Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora16sat/train.log

@@ -0,0 +1,112 @@
+2026-10-03T03:08:54 split=hoL_ye2026_sepsis K=1000 mode=lora trainable=1,296,224 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; imperial_eskd: train 146 rows / 462,172 cells, inner 11; giroux2022: train 24 rows / 254,250 cells, inner 0; liu2021: train 40 rows / 147,532 cells, inner 4
+2026-10-03T03:08:56 step 0 inner-val loss 0.10828 (pretrained) {'immicro': 0.10636159498244524, 'imperial_eskd': 0.1086193590678952, 'liu2021': 0.10984707903116941}
+2026-10-03T03:09:07 step 250 train 0.10334 inner-val 0.10760 best 0.10760@250 (11s) immicro=0.1057 imperial_eskd=0.1079 liu2021=0.1092
+2026-10-03T03:09:18 step 500 train 0.10093 inner-val 0.10596 best 0.10596@500 (22s) immicro=0.1043 imperial_eskd=0.1061 liu2021=0.1075
+2026-10-03T03:09:30 step 750 train 0.10057 inner-val 0.10429 best 0.10429@750 (34s) immicro=0.1030 imperial_eskd=0.1041 liu2021=0.1059
+2026-10-03T03:09:40 step 1000 train 0.10027 inner-val 0.10292 best 0.10292@1000 (44s) immicro=0.1019 imperial_eskd=0.1023 liu2021=0.1046
+2026-10-03T03:09:50 step 1250 train 0.10035 inner-val 0.10197 best 0.10197@1250 (54s) immicro=0.1010 imperial_eskd=0.1012 liu2021=0.1037
+2026-10-03T03:10:00 step 1500 train 0.10010 inner-val 0.10134 best 0.10134@1500 (64s) immicro=0.1004 imperial_eskd=0.1004 liu2021=0.1032
+2026-10-03T03:10:10 step 1750 train 0.10021 inner-val 0.10093 best 0.10093@1750 (74s) immicro=0.0999 imperial_eskd=0.1000 liu2021=0.1029
+2026-10-03T03:10:21 step 2000 train 0.09997 inner-val 0.10067 best 0.10067@2000 (85s) immicro=0.0996 imperial_eskd=0.0997 liu2021=0.1027
+2026-10-03T03:10:32 step 2250 train 0.10009 inner-val 0.10049 best 0.10049@2250 (96s) immicro=0.0994 imperial_eskd=0.0995 liu2021=0.1026
+2026-10-03T03:10:43 step 2500 train 0.10035 inner-val 0.10037 best 0.10037@2500 (107s) immicro=0.0992 imperial_eskd=0.0994 liu2021=0.1025
+2026-10-03T03:10:54 step 2750 train 0.09997 inner-val 0.10028 best 0.10028@2750 (119s) immicro=0.0991 imperial_eskd=0.0993 liu2021=0.1025
+2026-10-03T03:11:06 step 3000 train 0.09993 inner-val 0.10022 best 0.10022@3000 (130s) immicro=0.0990 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-03T03:11:17 step 3250 train 0.10014 inner-val 0.10018 best 0.10018@3250 (142s) immicro=0.0990 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-03T03:11:29 step 3500 train 0.10010 inner-val 0.10015 best 0.10015@3500 (153s) immicro=0.0989 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-03T03:11:40 step 3750 train 0.09998 inner-val 0.10012 best 0.10012@3750 (164s) immicro=0.0989 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-03T03:11:51 step 4000 train 0.09992 inner-val 0.10010 best 0.10010@4000 (176s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-03T03:12:02 step 4250 train 0.09995 inner-val 0.10008 best 0.10008@4250 (187s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-03T03:12:14 step 4500 train 0.09989 inner-val 0.10007 best 0.10007@4500 (198s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-03T03:12:25 step 4750 train 0.09979 inner-val 0.10006 best 0.10006@4750 (210s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:12:36 step 5000 train 0.09996 inner-val 0.10004 best 0.10004@5000 (221s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:12:48 step 5250 train 0.09992 inner-val 0.10003 best 0.10003@5250 (232s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:12:59 step 5500 train 0.09996 inner-val 0.10002 best 0.10002@5500 (244s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:13:11 step 5750 train 0.09952 inner-val 0.10001 best 0.10001@5750 (255s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:13:22 step 6000 train 0.09982 inner-val 0.10000 best 0.10000@6000 (267s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:13:34 step 6250 train 0.09987 inner-val 0.09999 best 0.09999@6250 (278s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T03:13:46 step 6500 train 0.09995 inner-val 0.09998 best 0.09998@6500 (290s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:13:58 step 6750 train 0.09981 inner-val 0.09997 best 0.09997@6750 (302s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:14:09 step 7000 train 0.09974 inner-val 0.09996 best 0.09996@7000 (313s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:14:20 step 7250 train 0.09997 inner-val 0.09995 best 0.09995@7250 (325s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:14:32 step 7500 train 0.09978 inner-val 0.09994 best 0.09994@7500 (336s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:14:43 step 7750 train 0.09977 inner-val 0.09994 best 0.09994@7750 (347s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:14:55 step 8000 train 0.09947 inner-val 0.09993 best 0.09993@8000 (359s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:15:06 step 8250 train 0.09992 inner-val 0.09992 best 0.09992@8250 (370s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-03T03:15:18 step 8500 train 0.09951 inner-val 0.09992 best 0.09992@8500 (382s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:15:29 step 8750 train 0.09998 inner-val 0.09991 best 0.09991@8750 (393s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:15:41 step 9000 train 0.09978 inner-val 0.09991 best 0.09991@9000 (405s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:15:52 step 9250 train 0.09980 inner-val 0.09990 best 0.09990@9250 (416s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:16:03 step 9500 train 0.09972 inner-val 0.09990 best 0.09990@9500 (427s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:16:14 step 9750 train 0.09976 inner-val 0.09990 best 0.09990@9750 (439s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:16:26 step 10000 train 0.09970 inner-val 0.09990 best 0.09990@10000 (450s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:16:37 step 10250 train 0.09985 inner-val 0.09990 best 0.09990@10250 (461s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:16:48 step 10500 train 0.09991 inner-val 0.09989 best 0.09989@10500 (473s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:00 step 10750 train 0.09971 inner-val 0.09990 best 0.09989@10500 (484s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:11 step 11000 train 0.09973 inner-val 0.09989 best 0.09989@10500 (495s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:23 step 11250 train 0.09965 inner-val 0.09989 best 0.09989@11250 (507s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:34 step 11500 train 0.09972 inner-val 0.09989 best 0.09989@11500 (518s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:45 step 11750 train 0.09962 inner-val 0.09988 best 0.09988@11750 (530s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:17:57 step 12000 train 0.09943 inner-val 0.09988 best 0.09988@12000 (541s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:18:08 step 12250 train 0.09960 inner-val 0.09988 best 0.09988@12250 (552s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:18:19 step 12500 train 0.09978 inner-val 0.09987 best 0.09987@12500 (564s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T03:18:31 step 12750 train 0.09972 inner-val 0.09987 best 0.09987@12750 (575s) immicro=0.0986 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:18:42 step 13000 train 0.09981 inner-val 0.09987 best 0.09987@13000 (586s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:18:53 step 13250 train 0.09978 inner-val 0.09986 best 0.09986@13250 (598s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:19:05 step 13500 train 0.09964 inner-val 0.09986 best 0.09986@13500 (609s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:19:16 step 13750 train 0.09974 inner-val 0.09986 best 0.09986@13750 (620s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:19:28 step 14000 train 0.09959 inner-val 0.09986 best 0.09986@14000 (632s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:19:39 step 14250 train 0.09949 inner-val 0.09985 best 0.09985@14250 (643s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:19:50 step 14500 train 0.09984 inner-val 0.09985 best 0.09985@14500 (655s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:02 step 14750 train 0.09970 inner-val 0.09985 best 0.09985@14750 (666s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:13 step 15000 train 0.09981 inner-val 0.09985 best 0.09985@14750 (677s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:25 step 15250 train 0.09957 inner-val 0.09985 best 0.09985@14750 (689s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:36 step 15500 train 0.09977 inner-val 0.09985 best 0.09985@15500 (700s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:47 step 15750 train 0.09952 inner-val 0.09984 best 0.09984@15750 (712s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:20:59 step 16000 train 0.09958 inner-val 0.09984 best 0.09984@15750 (723s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:21:10 step 16250 train 0.09966 inner-val 0.09984 best 0.09984@15750 (734s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:21:21 step 16500 train 0.09978 inner-val 0.09984 best 0.09984@16500 (746s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:21:33 step 16750 train 0.09950 inner-val 0.09984 best 0.09984@16750 (757s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:21:44 step 17000 train 0.09981 inner-val 0.09984 best 0.09984@16750 (768s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:21:56 step 17250 train 0.09989 inner-val 0.09984 best 0.09984@17250 (780s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:22:07 step 17500 train 0.09974 inner-val 0.09983 best 0.09983@17500 (791s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:22:19 step 17750 train 0.09945 inner-val 0.09983 best 0.09983@17750 (803s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:22:30 step 18000 train 0.10002 inner-val 0.09983 best 0.09983@17750 (814s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:22:41 step 18250 train 0.09945 inner-val 0.09983 best 0.09983@17750 (826s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:22:53 step 18500 train 0.09975 inner-val 0.09983 best 0.09983@17750 (837s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:23:04 step 18750 train 0.09952 inner-val 0.09982 best 0.09982@18750 (848s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:23:15 step 19000 train 0.09966 inner-val 0.09982 best 0.09982@19000 (860s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:23:27 step 19250 train 0.09955 inner-val 0.09982 best 0.09982@19250 (871s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:23:38 step 19500 train 0.09954 inner-val 0.09982 best 0.09982@19250 (882s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:23:50 step 19750 train 0.09948 inner-val 0.09982 best 0.09982@19250 (894s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:24:01 step 20000 train 0.09970 inner-val 0.09982 best 0.09982@19250 (905s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:24:03 block end 20000: best 0.09982@19250; block improvement 0.008457 (100.00 % of total 0.008457) -> continue
+2026-10-03T03:24:14 step 20250 train 0.09964 inner-val 0.09982 best 0.09982@19250 (918s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:24:26 step 20500 train 0.09933 inner-val 0.09982 best 0.09982@20500 (930s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:24:37 step 20750 train 0.09958 inner-val 0.09981 best 0.09981@20750 (941s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:24:48 step 21000 train 0.09973 inner-val 0.09981 best 0.09981@20750 (953s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:00 step 21250 train 0.09916 inner-val 0.09981 best 0.09981@21250 (964s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:11 step 21500 train 0.09940 inner-val 0.09981 best 0.09981@21500 (976s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:23 step 21750 train 0.09975 inner-val 0.09981 best 0.09981@21750 (987s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:34 step 22000 train 0.09962 inner-val 0.09981 best 0.09981@21750 (998s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:46 step 22250 train 0.09958 inner-val 0.09981 best 0.09981@21750 (1010s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:25:57 step 22500 train 0.09954 inner-val 0.09980 best 0.09980@22500 (1021s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:26:09 step 22750 train 0.09965 inner-val 0.09981 best 0.09980@22500 (1033s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:26:20 step 23000 train 0.09992 inner-val 0.09981 best 0.09980@22500 (1045s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:26:32 step 23250 train 0.09972 inner-val 0.09980 best 0.09980@22500 (1056s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:26:44 step 23500 train 0.09978 inner-val 0.09980 best 0.09980@23500 (1068s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:26:55 step 23750 train 0.09944 inner-val 0.09980 best 0.09980@23750 (1079s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:27:07 step 24000 train 0.09986 inner-val 0.09980 best 0.09980@24000 (1091s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:27:18 step 24250 train 