 82/1121 [50:32<10:31:46, 36.48s/it]
83
  7%|▋ | 83/1121 [51:15<11:02:50, 38.31s/it]
84
  7%|▋ | 84/1121 [52:03<11:49:39, 41.06s/it]
85
  8%|▊ | 85/1121 [52:37<11:13:34, 39.01s/it]
86
  8%|▊ | 86/1121 [53:12<10:54:16, 37.93s/it]
87
  8%|▊ | 87/1121 [53:49<10:47:46, 37.59s/it]
88
  8%|▊ | 88/1121 [54:10<9:21:47, 32.63s/it]
89
  8%|▊ | 89/1121 [54:46<9:38:56, 33.66s/it]
90
  8%|▊ | 90/1121 [55:33<10:48:17, 37.73s/it]
91
  8%|▊ | 91/1121 [56:15<11:10:07, 39.04s/it]
92
  8%|▊ | 92/1121 [56:38<9:46:49, 34.22s/it]
93
  8%|▊ | 93/1121 [57:14<9:51:14, 34.51s/it]
94
  8%|▊ | 94/1121 [58:03<11:07:02, 38.97s/it]
95
  8%|▊ | 95/1121 [58:32<10:13:30, 35.88s/it]
96
  9%|▊ | 96/1121 [58:52<8:54:53, 31.31s/it]
97
  9%|▊ | 97/1121 [59:33<9:42:14, 34.12s/it]
98
  9%|▊ | 98/1121 [1:00:35<12:02:38, 42.38s/it]
99
  9%|▉ | 99/1121 [1:01:25<12:45:20, 44.93s/it]
100
  9%|▉ | 100/1121 [1:02:11<12:48:55, 45.19s/it]
101
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102
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103
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104
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105
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106
  9%|▉ | 106/1121 [1:06:11<9:23:30, 33.31s/it]
107
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108
  10%|▉ | 108/1121 [1:07:28<9:45:23, 34.67s/it]
109
  10%|▉ | 109/1121 [1:07:57<9:12:41, 32.77s/it]
110
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111
  10%|▉ | 111/1121 [1:09:26<10:44:09, 38.27s/it]
112
  10%|▉ | 112/1121 [1:10:32<13:06:27, 46.77s/it]
113
  10%|█ | 113/1121 [1:11:33<14:18:34, 51.11s/it]
114
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115
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116
  10%|█ | 116/1121 [1:12:48<10:00:58, 35.88s/it]
117
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118
  11%|█ | 118/1121 [1:13:28<7:33:49, 27.15s/it]
119
  11%|█ | 119/1121 [1:14:21<9:43:26, 34.94s/it]
120
  11%|█ | 120/1121 [1:14:48<9:04:35, 32.64s/it]
121
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122
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123
  11%|█ | 123/1121 [1:16:35<9:32:24, 34.41s/it]
124
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125
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126
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127
  11%|█▏ | 127/1121 [1:19:12<9:44:39, 35.29s/it]
128
  11%|█▏ | 128/1121 [1:20:16<12:03:40, 43.73s/it]
129
  12%|█▏ | 129/1121 [1:20:58<11:56:13, 43.32s/it]
130
  12%|█▏ | 130/1121 [1:21:26<10:38:23, 38.65s/it]
131
  12%|█▏ | 131/1121 [1:22:13<11:22:21, 41.35s/it]
132
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133
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134
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135
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136
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137
  12%|█▏ | 137/1121 [1:26:19<11:32:39, 42.24s/it]
138
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139
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140
  12%|█▏ | 140/1121 [1:28:18<11:05:04, 40.68s/it]
141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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180
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181
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182
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183
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184
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185
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https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/out/out4.txt DELETED
@@ -1,4 +0,0 @@
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- cpu-bind=MASK - nlpgpu10, task 0 0 [56119]: mask 0x100000001 set
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0
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96
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https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/out/out5.txt DELETED
@@ -1,4 +0,0 @@
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  97%|█████████▋| 1088/1121 [10:43:55<19:14, 34.99s/it]
1094
