OpenBMB/BitCPM-CANN-8B-unquantized
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初始化项目，由ModelHub XC社区提供模型

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Model: openbmb/BitCPM-CANN-8B-unquantized
Source: Original Platform
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ModelHub XC
2026-06-04 15:20:58 +08:00
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step,train/loss,train/grad_norm,train/learning_rate,train/epoch,train/train_runtime,train/train_samples_per_second,train/train_steps_per_second,train/total_flos,train/train_loss
2,1.1492999792099,0.6216375231742859,1.9999999949504854e-06,0.0004617871018126607,,,,,
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6,1.1269999742507935,0.784303605556488,6.000000212225132e-06,0.001385361305437982,,,,,
8,1.0542000532150269,0.8737029433250427,7.999999979801942e-06,0.0018471484072506428,,,,,
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60,1.079300045967102,0.3605058789253235,9.999999747378752e-06,0.013853613287210464,,,,,
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74,1.1460000276565552,0.41202062368392944,4.7750145313329995e-06,0.017086124047636986,,,,,
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90,1.1883000135421753,0.4011874198913574,7.612046601934708e-07,0.020780419930815697,,,,,
92,1.1526999473571777,0.3836020231246948,4.894348535344761e-07,0.021242206916213036,,,,,
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96,1.062000036239624,0.3502688705921173,1.2311659247643547e-07,0.022165780887007713,,,,,
98,1.0271999835968018,0.4022065997123718,3.0826662111849146e-08,0.022627567872405052,,,,,
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1 step train/loss train/grad_norm train/learning_rate train/epoch train/train_runtime train/train_samples_per_second train/train_steps_per_second train/total_flos train/train_loss
2 2 1.1492999792099 0.6216375231742859 1.9999999949504854e-06 0.0004617871018126607
3 4 1.0979000329971313 0.681877851486206 3.999999989900971e-06 0.0009235742036253214
4 6 1.1269999742507935 0.784303605556488 6.000000212225132e-06 0.001385361305437982
5 8 1.0542000532150269 0.8737029433250427 7.999999979801942e-06 0.0018471484072506428
6 10 1.2440999746322632 0.7068291902542114 9.999999747378752e-06 0.0023089356254786253
7 12 1.2925000190734863 0.6821666955947876 1.2000000424450263e-05 0.002770722610875964
8 14 1.0843000411987305 0.525643527507782 1.4000000192027073e-05 0.0032325098291039467
9 16 1.0961999893188477 0.43757057189941406 1.5999999959603883e-05 0.0036942968145012856
10 18 1.0614999532699585 0.46141618490219116 1.8000000636675395e-05 0.004156084265559912
11 20 1.332900047302246 0.715879499912262 1.9999999494757503e-05 0.004617871250957251
12 22 1.2070000171661377 0.5926885008811951 1.996917308133561e-05 0.0050796582363545895
13 24 1.2043999433517456 0.5833240747451782 1.9876883015967906e-05 0.005541445221751928
14 26 1.0740000009536743 0.44734400510787964 1.9723698642337695e-05 0.0060032326728105545
15 28 1.1162999868392944 0.3701137900352478 1.9510565834934823e-05 0.006465019658207893
16 30 1.0454000234603882 0.43832680583000183 1.9238796085119247e-05 0.006926806643605232
17 32 1.124899983406067 0.4591037631034851 1.8910064682131633e-05 0.007388593629002571
18 34 1.0686999559402466 0.3873400390148163 1.8526401618146338e-05 0.00785038061439991
19 36 1.0291999578475952 0.40313437581062317 1.8090169760398567e-05 0.008312168531119823
20 38 1.1052000522613525 0.3735405504703522 1.7604059394216165e-05 0.008773955516517162
21 40 1.1555999517440796 0.3818407654762268 1.7071068214136176e-05 0.009235742501914501
22 42 1.0235999822616577 0.4255191683769226 1.6494481315021403e-05 0.00969752948731184
23 44 1.0364999771118164 0.4794503152370453 1.5877853002166376e-05 0.010159316472709179
24 46 1.1344000101089478 0.37273937463760376 1.5224985872919206e-05 0.010621103458106518
25 48 1.0866999626159668 0.417492538690567 1.453990535082994e-05 0.011082890443503857
26 50 1.1038000583648682 0.35408055782318115 1.3826834219798911e-05 0.01154467836022377
27 52 1.1478999853134155 0.3930828273296356 1.3090169886709191e-05 0.012006465345621109
28 54 1.1858999729156494 0.3965947926044464 1.2334453458606731e-05 0.012468252331018448
29 56 1.0096999406814575 0.3860221207141876 1.1564344276848715e-05 0.012930039316415787
30 58 1.114799976348877 0.44393691420555115 1.0784590813273098e-05 0.013391826301813126
31 60 1.079300045967102 0.3605058789253235 9.999999747378752e-06 0.013853613287210464
32 62 1.1766999959945679 0.40689122676849365 9.215408681484405e-06 0.014315400272607803
33 64 1.1075999736785889 0.4002344310283661 8.435655217908788e-06 0.014777187258005142
34 66 1.1866999864578247 0.46947163343429565 7.665546036150772e-06 0.015238975174725056
35 68 1.0311000347137451 0.3296957314014435 6.909830062795663e-06 0.01570076122879982
36 70 1.1088999509811401 0.33858785033226013 6.173165729705943e-06 0.01616254821419716
37 72 1.0720000267028809 0.3967427909374237 5.460095053422265e-06 0.016624337062239647
38 74 1.1460000276565552 0.41202062368392944 4.7750145313329995e-06 0.017086124047636986
39 76 1.0425000190734863 0.38334518671035767 4.1221474020858295e-06 0.017547911033034325
40 78 0.9154000282287598 0.40649303793907166 3.505519543978153e-06 0.018009698018431664
41 80 1.1110999584197998 0.35371580719947815 2.9289321901160292e-06 0.018471485003829002
42 82 1.1672999858856201 0.3381657302379608 2.3959403279150138e-06 0.01893327198922634
43 84 1.2374000549316406 0.3815234303474426 1.909829961732612e-06 0.01939505897462368
44 86 1.2151000499725342 0.38446080684661865 1.4735983313585166e-06 0.01985684596002102
45 88 1.163100004196167 0.40419140458106995 1.0899348126258701e-06 0.020318632945418358
46 90 1.1883000135421753 0.4011874198913574 7.612046601934708e-07 0.020780419930815697
47 92 1.1526999473571777 0.3836020231246948 4.894348535344761e-07 0.021242206916213036
48 94 1.15339994430542 0.452364057302475 2.7630079557638965e-07 0.021703993901610374
49 96 1.062000036239624 0.3502688705921173 1.2311659247643547e-07 0.022165780887007713
50 98 1.0271999835968018 0.4022065997123718 3.0826662111849146e-08 0.022627567872405052
51 100 1.0283000469207764 0.38241174817085266 0.0 0.02308935672044754 183.9481964111328 8.697999954223633 0.5440000295639038 1862467846144.0 1.1177252531051636