0.09937 inner-val 0.09980 best 0.09980@24250 (1102s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:27:29 step 24500 train 0.09931 inner-val 0.09979 best 0.09979@24500 (1114s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:27:40 step 24750 train 0.09966 inner-val 0.09979 best 0.09979@24750 (1125s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:27:52 step 25000 train 0.09948 inner-val 0.09979 best 0.09979@24750 (1136s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:28:03 step 25250 train 0.09961 inner-val 0.09979 best 0.09979@24750 (1148s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:28:15 step 25500 train 0.09942 inner-val 0.09979 best 0.09979@24750 (1159s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:28:26 step 25750 train 0.09966 inner-val 0.09980 best 0.09979@24750 (1170s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T03:28:37 step 26000 train 0.09964 inner-val 0.09979 best 0.09979@24750 (1181s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:28:49 step 26250 train 0.09952 inner-val 0.09979 best 0.09979@24750 (1193s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:29:00 step 26500 train 0.09972 inner-val 0.09979 best 0.09979@24750 (1204s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:29:11 step 26750 train 0.09925 inner-val 0.09979 best 0.09979@24750 (1216s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T03:29:11 early stop: no improvement in 8 evaluations
+2026-10-03T03:29:17 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora16sat/checkpoint_ft.pt (selected step 24750, inner-val 0.09979 vs pretrained 0.10828); sha 25db6528ed04

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64/history.json

@@ -0,0 +1,705 @@
+[
+ {
+  "step": 0,
+  "inner_val_loss": 0.10827601102716995,
+  "inner_val_per_cohort": {
+   "immicro": 0.10636159498244524,
+   "imperial_eskd": 0.1086193590678952,
+   "liu2021": 0.10984707903116941
+  },
+  "train_loss": null
+ },
+ {
+  "step": 250,
+  "inner_val_loss": 0.10726830744269218,
+  "inner_val_per_cohort": {
+   "immicro": 0.10542473662644625,
+   "imperial_eskd": 0.10751504010774872,
+   "liu2021": 0.10886514559388161
+  },
+  "train_loss": 0.10270090734958649,
+  "sec": 11.7
+ },
+ {
+  "step": 500,
+  "inner_val_loss": 0.10546406937029325,
+  "inner_val_per_cohort": {
+   "immicro": 0.10382997244596481,
+   "imperial_eskd": 0.10550473224033009,
+   "liu2021": 0.10705750342458487
+  },
+  "train_loss": 0.10063257715106011,
+  "sec": 23.7
+ },
+ {
+  "step": 750,
+  "inner_val_loss": 0.10373948439203128,
+  "inner_val_per_cohort": {
+   "immicro": 0.1024517451102535,
+   "imperial_eskd": 0.10336958142844113,
+   "liu2021": 0.1053971266373992
+  },
+  "train_loss": 0.10037505868077277,
+  "sec": 35.6
+ },
+ {
+  "step": 1000,
+  "inner_val_loss": 0.1024205447786083,
+  "inner_val_per_cohort": {
+   "immicro": 0.10137436197449763,
+   "imperial_eskd": 0.10169565711509097,
+   "liu2021": 0.10419161524623632
+  },
+  "train_loss": 0.1000797398686409,
+  "sec": 47.6
+ },
+ {
+  "step": 1250,
+  "inner_val_loss": 0.1015513694628772,
+  "inner_val_per_cohort": {
+   "immicro": 0.1005761946241061,
+   "imperial_eskd": 0.10063335333358157,
+   "liu2021": 0.10344456043094397
+  },
+  "train_loss": 0.10017555391788482,
+  "sec": 59.6
+ },
+ {
+  "step": 1500,
+  "inner_val_loss": 0.10100688134329487,
+  "inner_val_per_cohort": {
+   "immicro": 0.09999394478897254,
+   "imperial_eskd": 0.10001225058328021,
+   "liu2021": 0.10301444865763187
+  },
+  "train_loss": 0.09994212421774865,
+  "sec": 71.4
+ },
+ {
+  "step": 1750,
+  "inner_val_loss": 0.10065855000243344,
+  "inner_val_per_cohort": {
+   "immicro": 0.09958950740595658,
+   "imperial_eskd": 0.09963068636980923,
+   "liu2021": 0.10275545623153448
+  },
+  "train_loss": 0.10005727735161782,
+  "sec": 83.3
+ },
+ {
+  "step": 2000,
+  "inner_val_loss": 0.10043430085660833,
+  "inner_val_per_cohort": {
+   "immicro": 0.09930831100791693,
+   "imperial_eskd": 0.09940264204686339,
+   "liu2021": 0.10259194951504469
+  },
+  "train_loss": 0.09982226571440697,
+  "sec": 95.3
+ },
+ {
+  "step": 2250,
+  "inner_val_loss": 0.1002833242951469,
+  "inner_val_per_cohort": {
+   "immicro": 0.09911872446537018,
+   "imperial_eskd": 0.09925826605070721,
+   "liu2021": 0.10247298236936331
+  },
+  "train_loss": 0.09994704848527908,
+  "sec": 107.1
+ },
+ {
+  "step": 2500,
+  "inner_val_loss": 0.10017923964657867,
+  "inner_val_per_cohort": {
+   "immicro": 0.09898490117241938,
+   "imperial_eskd": 0.09915754774754698,
+   "liu2021": 0.10239527001976967
+  },
+  "train_loss": 0.1001971900165081,
+  "sec": 118.7
+ },
+ {
+  "step": 2750,
+  "inner_val_loss": 0.10010535929427304,
+  "inner_val_per_cohort": {
+   "immicro": 0.09888610740502675,
+   "imperial_eskd": 0.0990916107865897,
+   "liu2021": 0.10233835969120264
+  },
+  "train_loss": 0.09980975806713105,
+  "sec": 130.5
+ },
+ {
+  "step": 3000,
+  "inner_val_loss": 0.10005084914390487,
+  "inner_val_per_cohort": {
+   "immicro": 0.09881323544929425,
+   "imperial_eskd": 0.0990413288501176,
+   "liu2021": 0.10229798313230276
+  },
+  "train_loss": 0.09977523875236512,
+  "sec": 142.4
+ },
+ {
+  "step": 3250,
+  "inner_val_loss": 0.10001074099405245,
+  "inner_val_per_cohort": {
+   "immicro": 0.09875891171395779,
+   "imperial_eskd": 0.09900419447909702,
+   "liu2021": 0.10226911678910255
+  },
+  "train_loss": 0.09998596471548081,
+  "sec": 154.2
+ },
+ {
+  "step": 3500,
+  "inner_val_loss": 0.09997580461956636,
+  "inner_val_per_cohort": {
+   "immicro": 0.09871138942738374,
+   "imperial_eskd": 0.09897003051909534,
+   "liu2021": 0.10224599391222
+  },
+  "train_loss": 0.09993550208210945,
+  "sec": 165.2
+ },
+ {
+  "step": 3750,
+  "inner_val_loss": 0.0999490846268306,
+  "inner_val_per_cohort": {
+   "immicro": 0.09867636455843846,
+   "imperial_eskd": 0.09894275258887898,
+   "liu2021": 0.10222813673317432
+  },
+  "train_loss": 0.09980573174357414,
+  "sec": 176.9
+ },
+ {
+  "step": 4000,
+  "inner_val_loss": 0.09992596615256384,
+  "inner_val_per_cohort": {
+   "immicro": 0.09864036335299413,
+   "imperial_eskd": 0.09891629760915582,
+   "liu2021": 0.10222123749554157
+  },
+  "train_loss": 0.09974821138381958,
+  "sec": 188.9
+ },
+ {
+  "step": 4250,
+  "inner_val_loss": 0.0999076326631687,
+  "inner_val_per_cohort": {
+   "immicro": 0.09861395973712206,
+   "imperial_eskd": 0.09889652038162405,
+   "liu2021": 0.10221241787075996
+  },
+  "train_loss": 0.09977313852310181,
+  "sec": 200.7
+ },
+ {
+  "step": 4500,