  97%|█████████▋| 1089/1121 [10:44:35<19:29, 36.55s/it]
1095
  97%|█████████▋| 1090/1121 [10:45:14<19:14, 37.24s/it]
1096
  97%|█████████▋| 1091/1121 [10:45:47<18:01, 36.06s/it]
1097
  97%|█████████▋| 1092/1121 [10:46:24<17:32, 36.29s/it]
1098
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1099
  98%|█████████▊| 1094/1121 [10:47:38<17:02, 37.86s/it]
1100
  98%|█████████▊| 1095/1121 [10:48:26<17:40, 40.78s/it]
1101
  98%|█████████▊| 1096/1121 [10:48:56<15:42, 37.68s/it]
1102
  98%|█████████▊| 1097/1121 [10:49:33<14:57, 37.40s/it]
1103
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1104
  98%|█████████▊| 1099/1121 [10:50:47<12:59, 35.45s/it]
1105
  98%|█████████▊| 1100/1121 [10:51:12<11:15, 32.14s/it]
1106
  98%|█████████▊| 1101/1121 [10:51:28<09:05, 27.26s/it]
1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
  99%|█████████▉| 1111/1121 [10:56:54<04:54, 29.42s/it]
1117
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1118
  99%|█████████▉| 1113/1121 [10:58:01<04:15, 31.94s/it]
1119
  99%|█████████▉| 1114/1121 [10:58:50<04:20, 37.25s/it]
1120
  99%|█████████▉| 1115/1121 [10:59:40<04:05, 40.88s/it]
 
 
 
 
 
0
  0%| | 0/1121 [00:00<?, ?it/s]
1
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2
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3
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4
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
  2%|▏ | 17/1121 [10:45<12:00:18, 39.15s/it]
18
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19
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20
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21
  2%|▏ | 21/1121 [13:17<10:36:29, 34.72s/it]
22
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23
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24
  2%|▏ | 24/1121 [15:25<12:30:20, 41.04s/it]
25
  2%|▏ | 25/1121 [15:46<10:39:39, 35.02s/it]
26
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27
  2%|▏ | 27/1121 [17:11<11:58:14, 39.39s/it]
28
  2%|▏ | 28/1121 [17:27<9:51:01, 32.44s/it]
29
  3%|▎ | 29/1121 [17:49<8:52:09, 29.24s/it]
30
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31
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32
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33
  3%|▎ | 33/1121 [20:37<11:33:48, 38.26s/it]
34
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35
  3%|▎ | 35/1121 [22:20<13:41:53, 45.41s/it]
36
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37
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38
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39
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40
  4%|▎ | 40/1121 [26:12<13:57:17, 46.47s/it]
41
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42
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43
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44
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45
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46
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47
  4%|▍ | 47/1121 [29:33<9:16:07, 31.07s/it]
48
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49
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50
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51
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52
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53
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54
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55
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56
  5%|▍ | 56/1121 [34:14<9:50:51, 33.29s/it]
57
  5%|▌ | 57/1121 [34:59<10:55:27, 36.96s/it]
58
  5%|▌ | 58/1121 [35:33<10:38:31, 36.04s/it]
59
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60
  5%|▌ | 60/1121 [36:55<11:33:24, 39.21s/it]
61
  5%|▌ | 61/1121 [37:43<12:21:47, 41.99s/it]
62
  6%|▌ | 62/1121 [38:02<10:19:03, 35.07s/it]
63
  6%|▌ | 63/1121 [38:23<9:02:35, 30.77s/it]
64
  6%|▌ | 64/1121 [38:39<7:42:51, 26.27s/it]
65
  6%|▌ | 65/1121 [39:06<7:46:04, 26.48s/it]
66
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67
  6%|▌ | 67/1121 [40:18<9:00:22, 30.76s/it]
68
  6%|▌ | 68/1121 [40:52<9:16:04, 31.69s/it]
69
  6%|▌ | 69/1121 [41:27<9:30:14, 32.52s/it]
70
  6%|▌ | 70/1121 [41:47<8:25:03, 28.83s/it]