+  "inner_val_loss": 0.09989045167134868,
+  "inner_val_per_cohort": {
+   "immicro": 0.09859093030293782,
+   "imperial_eskd": 0.09887237474322319,
+   "liu2021": 0.10220804996788502
+  },
+  "train_loss": 0.09970952105522156,
+  "sec": 212.7
+ },
+ {
+  "step": 4750,
+  "inner_val_loss": 0.09987612898376856,
+  "inner_val_per_cohort": {
+   "immicro": 0.09857195522636175,
+   "imperial_eskd": 0.09885659881613472,
+   "liu2021": 0.10219983290880919
+  },
+  "train_loss": 0.09959850868582726,
+  "sec": 224.8
+ },
+ {
+  "step": 5000,
+  "inner_val_loss": 0.0998623613268137,
+  "inner_val_per_cohort": {
+   "immicro": 0.09855174459517002,
+   "imperial_eskd": 0.09884344041347504,
+   "liu2021": 0.10219189897179604
+  },
+  "train_loss": 0.09977317202091217,
+  "sec": 236.6
+ },
+ {
+  "step": 5250,
+  "inner_val_loss": 0.09984872853289349,
+  "inner_val_per_cohort": {
+   "immicro": 0.09853378714372714,
+   "imperial_eskd": 0.09882956404577602,
+   "liu2021": 0.1021828344091773
+  },
+  "train_loss": 0.09972456341981888,
+  "sec": 248.6
+ },
+ {
+  "step": 5500,
+  "inner_val_loss": 0.09983680739697783,
+  "inner_val_per_cohort": {
+   "immicro": 0.09851657195637624,
+   "imperial_eskd": 0.09881759841333736,
+   "liu2021": 0.10217625182121992
+  },
+  "train_loss": 0.09976890355348587,
+  "sec": 260.6
+ },
+ {
+  "step": 5750,
+  "inner_val_loss": 0.099825535214158,
+  "inner_val_per_cohort": {
+   "immicro": 0.0985001086567839,
+   "imperial_eskd": 0.0988028957085176,
+   "liu2021": 0.10217360127717257
+  },
+  "train_loss": 0.0993116979598999,
+  "sec": 272.7
+ },
+ {
+  "step": 6000,
+  "inner_val_loss": 0.09981175562874837,
+  "inner_val_per_cohort": {
+   "immicro": 0.09848416689783335,
+   "imperial_eskd": 0.09878998588431966,
+   "liu2021": 0.10216111410409212
+  },
+  "train_loss": 0.09960695725679397,
+  "sec": 284.7
+ },
+ {
+  "step": 6250,
+  "inner_val_loss": 0.09980083682169817,
+  "inner_val_per_cohort": {
+   "immicro": 0.09846683156987031,
+   "imperial_eskd": 0.09878119487654079,
+   "liu2021": 0.10215448401868343
+  },
+  "train_loss": 0.09966992327570916,
+  "sec": 296.7
+ },
+ {
+  "step": 6500,
+  "inner_val_loss": 0.09978956626634104,
+  "inner_val_per_cohort": {
+   "immicro": 0.0984572305654486,
+   "imperial_eskd": 0.0987721945751797,
+   "liu2021": 0.10213927365839481
+  },
+  "train_loss": 0.0997515520453453,
+  "sec": 308.8
+ },
+ {
+  "step": 6750,
+  "inner_val_loss": 0.09977524873396061,
+  "inner_val_per_cohort": {
+   "immicro": 0.09844368354727824,
+   "imperial_eskd": 0.09875918213616718,
+   "liu2021": 0.10212288051843643
+  },
+  "train_loss": 0.09959788158535958,
+  "sec": 320.9
+ },
+ {
+  "step": 7000,
+  "inner_val_loss": 0.09976577483155209,
+  "inner_val_per_cohort": {
+   "immicro": 0.09843429146955411,
+   "imperial_eskd": 0.09875817732377486,
+   "liu2021": 0.10210485570132732
+  },
+  "train_loss": 0.09952098730206489,
+  "sec": 332.9
+ },
+ {
+  "step": 7250,
+  "inner_val_loss": 0.09975827131608521,
+  "inner_val_per_cohort": {
+   "immicro": 0.0984239283328255,
+   "imperial_eskd": 0.09875076568939468,
+   "liu2021": 0.1021001199260354
+  },
+  "train_loss": 0.09974249657988549,
+  "sec": 343.9
+ },
+ {
+  "step": 7500,
+  "inner_val_loss": 0.09975153274570751,
+  "inner_val_per_cohort": {
+   "immicro": 0.09841166281451781,
+   "imperial_eskd": 0.09874993732029741,
+   "liu2021": 0.10209299810230732
+  },
+  "train_loss": 0.09955127349495888,
+  "sec": 354.1
+ },
+ {
+  "step": 7750,
+  "inner_val_loss": 0.09974195210808756,
+  "inner_val_per_cohort": {
+   "immicro": 0.09839833310494821,
+   "imperial_eskd": 0.09874412519010631,
+   "liu2021": 0.10208339802920818
+  },
+  "train_loss": 0.09954452291131019,
+  "sec": 364.4
+ },
+ {
+  "step": 8000,
+  "inner_val_loss": 0.09973331814575376,
+  "inner_val_per_cohort": {
+   "immicro": 0.09838834963738918,
+   "imperial_eskd": 0.09873570671135729,
+   "liu2021": 0.1020758980885148
+  },
+  "train_loss": 0.09924208855628967,
+  "sec": 374.8
+ },
+ {
+  "step": 8250,
+  "inner_val_loss": 0.09972917481391419,
+  "inner_val_per_cohort": {
+   "immicro": 0.09838081213335197,
+   "imperial_eskd": 0.09873436255888506,
+   "liu2021": 0.10207234974950552
+  },
+  "train_loss": 0.09968985944986343,
+  "sec": 385.1
+ },
+ {
+  "step": 8500,
+  "inner_val_loss": 0.0997211794660549,
+  "inner_val_per_cohort": {
+   "immicro": 0.09837040274093549,
+   "imperial_eskd": 0.09872928702018478,
+   "liu2021": 0.10206384863704443
+  },
+  "train_loss": 0.09927724292874336,
+  "sec": 395.4
+ },
+ {
+  "step": 8750,
+  "inner_val_loss": 0.09971236850539543,
+  "inner_val_per_cohort": {
+   "immicro": 0.09835935073594253,
+   "imperial_eskd": 0.09872640262950551,
+   "liu2021": 0.10205135215073824
+  },
+  "train_loss": 0.099745321393013,
+  "sec": 405.7
+ },
+ {
+  "step": 9000,
+  "inner_val_loss": 0.09970722291054147,
+  "inner_val_per_cohort": {
+   "immicro": 0.09835299849510193,
+   "imperial_eskd": 0.09872502664273436,
+   "liu2021": 0.10204364359378815
+  },
+  "train_loss": 0.09955113330483437,
+  "sec": 416.1
+ },
+ {
+  "step": 9250,
+  "inner_val_loss": 0.09969991944128215,
+  "inner_val_per_cohort": {
+   "immicro": 0.09834584624816974,
+   "imperial_eskd": 0.09871771254322746,
+   "liu2021": 0.10203619953244925
+  },
+  "train_loss": 0.0995605261027813,
+  "sec": 426.5
+ },
+ {
+  "step": 9500,
+  "inner_val_loss": 0.09969683978330306,
+  "inner_val_per_cohort": {
+   "immicro": 0.09833913141240676,
+   "imperial_eskd": 0.09871826083822684,
+   "liu2021": 0.10203312709927559
+  },
+  "train_loss": 0.09947743394970894,
+  "sec": 436.9
+ },
+ {
+  "step": 9750,
+  "inner_val_loss": 0.09969315957278013,
+  "inner_val_per_cohort": {
+   "immicro": 0.09833037573844194,
+   "imperial_eskd": 0.09871664270758629,
+   "liu2021": 0.10203246027231216
+  },
+  "train_loss": 0.09951701188087464,
+  "sec": 448.0
+ },
+ {
+  "step": 10000,
+  "inner_val_loss": 0.09969036514409864,
+  "inner_val_per_cohort": {
+   "immicro": 0.09832167749603589,
+   "imperial_eskd": 0.09871740097349341,
+   "liu2021": 0.10203201696276665
+  },
+  "train_loss": 0.09944971358776092,
+  "sec": 460.1
+ },
+ {
+  "step": 10250,
+  "inner_val_loss": 0.0996825485725445,
+  "inner_val_per_cohort": {
+   "immicro": 0.09831090333561103,
+   "imperial_eskd": 0.09870920601216229,
+   "liu2021": 0.10202753636986017
+  },
+  "train_loss": 0.09960869258642197,
+  "sec": 471.0
+ },
+ {
+  "step": 10500,
+  "inner_val_loss": 0.09967935987456579,
+  "inner_val_per_cohort": {
+   "immicro": 0.09830323824038108,
+   "imperial_eskd": 0.09870820560238579,
+   "liu2021": 0.10202663578093052
+  },
+  "train_loss": 0.09963642501831055,
+  "sec": 481.2
+ },
+ {
+  "step": 10750,
+  "inner_val_loss": 0.09967945546212824,
+  "inner_val_per_cohort": {
+   "immicro": 0.09829776299496491,
+   "imperial_eskd": 0.09871302883733403,
+   "liu2021": 0.10202757455408573
+  },
+  "train_loss": 0.09946285966038704,
+  "sec": 491.7
+ },
+ {
+  "step": 11000,
+  "inner_val_loss": 0.09967610754561845,
+  "inner_val_per_cohort": {
+   "immicro": 0.09828899490336578,
+   "imperial_eskd": 0.09871583702889355,
+   "liu2021": 0.10202349070459604
+  },
+  "train_loss": 0.09950033336877823,
+  "sec": 502.0
+ },
+ {
+  "step": 11250,
+  "inner_val_loss": 0.0996686208692163,
+  "inner_val_per_cohort": {
+   "immicro": 0.09828084024290244,
+   "imperial_eskd": 0.09870653193105351,
+   "liu2021": 0.10201849043369293
+  },
+  "train_loss": 0.0993753208220005,
+  "sec": 512.3
+ },
+ {
+  "step": 11500,
+  "inner_val_loss": 0.09966325551986394,
+  "inner_val_per_cohort": {
+   "immicro": 0.0982746339092652,
+   "imperial_eskd": 0.09870057146657597,
+   "liu2021": 0.10201456118375063
+  },
+  "train_loss": 0.09946013849973678,
+  "sec": 522.6
+ },
+ {
+  "step": 11750,
+  "inner_val_loss": 0.09965910831221729,
+  "inner_val_per_cohort": {
+   "immicro": 0.09826936076084773,
+   "imperial_eskd": 0.09869798137383028,
+   "liu2021": 0.10200998280197382
+  },
+  "train_loss": 0.09935733038187027,
+  "sec": 533.1
+ },
+ {
+  "step": 12000,
+  "inner_val_loss": 0.09965059354976573,
+  "inner_val_per_cohort": {
+   "immicro": 0.09825734173258145,
+   "imperial_eskd": 0.0986866642805663,
+   "liu2021": 0.1020077746361494
+  },
+  "train_loss": 0.09916825929284095,
+  "sec": 543.4
+ },
+ {
+  "step": 12250,
+  "inner_val_loss": 0.09963928670105006,
+  "inner_val_per_cohort": {
+   "immicro": 0.09824763083209594,
+   "imperial_eskd": 0.09867751598358154,
+   "liu2021": 0.10199271328747272
+  },
+  "train_loss": 0.09933510613441467,
+  "sec": 553.8
+ },
+ {
+  "step": 12500,
+  "inner_val_loss": 0.0996344072386773,
+  "inner_val_per_cohort": {
+   "immicro": 0.09824000174800555,
+   "imperial_eskd": 0.09867345338517969,
+   "liu2021": 0.10198976658284664
+  },
+  "train_loss": 0.09950089830160141,
+  "sec": 564.2
+ },
+ {
+  "step": 12750,
+  "inner_val_loss": 0.09962613017044285,
+  "inner_val_per_cohort": {
+   "immicro": 0.09823012538254261,
+   "imperial_eskd": 0.0986643900925463,
+   "liu2021": 0.10198387503623962
+  },
+  "train_loss": 0.09943969869613647,
+  "sec": 574.4
+ },
+ {
+  "step": 13000,
+  "inner_val_loss": 0.09961980148547828,
+  "inner_val_per_cohort": {
+   "immicro": 0.09822151561578114,
+   "imperial_eskd": 0.09866493263027885,
+   "liu2021": 0.10197295621037483
+  },
+  "train_loss": 0.09951297789812089,
+  "sec": 584.6
+ },
+ {
+  "step": 13250,
+  "inner_val_loss": 0.09961521937841117,
+  "inner_val_per_cohort": {
+   "immicro": 0.09821235047032435,
+   "imperial_eskd": 0.09866002269766548,
+   "liu2021": 0.10197328496724367
+  },
+  "train_loss": 0.09950392726063728,
+  "sec": 595.0
+ },
+ {
+  "step": 13500,
+  "inner_val_loss": 0.09961062080857126,
+  "inner_val_per_cohort": {
+   "immicro": 0.0982035684088866,
+   "imperial_eskd": 0.09865671430121768,
+   "liu2021": 0.10197157971560955
+  },
+  "train_loss": 0.09935772740840912,
+  "sec": 605.5
+ },
+ {
+  "step": 13750,
+  "inner_val_loss": 0.09960528922201406,
+  "inner_val_per_cohort": {
+   "immicro": 0.09819372774412234,
+   "imperial_eskd": 0.09865068407221274,
+   "liu2021": 0.10197145584970713
+  },
+  "train_loss": 0.09944565895199775,
+  "sec": 615.9
+ },
+ {
+  "step": 14000,
+  "inner_val_loss": 0.09960106923009711,
+  "inner_val_per_cohort": {
+   "immicro": 0.09818459705760081,
+   "imperial_eskd": 0.09865037622776898,
+   "liu2021": 0.10196823440492153
+  },
+  "train_loss": 0.0992985166311264,
+  "sec": 626.2
+ },
+ {
+  "step": 14250,
+  "inner_val_loss": 0.09959160736903096,
+  "inner_val_per_cohort": {
+   "immicro": 0.09817402344197035,
+   "imperial_eskd": 0.09864599393172697,
+   "liu2021": 0.10195480473339558
+  },
+  "train_loss": 0.09919915196299553,
+  "sec": 636.5
+ },
+ {
+  "step": 14500,
+  "inner_val_loss": 0.09958825715714031,
+  "inner_val_per_cohort": {
+   "immicro": 0.09816848828146855,
+   "imperial_eskd": 0.09864668548107147,
+   "liu2021": 0.1019495977088809
+  },
+  "train_loss": 0.09953417009115219,
+  "sec": 646.9
+ },
+ {
+  "step": 14750,
+  "inner_val_loss": 0.0995865830154431,
+  "inner_val_per_cohort": {
+   "immicro": 0.0981625368197759,
+   "imperial_eskd": 0.09864760427312418,
+   "liu2021": 0.10194960795342922
+  },
+  "train_loss": 0.09940882018208504,
+  "sec": 657.2
+ },
+ {
+  "step": 15000,
+  "inner_val_loss": 0.09958488608926835,
+  "inner_val_per_cohort": {
+   "immicro": 0.09816165392597516,
+   "imperial_eskd": 0.0986476990986954,
+   "liu2021": 0.1019453052431345
+  },
+  "train_loss": 0.09948887386918068,
+  "sec": 667.6
+ },
+ {
+  "step": 15250,
+  "inner_val_loss": 0.09958014113245288,
+  "inner_val_per_cohort": {
+   "immicro": 0.09815801742176215,
+   "imperial_eskd": 0.09864577041430907,
+   "liu2021": 0.1019366355612874
+  },
+  "train_loss": 0.09925769266486167,
+  "sec": 679.3
+ },
+ {
+  "step": 15500,
+  "inner_val_loss": 0.09957331853608291,
+  "inner_val_per_cohort": {
+   "immicro": 0.09814999252557755,
+   "imperial_eskd": 0.09863975644111633,
+   "liu2021": 0.10193020664155483
+  },
+  "train_loss": 0.09946393185853958,
+  "sec": 689.5
+ },
+ {
+  "step": 15750,
+  "inner_val_loss": 0.09956708879708642,
+  "inner_val_per_cohort": {
+   "immicro": 0.09814226751526196,
+   "imperial_eskd": 0.09863144836642525,
+   "liu2021": 0.10192755050957203
+  },
+  "train_loss": 0.09920621544122696,
+  "sec": 699.9
+ }
+]

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64/train.log

@@ -0,0 +1,65 @@
+2026-10-03T01:44:40 split=hoL_ye2026_sepsis K=1000 mode=lora trainable=5,184,896 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; imperial_eskd: train 146 rows / 462,172 cells, inner 11; giroux2022: train 24 rows / 254,250 cells, inner 0; liu2021: train 40 rows / 147,532 cells, inner 4
+2026-10-03T01:44:41 step 0 inner-val loss 0.10828 (pretrained) {'immicro': 0.10636159498244524, 'imperial_eskd': 0.1086193590678952, 'liu2021': 0.10984707903116941}
+2026-10-03T01:44:53 step 250 train 0.10270 inner-val 0.10727 best 0.10727@250 (12s) immicro=0.1054 imperial_eskd=0.1075 liu2021=0.1089