71
  6%|▋ | 71/1121 [42:50<11:24:36, 39.12s/it]
72
  6%|▋ | 72/1121 [43:26<11:05:04, 38.04s/it]
73
  7%|▋ | 73/1121 [43:51<9:57:11, 34.19s/it]
74
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75
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76
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77
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78
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79
  7%|▋ | 79/1121 [47:46<8:43:38, 30.15s/it]
80
  7%|▋ | 80/1121 [48:26<9:37:04, 33.26s/it]
81
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82
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83
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84
  7%|▋ | 84/1121 [51:04<11:31:06, 39.99s/it]
85
  8%|▊ | 85/1121 [51:37<10:58:10, 38.12s/it]
86
  8%|▊ | 86/1121 [52:12<10:39:40, 37.08s/it]
87
  8%|▊ | 87/1121 [52:49<10:36:44, 36.95s/it]
88
  8%|▊ | 88/1121 [53:09<9:09:48, 31.93s/it]
89
  8%|▊ | 89/1121 [53:44<9:26:36, 32.94s/it]
90
  8%|▊ | 90/1121 [54:31<10:34:57, 36.95s/it]
91
  8%|▊ | 91/1121 [55:12<10:55:15, 38.17s/it]
92
  8%|▊ | 92/1121 [55:34<9:34:25, 33.49s/it]
93
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94
  8%|▊ | 94/1121 [56:54<10:43:08, 37.57s/it]
95
  8%|▊ | 95/1121 [57:22<9:53:08, 34.69s/it]
96
  9%|▊ | 96/1121 [57:41<8:33:44, 30.07s/it]
97
  9%|▊ | 97/1121 [58:22<9:25:30, 33.14s/it]
98
  9%|▊ | 98/1121 [59:22<11:45:20, 41.37s/it]
99
  9%|▉ | 99/1121 [1:00:12<12:24:51, 43.73s/it]
100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
  10%|▉ | 112/1121 [1:09:03<12:45:04, 45.49s/it]
113
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114
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115
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116
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117
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118
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119
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120
  11%|█ | 120/1121 [1:13:15<8:55:46, 32.11s/it]
121
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122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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140
  12%|█▏ | 140/1121 [1:26:30<10:45:50, 39.50s/it]
141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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170
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171
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172
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173
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174
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175
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177
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178
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https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/run.sh DELETED
@@ -1,17 +0,0 @@
1
- #!/bin/bash
2
- #SBATCH --job-name=event
3
- #SBATCH --output=/shared/xdyu/msr/reasoning_eval/out/out.txt
4
- #SBATCH --partition=p_nlp
5
- #SBATCH --constraint=48GBgpu
6
- #SBATCH --nodes=1
7
- #SBATCH --gpus=4
8
- #SBATCH --cpus-per-task=1
9
- #SBATCH --mem=200GB
10
-
11
- cd /shared/xdyu/msr/reasoning_eval
12
-
13
- export HF_HOME="/shared/.cache/transformers"
14
- export PYTHONPATH="/shared/xdyu/msr/reasoning_eval"
15
-
16
- python run_program.py
17
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
https:/huggingface.co/xdy-reasoning-project/reasoning_eval/tree/main/run_program.py DELETED
@@ -1,446 +0,0 @@
1
- import json
2
- import random
3
- import csv
4
- from io import StringIO
5
- from contextlib import redirect_stdout
6
- from openai import OpenAI
7
- from tqdm import tqdm
8
- import os
9
- import re
10
- import anthropic
11
- import math
12
- from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
13
- import torch
14
-
15
-
16
- claude_client = anthropic.Anthropic(
17
- # defaults to os.environ.get("ANTHROPIC_API_KEY")
18
- api_key="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",
19
- )
20
-
21
-
22
- client = OpenAI(
23
- organization='org-GOLjWTjRYGCBi47VKNk0jhMm',
24
- project='proj_YotrLJgFnsq9wXOXBYaLjnO4',
25