+2026-10-03T01:45:05 step 500 train 0.10063 inner-val 0.10546 best 0.10546@500 (24s) immicro=0.1038 imperial_eskd=0.1055 liu2021=0.1071
+2026-10-03T01:45:17 step 750 train 0.10038 inner-val 0.10374 best 0.10374@750 (36s) immicro=0.1025 imperial_eskd=0.1034 liu2021=0.1054
+2026-10-03T01:45:29 step 1000 train 0.10008 inner-val 0.10242 best 0.10242@1000 (48s) immicro=0.1014 imperial_eskd=0.1017 liu2021=0.1042
+2026-10-03T01:45:41 step 1250 train 0.10018 inner-val 0.10155 best 0.10155@1250 (60s) immicro=0.1006 imperial_eskd=0.1006 liu2021=0.1034
+2026-10-03T01:45:53 step 1500 train 0.09994 inner-val 0.10101 best 0.10101@1500 (71s) immicro=0.1000 imperial_eskd=0.1000 liu2021=0.1030
+2026-10-03T01:46:05 step 1750 train 0.10006 inner-val 0.10066 best 0.10066@1750 (83s) immicro=0.0996 imperial_eskd=0.0996 liu2021=0.1028
+2026-10-03T01:46:17 step 2000 train 0.09982 inner-val 0.10043 best 0.10043@2000 (95s) immicro=0.0993 imperial_eskd=0.0994 liu2021=0.1026
+2026-10-03T01:46:29 step 2250 train 0.09995 inner-val 0.10028 best 0.10028@2250 (107s) immicro=0.0991 imperial_eskd=0.0993 liu2021=0.1025
+2026-10-03T01:46:40 step 2500 train 0.10020 inner-val 0.10018 best 0.10018@2500 (119s) immicro=0.0990 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-03T01:46:52 step 2750 train 0.09981 inner-val 0.10011 best 0.10011@2750 (130s) immicro=0.0989 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-03T01:47:04 step 3000 train 0.09978 inner-val 0.10005 best 0.10005@3000 (142s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T01:47:16 step 3250 train 0.09999 inner-val 0.10001 best 0.10001@3250 (154s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-03T01:47:27 step 3500 train 0.09994 inner-val 0.09998 best 0.09998@3500 (165s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1022
+2026-10-03T01:47:38 step 3750 train 0.09981 inner-val 0.09995 best 0.09995@3750 (177s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T01:47:50 step 4000 train 0.09975 inner-val 0.09993 best 0.09993@4000 (189s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T01:48:02 step 4250 train 0.09977 inner-val 0.09991 best 0.09991@4250 (201s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T01:48:14 step 4500 train 0.09971 inner-val 0.09989 best 0.09989@4500 (213s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T01:48:26 step 4750 train 0.09960 inner-val 0.09988 best 0.09988@4750 (225s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-03T01:48:38 step 5000 train 0.09977 inner-val 0.09986 best 0.09986@5000 (237s) immicro=0.0986 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:48:50 step 5250 train 0.09972 inner-val 0.09985 best 0.09985@5250 (249s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:49:02 step 5500 train 0.09977 inner-val 0.09984 best 0.09984@5500 (261s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:49:14 step 5750 train 0.09931 inner-val 0.09983 best 0.09983@5750 (273s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:49:26 step 6000 train 0.09961 inner-val 0.09981 best 0.09981@6000 (285s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:49:38 step 6250 train 0.09967 inner-val 0.09980 best 0.09980@6250 (297s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-03T01:49:50 step 6500 train 0.09975 inner-val 0.09979 best 0.09979@6500 (309s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T01:50:02 step 6750 train 0.09960 inner-val 0.09978 best 0.09978@6750 (321s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T01:50:14 step 7000 train 0.09952 inner-val 0.09977 best 0.09977@7000 (333s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T01:50:25 step 7250 train 0.09974 inner-val 0.09976 best 0.09976@7250 (344s) immicro=0.0984 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-03T01:50:36 step 7500 train 0.09955 inner-val 0.09975 best 0.09975@7500 (354s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:50:46 step 7750 train 0.09954 inner-val 0.09974 best 0.09974@7750 (364s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:50:56 step 8000 train 0.09924 inner-val 0.09973 best 0.09973@8000 (375s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:51:07 step 8250 train 0.09969 inner-val 0.09973 best 0.09973@8250 (385s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:51:17 step 8500 train 0.09928 inner-val 0.09972 best 0.09972@8500 (395s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:51:27 step 8750 train 0.09975 inner-val 0.09971 best 0.09971@8750 (406s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1021
+2026-10-03T01:51:38 step 9000 train 0.09955 inner-val 0.09971 best 0.09971@9000 (416s) immicro=0.0984 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:51:48 step 9250 train 0.09956 inner-val 0.09970 best 0.09970@9250 (426s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:51:58 step 9500 train 0.09948 inner-val 0.09970 best 0.09970@9500 (437s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:52:10 step 9750 train 0.09952 inner-val 0.09969 best 0.09969@9750 (448s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:52:22 step 10000 train 0.09945 inner-val 0.09969 best 0.09969@10000 (460s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:52:32 step 10250 train 0.09961 inner-val 0.09968 best 0.09968@10250 (471s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:52:43 step 10500 train 0.09964 inner-val 0.09968 best 0.09968@10500 (481s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:52:53 step 10750 train 0.09946 inner-val 0.09968 best 0.09968@10500 (492s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:03 step 11000 train 0.09950 inner-val 0.09968 best 0.09968@11000 (502s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:14 step 11250 train 0.09938 inner-val 0.09967 best 0.09967@11250 (512s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:24 step 11500 train 0.09946 inner-val 0.09966 best 0.09966@11500 (523s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:35 step 11750 train 0.09936 inner-val 0.09966 best 0.09966@11750 (533s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:45 step 12000 train 0.09917 inner-val 0.09965 best 0.09965@12000 (543s) immicro=0.0983 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:53:55 step 12250 train 0.09934 inner-val 0.09964 best 0.09964@12250 (554s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:06 step 12500 train 0.09950 inner-val 0.09963 best 0.09963@12500 (564s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:16 step 12750 train 0.09944 inner-val 0.09963 best 0.09963@12750 (574s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:26 step 13000 train 0.09951 inner-val 0.09962 best 0.09962@13000 (585s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:37 step 13250 train 0.09950 inner-val 0.09962 best 0.09962@13250 (595s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:47 step 13500 train 0.09936 inner-val 0.09961 best 0.09961@13500 (606s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:54:57 step 13750 train 0.09945 inner-val 0.09961 best 0.09961@13750 (616s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:55:08 step 14000 train 0.09930 inner-val 0.09960 best 0.09960@14000 (626s) immicro=0.0982 imperial_eskd=0.0987 liu2021=0.1020
+2026-10-03T01:55:18 step 14250 train 0.09920 inner-val 0.09959 best 0.09959@14250 (636s) immicro=0.0982 imperial_eskd=0.0986 liu2021=0.1020
+2026-10-03T01:55:28 step 14500 train 0.09953 inner-val 0.09959 best 0.09959@14500 (647s) immicro=0.0982 imperial_eskd=0.0986 liu2021=0.1019
+2026-10-03T01:55:39 step 14750 train 0.09941 inner-val 0.09959 best 0.09959@14750 (657s) immicro=0.0982 imperial_eskd=0.0986 liu2021=0.1019
+2026-10-03T01:55:49 step 15000 train 0.09949 inner-val 0.09958 best 0.09958@15000 (668s) immicro=0.0982 imperial_eskd=0.0986 liu2021=0.1019
+2026-10-03T01:56:01 step 15250 train 0.09926 inner-val 0.09958 best 0.09958@15250 (679s) immicro=0.0982 imperial_eskd=0.0986 liu2021=0.1019
+2026-10-03T01:56:11 step 15500 train 0.09946 inner-val 0.09957 best 0.09957@15500 (690s) immicro=0.0981 imperial_eskd=0.0986 liu2021=0.1019
+2026-10-03T01:56:21 step 15750 train 0.09921 inner-val 0.09957 best 0.09957@15750 (700s) immicro=0.0981 imperial_eskd=0.0986 liu2021=0.1019

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64sat/RUN.json

@@ -0,0 +1,552 @@
+{
+ "schema": "pbisc_sdd.ft_crosscohort.run.v1",
+ "source_checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt",
+ "source_checkpoint_sha256": "8c7cd0e83e2bc19fe03dfcbc084919a1e2f61be8cc8bc12f4caf543dfda91633",
+ "split": "hoL_ye2026_sepsis",
+ "split_file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/splits/hoL_ye2026_sepsis.json",
+ "split_sha256": "39cad7b25faf6c15cd65f0512977b9e526353cfb085dc2c45d18f8edc56db426",
+ "held_out": "ye2026_sepsis",
+ "recipe_k": 1000,
+ "balance": "cohort",
+ "mode": "lora",
+ "trainable": {
+  "mode": "lora",
+  "lora_layers": [
+   "time_encoder.0",
+   "time_encoder.2",
+   "condition_encoder.0",
+   "condition_encoder.3",
+   "context_mixer.2",
+   "down_blocks.0.input_projection",
+   "down_blocks.0.context_projection.1",
+   "down_blocks.1.input_projection",
+   "down_blocks.1.context_projection.1",
+   "down_blocks.2.input_projection",
+   "down_blocks.2.context_projection.1",
+   "down_blocks.3.input_projection",
+   "down_blocks.3.context_projection.1",
+   "up_blocks.0.input_projection",
+   "up_blocks.0.context_projection.1",
+   "up_blocks.1.input_projection",
+   "up_blocks.1.context_projection.1",
+   "up_blocks.2.input_projection",
+   "up_blocks.2.context_projection.1",
+   "output_trunk.0",
+   "value_head",
+   "active_head"
+  ],
+  "lora_rank": 64,
+  "lora_alpha": 64.0,
+  "n_trainable": 5184896,
+  "n_total_base": 54185184,
+  "trainable_tensors": 44
+ },
+ "lr": 0.0005,
+ "warmup": 200,
+ "batch_size": 256,
+ "ema_decay": 0.999,
+ "max_steps": 200000,
+ "steps_run": 59000,
+ "eval_every": 250,
+ "patience": 8,
+ "selected_step": 57000,
+ "selected_inner_val_loss": 0.09907439871305468,
+ "pretrained_inner_val_loss": 0.10827601102716995,
+ "cohorts": [
+  {
+   "cohort": "immicro",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/immicro/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "2be5717d19e86bdf38e4838d4bd2886a211ba78bfbfe749c458305a3e0ac2dcc",
+   "train_rows": [
+    "HS1",
+    "HS2",
+    "HS3",
+    "HS6",
+    "HS7",
+    "HS8",
+    "HS9",
+    "HS10",
+    "HS12",
+    "HS13",
+    "HS14",
+    "HS15",
+    "HS16",
+    "HS17",
+    "HS18",
+    "HS19",
+    "HS20",
+    "HS21",
+    "HS23",
+    "HS24",
+    "HS25",
+    "HS28",
+    "HS29",
+    "HS30",
+    "HS32",
+    "HS34",
+    "HS35",
+    "HS36",
+    "HS37",
+    "HS38",
+    "HS39",
+    "HS40",
+    "HS41",
+    "HS42",
+    "HS43",
+    "HS44",
+    "HS45",
+    "HS47",
+    "HS48",
+    "HS49",
+    "HS50",
+    "HS51",
+    "HS52",
+    "HS53",
+    "HS54",
+    "HS56",
+    "HS57",
+    "HS58",
+    "HS59",
+    "HS61",
+    "HS63",
+    "HS64",
+    "HS65",
+    "HS66",
+    "HS67",
+    "HS68",
+    "HS69",
+    "HS70",
+    "HS71",
+    "HS72",
+    "HS73",
+    "HS74",
+    "HS75",
+    "HS76",
+    "HS77",
+    "HS78",
+    "HS79",
+    "HS80",
+    "HS81",
+    "HS82",
+    "HS83",
+    "HS84",
+    "HS85",
+    "HS86",
+    "HS88",
+    "HS89",
+    "HS90",
+    "HS91",
+    "HS92",
+    "HS93",
+    "HS94",
+    "HS95",
+    "HS96",
+    "HS97",
+    "HS98",
+    "HS100",
+    "HS101",
+    "HS102",
+    "HS103",
+    "HS104",
+    "HS105",
+    "HS106",
+    "HS107",
+    "HS110",
+    "HS111"
+   ],
+   "inner_val_rows": [
+    "HS4",
+    "HS11",
+    "HS26",
+    "HS27",
+    "HS31",
+    "HS46",
+    "HS55",
+    "HS60",
+    "HS62",
+    "HS87",
+    "HS108",
+    "HS109"
+   ]
+  },
+  {
+   "cohort": "imperial_eskd",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/imperial_eskd/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "41a62291e98e367d05108a0a95a859ef9ca1997f1fda4656cba58eeefbe56442",
+   "train_rows": [
+    "C20_1004",
+    "C20_1404",
+    "C21_1004",
+    "C21_1704",
+    "C21_2504",
+    "C23_2204",
+    "C31_1304",
+    "C31_2004",
+    "C31_2704",
+    "C33_1401",
+    "C33_1601",
+    "C33_1901",
+    "C34_1404",
+    "C34_2104",
+    "C34_2504",
+    "C36_1504",
+    "C36_1704",
+    "C36_2104",
+    "C40_1504",
+    "C40_2204",
+    "C40_0105",
+    "C42_1504",
+    "C42_2204",
+    "C42_2704",
+    "C58_1604",
+    "C58_2501",
+    "C58_2901",
+    "C58_0102",
+    "C58_0402",
+    "C58_1003",
+    "C60_2504",
+    "C65_1704",
+    "C65_2104",
+    "C65_2504",
+    "C69_1804",
+    "C82_2504",
+    "C85_2004",
+    "C93_2104",
+    "C93_2704",
+    "C93_0405",
+    "C101_0601",
+    "C101_1101",
+    "C101_1301",
+    "C101_1801",
+    "C101_2001",
+    "C101_2502",
+    "C103_2204",
+    "C104_2204",
+    "C106_2204",
+    "C108_2204",
+    "C108_1501",
+    "C108_1801",
+    "C108_2201",
+    "C113_0605",
+    "C123_2404",
+    "C123_2904",
+    "C123_0505",
+    "C124_2404",
+    "C124_2704",
+    "C124_0105",
+    "C126_2504",
+    "C126_3004",
+    "C126_0605",
+    "C127_2504",
+    "C127_0405",