- api_key='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'
26
- )
27
-
28
-
29
- torch.random.manual_seed(0)
30
-
31
- # phi_model = AutoModelForCausalLM.from_pretrained(
32
- # "microsoft/Phi-3.5-MoE-instruct",
33
- # device_map="cuda",
34
- # torch_dtype="auto",
35
- # trust_remote_code=True,
36
- # )
37
- #
38
- # phi_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
39
-
40
- # qwen_model_name = "Qwen/Qwen2.5-7B-Instruct"
41
- #
42
- # qwen_model = AutoModelForCausalLM.from_pretrained(
43
- # qwen_model_name,
44
- # torch_dtype="auto",
45
- # device_map="auto"
46
- # )
47
- # qwen_tokenizer = AutoTokenizer.from_pretrained(qwen_model_name)
48
-
49
- llama_pipeline = pipeline(
50
- "text-generation",
51
- model="meta-llama/Meta-Llama-3.1-8B-Instruct",
52
- model_kwargs={"torch_dtype": torch.bfloat16},
53
- device_map="auto",
54
- )
55
-
56
-
57
-
58
-
59
- program_data = []
60
-
61
- PROMPT_DICT = {
62
- "prompt_new_parameter_value": (
63
- "Here is a math question with the parameter and parameter values. Please perturb the value of parameters into different values. Output five kinds of new values in the same format as the given parameters in five lines without index.\n\n"
64
- "Question:\n{question}\n\nParameters:\n{parameters}\n\n"
65
- ),
66
- "prompt_rewrite_question": (
67
- "Here is a math question with old parameter values, and five kinds of new parameter values. Please rewrite the question five times to update all the parameters from old value to each corresponding new value in five lines without index.\n\n"
68
- "Question: :\n{question}\n\nOld Parameters:\n{parameters}\n\nNew Parameters:\n{new_parameters}\n\nNew Question:"
69
- ),
70
- "prompt_answer_question": (
71
- "Answer the math question below. Only output the answer without units and any context words.\n\n"
72
- "Question:\n{question}\n\nAnswer:"
73
- ),
74
- "prompt_answer_question_few_shot_cot": (
75
- "Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\n"
76
- "A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer is 6.\n\n"
77
- "Q: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n"
78
- "A: There are originally 3 cars. 2 more cars arrive. 3 + 2 = 5. The answer is 5.\n\n"
79
- "Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n"
80
- "A: Originally, Leah had 32 chocolates. Her sister had 42. So in total they had 32 + 42 = 74. After eating 35, they had 74 - 35 = 39. The answer is 39.\n\n"
81
- "Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n"
82
- "A: Jason started with 20 lollipops. Then he had 12 after giving some to Denny. So he gave Denny 20 - 12 = 8. The answer is 8.\n\n"
83
- "Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n"
84
- "A: Shawn started with 5 toys. If he got 2 toys each from his mom and dad, then that is 4 more toys. 5 + 4 = 9. The answer is 9.\n\n"
85
- "Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\n"
86
- "A: There were originally 9 computers. For each of 4 days, 5 more computers were added. So 5 * 4 = 20 computers were added. 9 + 20 is 29. The answer is 29.\n\n"
87
- "Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n"
88
- "A: Michael started with 58 golf balls. After losing 23 on tuesday, he had 58 - 23 = 35. After losing 2 more, he had 35 - 2 = 33 golf balls. The answer is 33.\n\n"
89
- "Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n"
90
- "A: Olivia had 23 dollars. 5 bagels for 3 dollars each will be 5 x 3 = 15 dollars. So she has 23 - 15 dollars left. 23 - 15 is 8. The answer is 8.\n\n"
91
- "Q: {question}\n"
92
- "A: "
93
- )
94
- }
95
-
96
- def call_function_with_args(parameter):
97
- # outstr = '\noutput = answer('
98
- # for parameter in case['parameter_value']:
99
- # para_name = parameter.split(':')[0]
100
- # if ': str' not in parameter:
101
- # value = str(case['parameter_value'][parameter])
102
- # else:
103
- # value = '"' + case['parameter_value'][parameter] + '"'
104
- # # print(para_name, value)
105
- # outstr += para_name + '=' + value + ', '
106
- # outstr += ')\nprint(output)'
107
-
108
- outstr = '\noutput = answer({})\nprint(output)'.format(parameter)
109
- return outstr
110
-
111
-
112
- def perturb_parameter_value(case):
113
- prompt_template = PROMPT_DICT['prompt_new_parameter_value']
114
- prompt = prompt_template.format_map(
115
- {"question": case['question'], "parameters": case['parameters']}
116
- )
117
- response = client.chat.completions.create(
118
- model="gpt-4o",
119
- messages=[
120
- {"role": "system", "content": "You are a helpful assistant."},
121
- {"role": "user", "content": prompt}
122
- ]
123
- )
124
- return response
125
-
126
-
127
- def clear_parameter_output(response):
128
- response = response.replace('Parameters:', '').replace('```', '').replace('- ', '').strip().split('\n')
129
- for idx, para in enumerate(response):
130
- para.replace(str(idx)+'. ', '').strip()
131
- para.replace(str(idx+1) + '. ', '').strip()
132
- response = [x for x in response if len(x) > 0]
133
- return response
134
-
135
- def generate_new_parameter_value():
136
- # infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
137
- infile = open('/shared/xdyu/msr/reasoning_eval/data/math/test_dump_math_train_4o_perturbed.json', 'r')
138
- program_data = json.load(infile)
139
-
140
- correct_counter = 0
141
- compile_fail_counter = 0
142
-
143
- correct_case = []
144
-
145
- for idx, case in enumerate(program_data):
146
- if idx in [306]:
147
- continue
148
- # program = case['candidate_programs'][0]
149
- if len(case['selected_programs']) == 0:
150
- continue
151
- program = case['selected_programs'][0]
152
- if 'math.' in program:
153
- program = 'import math\n\n' + program
154
- program += call_function_with_args(case['parameters'])
155
- # print(program)
156
- try:
157
- f = StringIO()
158
- with redirect_stdout(f):
159
- exec(program)
160
- s = f.getvalue().strip()
161
- if round(float(s)) == round(float(case['answer'])):
162
- correct_counter += 1
163
- correct_case.append(case)
164
- # else:
165
- # print(s)
166
- # print(case['answer'])
167
- except Exception as e:
168
- print(e)
169
- print(case)
170
- compile_fail_counter += 1
171
- print(len(program_data))
172
- print(correct_counter)
173
-
174
- dead_loop_counter = 0
175
- for case in tqdm(correct_case):
176
- if 'new_answers' in case and len(case['new_answers']) == 5:
177
- continue
178
- if 'new_parameters' not in case or ('new_parameters' in case and 'new_answers' in case and len(case['new_answers']) < 5):
179
- response = perturb_parameter_value(case)
180
- new_values = clear_parameter_output(response.choices[0].message.content)
181
- if len(new_values) == 6:
182
- new_values = new_values[1:]
183
- case['new_parameters'] = new_values
184
-
185
- try:
186
- case['new_answers'] = []
187
- for parameter in case['new_parameters']:
188
- program = case['candidate_programs'][0]
189
- program += call_function_with_args(parameter)
190
- f = StringIO()
191
- if 'while True:' in program:
192
- dead_loop_counter += 1
193
- continue
194
- with redirect_stdout(f):
195
- exec(program)
196
- s = f.getvalue().strip()
197
- case['new_answers'].append(s)
198
- except Exception as e:
199
- print(e)
200
- print(case['new_parameters'])
201
- continue
202
- # break
203
- outfile = open('data/math/test_dump_math_train_4o_perturbed.json', 'w')
204
- json.dump(correct_case, outfile, indent=4)
205
-
206
- print(dead_loop_counter)
207
-
208
- def rewrite_question(case):
209
- prompt_template = PROMPT_DICT['prompt_rewrite_question']
210
- prompt = prompt_template.format_map(
211
- {"question": case['question'], "parameters": case['parameters'], "new_parameters": "\n".join(case['new_parameters'])}
212
- )
213
- response = client.chat.completions.create(
214
- model="gpt-4o",
215
- messages=[
216
- {"role": "system", "content": "You are a helpful assistant."},
217
- {"role": "user", "content": prompt}
218
- ]
219
- )
220
- return response
221
-
222
-
223
- def update_question_with_new_parameters():
224
- # infile = open('/shared/xdyu/msr/reasoning_hallucination/data/math/test_dump_gsm8k_train_perturbed.json', 'r')
225
- infile = open('/shared/xdyu/msr/reasoning_eval/data/math/test_dump_math_train_4o_perturbed.json', 'r')
226
- program_data = json.load(infile)
227
- print(len(program_data))
228
- for case in tqdm(program_data):
229
- response = rewrite_question(case)
230