+    "C127_1105",
+    "C132_2804",
+    "C132_0205",
+    "C132_1105",
+    "C137_3004",
+    "C137_2502",
+    "C138_3004",
+    "C138_2301",
+    "C138_3001",
+    "C138_0202",
+    "C138_0402",
+    "C140_3004",
+    "C140_2801",
+    "C140_3001",
+    "C140_0202",
+    "C140_0402",
+    "C140_0602",
+    "C140_0902",
+    "C140_1103",
+    "C141_2001",
+    "C141_2201",
+    "C141_3001",
+    "C141_0202",
+    "C145_3004",
+    "C145_0701",
+    "C145_1201",
+    "C145_1401",
+    "C145_1901",
+    "C145_2301",
+    "C146_3004",
+    "C146_1201",
+    "C146_1401",
+    "C146_1601",
+    "C146_1901",
+    "C146_2301",
+    "C146_2701",
+    "C146_2502",
+    "C152_0405",
+    "C161_1105",
+    "C164_1105",
+    "C167_1205",
+    "C168_1205",
+    "C168_2001",
+    "C168_2301",
+    "C168_3001",
+    "C168_0202",
+    "C168_0403",
+    "C169_1205",
+    "C169_2001",
+    "C169_2301",
+    "C169_0202",
+    "C169_0403",
+    "C170_1205",
+    "C170_2301",
+    "C170_0202",
+    "C170_0402",
+    "C170_0403",
+    "C187_2605",
+    "C187_1201",
+    "C187_1401",
+    "C187_1901",
+    "C187_2301",
+    "C187_2502",
+    "C190_2605",
+    "C190_2901",
+    "C190_0102",
+    "C190_0302",
+    "C190_0502",
+    "C190_0802",
+    "C190_1002",
+    "C190_0903",
+    "C193_2605",
+    "C195_2605",
+    "C197_2605",
+    "C212_2705",
+    "C214_2705",
+    "C237_2805",
+    "C238_2905",
+    "C240_2905",
+    "C246_2905",
+    "C251_2905"
+   ],
+   "inner_val_rows": [
+    "C63_1704",
+    "C63_2404",
+    "C63_2904",
+    "C128_2504",
+    "C128_0405",
+    "C128_1105",
+    "C142_3004",
+    "C147_3004",
+    "C147_1401",
+    "C147_1901",
+    "C234_2805"
+   ]
+  },
+  {
+   "cohort": "giroux2022",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/giroux2022/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "b584655fcfa5fa4c0887d5dd83fcb1c5a0206da4afe5fefab96cba2272b1371c",
+   "train_rows": [
+    "450905_d0",
+    "450905_d14",
+    "0B943B_d0",
+    "0B943B_d14",
+    "7085CA_d0",
+    "7085CA_d7",
+    "7085CA_d14",
+    "2AD75E_d0",
+    "2AD75E_d7",
+    "2AD75E_d14",
+    "B85D75_d0",
+    "B85D75_d7",
+    "180E1A_d0",
+    "180E1A_d7",
+    "180E1A_d14",
+    "82CCF5_d0",
+    "82CCF5_d7",
+    "82CCF5_d14",
+    "BAAF62_d0",
+    "BAAF62_d7",
+    "BAAF62_d14",
+    "0BF51C_d0",
+    "0BF51C_d7",
+    "0BF51C_d14"
+   ],
+   "inner_val_rows": []
+  },
+  {
+   "cohort": "liu2021",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/liu2021/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "ad3131eabcfb11b166ca37c7585c50abb11efb60e2a736fe62de604794535e0c",
+   "train_rows": [
+    "AA220014_HC",
+    "AA220534_HC",
+    "AA220907_HC",
+    "HGR0000071_T0",
+    "HGR0000074_T0",
+    "HGR0000083_T1",
+    "HGR0000089_T1",
+    "HGR0000091_T0",
+    "HGR0000093_T0",
+    "HGR0000136_T0",
+    "HGR0000137_T0",
+    "HGR0000141_T0",
+    "IM000040_HC",
+    "HGR0000042_T1",
+    "HGR0000069_T0",
+    "HGR0000069_T1",
+    "HGR0000092_T0",
+    "HGR0000094_T0",
+    "HGR0000124_T0",
+    "HGR0000144_T2",
+    "HGR0000391_T0",
+    "HGR0000395_T0",
+    "HGR0000418_T1",
+    "SHD4_HC",
+    "SHD7_HC",
+    "HGR0000051_T0",
+    "HGR0000051_T1",
+    "HGR0000051_T3",
+    "HGR0000101_T1",
+    "HGR0000101_T3",
+    "HGR0000102_T0",
+    "HGR0000102_T1",
+    "HGR0000134_T0",
+    "HGR0000135_T0",
+    "HGR0000135_T1",
+    "HGR0000392_T0",
+    "HGR0000429_T1",
+    "HGR0000429_T2",
+    "SHD3_HC",
+    "SHD5_HC"
+   ],
+   "inner_val_rows": [
+    "HGR0000098_T0",
+    "HGR0000143_T0",
+    "HGR0000115_T1",
+    "SHD1_HC"
+   ]
+  }
+ ],
+ "held_out_test_rows_never_loaded": [
+  "Abd-PS04",
+  "Abd-PS05",
+  "Abd-PS07",
+  "Abd-PS19",
+  "Abd-PS20",
+  "Abd-PS23",
+  "Abd-PS27",
+  "Abd-S102",
+  "Abd-S103",
+  "Abd-S120",
+  "Abd-S123",
+  "Abd-S156",
+  "Abd-S39",
+  "Abd-S42",
+  "Abd-S47",
+  "Abd-S51",
+  "Abd-S54",
+  "Abd-S57",
+  "Abd-S66",
+  "Abd-S79",
+  "Abd-S83",
+  "Abd-S98",
+  "HC01",
+  "HC05",
+  "HC07",
+  "HC09",
+  "HC12",
+  "HC13",
+  "HC16",
+  "IC10",
+  "IC15",
+  "IC16",
+  "IC19",
+  "IC22",
+  "IC23",
+  "PHC01",
+  "PHC02",
+  "PHC03",
+  "PHC05",
+  "PIC01",
+  "PIC02",
+  "PIC04",
+  "PIC05",
+  "Res-PS02",
+  "Res-PS03",
+  "Res-PS12",
+  "Res-PS16",
+  "Res-PS21",
+  "Res-PS22",
+  "Res-PS25",
+  "Res-PS26",
+  "Res-S10",
+  "Res-S125",
+  "Res-S127",
+  "Res-S136",
+  "Res-S139",
+  "Res-S146",
+  "Res-S149",
+  "Res-S150",
+  "Res-S164",
+  "Res-S166",
+  "Res-S174",
+  "Res-S177",
+  "Res-S184",
+  "Res-S19",
+  "Res-S26",
+  "Res-S40",
+  "Res-S77",
+  "Res-S87",
+  "Skin-S140",
+  "Skin-S142",
+  "Skin-S152",
+  "Skin-S38",
+  "Skin-S43",
+  "Skin-S46",
+  "Skin-S52",
+  "Skin-S74",
+  "Uri-S134",
+  "Uri-S147",
+  "Uri-S157",
+  "Uri-S20",
+  "Uri-S93"
+ ],
+ "max_abs_weight_change": 32.55881881713867,
+ "n_tensors_changed": 22,
+ "seed": 20261002,
+ "wall_seconds": 2653.6,
+ "smoke": false,
+ "block": 20000,
+ "saturation_frac": 0.01,
+ "stop_reason": "early_stop",
+ "milestones": [
+  {
+   "milestone_step": 20000,
+   "selected_step": 20000,
+   "selected_inner_val_loss": 0.09948734277765257,
+   "block_improvement": 0.008788668249517378,
+   "total_improvement": 0.008788668249517378,
+   "reason": "block_end",
+   "checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64sat/checkpoint_ft_s20000.pt",
+   "checkpoint_sha256": "86adfd23e1cac07cf2fe066b4d8dcf912d2c5b7439f8df9bdc7dbca12f8e176e"
+  },
+  {
+   "milestone_step": 40000,
+   "selected_step": 40000,
+   "selected_inner_val_loss": 0.09921769783691024,
+   "block_improvement": 0.0002696449407423307,
+   "total_improvement": 0.00905831319025971,
+   "reason": "block_end",
+   "checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64sat/checkpoint_ft_s40000.pt",
+   "checkpoint_sha256": "9c45ad32993702c16f072cb340e345fef38bff4228633463bdccbfd3ab200340"
+  },
+  {
+   "milestone_step": 59000,
+   "selected_step": 57000,
+   "selected_inner_val_loss": 0.09907439871305468,
+   "block_improvement": 0.0001432991238555642,
+   "total_improvement": 0.009201612314115273,
+   "reason": "early_stop",
+   "checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora64sat/checkpoint_ft_s59000.pt",
+   "checkpoint_sha256": "1702ed8fcc77d0dfa0e7bc16bc4dd86f17def0e5c9aaf95d24d25c8ef9b3ad5b"
+  }
+ ],
+ "written_utc": "2026-10-02T22:40:31.358969+00:00",
+ "last_milestone_max_abs_diff": 0.0,
+ "checkpoint_sha256": "b402a1fd359b586d598aeb6da7845e019ddec0e0e0ec616a7934d40eaaa9b9b1",
+ "reload_forward_max_abs_diff": 0.0
+}