- new_values = [x.strip() for x in response.choices[0].message.content.split('\n') if len(x) > 0]
231
- # print(new_values)
232
- if len(new_values) == 6:
233
- new_values = new_values[1:]
234
- case['new_questions'] = new_values
235
- # print(case)
236
- # break
237
- outfile = open('data/math/test_dump_math_train_4o_perturbed_with_new_questions.json', 'w')
238
- json.dump(program_data, outfile, indent=4)
239
-
240
-
241
- def call_answer_question(question, model_name='gpt'):
242
- prompt_template = PROMPT_DICT['prompt_answer_question']
243
- # prompt_template = PROMPT_DICT['prompt_answer_question_few_shot_cot']
244
- prompt = prompt_template.format_map(
245
- {"question": question}
246
- )
247
- # print(prompt)
248
- if model_name == 'gpt':
249
- response = client.chat.completions.create(
250
- model="gpt-4o",
251
- messages=[
252
- {"role": "system", "content": "You are a helpful assistant."},
253
- {"role": "user", "content": prompt}
254
- ],
255
- temperature=0,
256
- max_tokens=300,
257
- top_p=1
258
- )
259
- return response.choices[0].message.content
260
- else:
261
- if model_name == 'claude':
262
- message = claude_client.messages.create(
263
- model="claude-3-5-sonnet-20240620",
264
- max_tokens=300,
265
- messages=[
266
- {"role": "user", "content": prompt}
267
- ],
268
- temperature=0,
269
- top_p=1
270
- )
271
- return message.content[0].text
272
- # if model_name == 'phi':
273
- # messages = [
274
- # {"role": "system", "content": "You are a helpful AI assistant."},
275
- # {"role": "user", "content": prompt}
276
- # ]
277
- #
278
- # pipe = pipeline(
279
- # "text-generation",
280
- # model=phi_model,
281
- # tokenizer=phi_tokenizer,
282
- # )
283
- #
284
- # generation_args = {
285
- # "max_new_tokens": 300,
286
- # "return_full_text": False,
287
- # "temperature": 0.0,
288
- # "do_sample": False,
289
- # }
290
- #
291
- # output = pipe(messages, **generation_args)
292
- # print(output[0]['generated_text'])
293
- # if model_name == 'qwen':
294
- # messages = [
295
- # {"role": "system", "content": "You are a helpful assistant."},
296
- # {"role": "user", "content": prompt}
297
- # ]
298
- # text = qwen_tokenizer.apply_chat_template(
299
- # messages,
300
- # tokenize=False,
301
- # add_generation_prompt=True
302
- # )
303
- # model_inputs = qwen_tokenizer([text], return_tensors="pt").to(qwen_model.device)
304
- #
305
- # generated_ids = qwen_model.generate(
306
- # **model_inputs,
307
- # max_new_tokens=300,
308
- # temperature=0.7
309
- # )
310
- # generated_ids = [
311
- # output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
312
- # ]
313
- #
314
- # response = qwen_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
315
- # return response
316
- if model_name == 'llama':
317
- messages = [
318
- {"role": "system", "content": "You are a helpful assistant."},
319
- {"role": "user", "content": prompt},
320
- ]
321
- outputs = llama_pipeline(
322
- messages,
323
- max_new_tokens=300,
324
- temperature=0.00001
325
- )
326
- # print(outputs[0]["generated_text"][-1])
327
- return outputs[0]["generated_text"][-1]['content']
328
-
329
-
330
- def answer_question(model_name='gpt'):
331
- infile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions.json', 'r')
332
- program_data = json.load(infile)
333
- print(len(program_data))
334
- for case in tqdm(program_data):
335
- response = call_answer_question(case['question'], model_name=model_name)
336
- case['prediction'] = response
337
- # print(case['prediction'])
338
- case['new_prediction'] = []
339
- for question in case['new_questions']:
340
- response = call_answer_question(question, model_name=model_name)
341
- case['new_prediction'].append(response)
342
- # print(case)
343
- # break
344
- # print(case)
345
- # break
346
- outfile = open('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_llama8b.json', 'w')
347
- # outfile = open('data/math/gsm8k_cot_sc_qwen/temp=0.7_iter=4.json', 'w')
348
- json.dump(program_data, outfile, indent=4)
349
-
350
-
351
- def parse_answer(answer):
352
- if type(answer) is not list:
353
- if 'answer is' in answer:
354
- answer = answer.split('answer is')[-1].strip()
355
- else:
356
- answer = answer.split(' ')[-1]
357
- if len(answer) > 0 and answer[-1] == '.':
358
- answer = answer[0:-1]
359
- answer = re.sub("[^\d\.]", "", answer)
360
- return answer
361
- else:
362
- answer_freq = {}
363
- for x in answer:
364
- parsed_answer = parse_answer(x)
365
- if parsed_answer not in answer_freq:
366
- answer_freq[parsed_answer] = 0
367
- answer_freq[parsed_answer] += 1
368
- answer_freq = sorted(answer_freq.items(), key=lambda item: item[1], reverse=True)
369
- return answer_freq[0][0]
370
-
371
-
372
- def collect_self_consistency_result(infile_path):
373
- folder_data_list = []
374
- for file in os.listdir(infile_path):
375
- infile = open(infile_path + '/' + file, 'r')
376
- file_data = json.load(infile)
377
- folder_data_list.append(file_data)
378
- merged_data = folder_data_list[0]
379
- for idx, case in enumerate(merged_data):
380
- prediction = []
381
- for file_data in folder_data_list:
382
- prediction.append(file_data[idx]['prediction'])
383
- case['prediction'] = prediction
384
- for q_idx in range(0, len(case['new_prediction'])):
385
- new_prediction = []
386
- for file_data in folder_data_list:
387
- new_prediction.append(file_data[idx]['new_prediction'][q_idx])
388
- case['new_prediction'][q_idx] = new_prediction
389
- outfile = open(infile_path+'_merged_result.json', 'w')
390
- json.dump(merged_data, outfile, indent=4)
391
-
392
-
393
-
394
- def evaluator(infile_path):
395
- infile = open(infile_path, 'r')
396
- data = json.load(infile)
397
- correct_case = 0
398
- total_case = 0
399
- total_percentage = 0
400
- new_parameter_correct_counter = {}
401
- for case in data:
402
- if 'new_answers' not in case or len(case['new_answers']) != len(case['new_prediction']):
403
- continue
404
- total_case += 1
405
- prediction = parse_answer(case['prediction'])
406
- if prediction == case['answer'] or case['answer'] in prediction:
407
- correct_case += 1
408
- new_parameter_correct_case = 0
409
- for idx, pred in enumerate(case['new_prediction']):
410
- parsed_pred = parse_answer(pred)
411
- if parsed_pred == case['new_answers'][idx] or case['new_answers'][idx] in parsed_pred:
412
- new_parameter_correct_case += 1
413
- else:
414
- try:
415
- parsed_pred = round(float(parsed_pred))
416
- new_answer = round(float(case['new_answers'][idx]))
417
- if parsed_pred == new_answer:
418
- new_parameter_correct_case += 1
419
- else:
420
- print(parsed_pred, case['new_answers'][idx])
421
- except:
422
- continue
423
- total_parameter_correct_case = len(case['new_prediction'])
424
- percentage = float(new_parameter_correct_case / total_parameter_correct_case)
425
- total_percentage += percentage
426
- if new_parameter_correct_case not in new_parameter_correct_counter:
427
- new_parameter_correct_counter[new_parameter_correct_case] = 0
428
- new_parameter_correct_counter[new_parameter_correct_case] += 1
429
-
430
- # else:
431
- # print(prediction, case['answer'])
432
- print(correct_case, total_case, correct_case/total_case)
433
- print(total_percentage/correct_case)
434
- print(new_parameter_correct_counter)
435
-
436
- def main():
437
- # generate_new_parameter_value()
438
- # update_question_with_new_parameters()
439
- answer_question(model_name='llama')
440
- # collect_self_consistency_result('data/math/gsm8k_cot_sc')
441
- # evaluator('data/math/gsm8k_cot_sc_merged_result.json')
442
- # evaluator('data/math/test_dump_gsm8k_train_perturbed_with_new_questions_answer_few_shot_cot_qwen.json')
443
-
444
-
445
- if __name__ == "__main__":
446
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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12
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15
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16
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17
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18
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19
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20
- margin: 0 auto;
21
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23
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26
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28
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