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_ji2025_chili/K1000/lora16sat/train.log

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+2026-10-03T02:33:52 split=hoL_ji2025_chili K=1000 mode=lora trainable=1,296,224 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; ye2026_sepsis: train 73 rows / 529,816 cells, inner 9; 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
+2026-10-03T02:33:54 step 0 inner-val loss 0.11207 (pretrained) {'immicro': 0.10636159498244524, 'ye2026_sepsis': 0.12489082664251328, 'imperial_eskd': 0.10889218679883263, 'liu2021': 0.1081176083534956}
+2026-10-03T02:34:05 step 250 train 0.10578 inner-val 0.11139 best 0.11139@250 (11s) immicro=0.1058 ye2026_sepsis=0.1243 imperial_eskd=0.1081 liu2021=0.1075
+2026-10-03T02:34:16 step 500 train 0.10326 inner-val 0.10979 best 0.10979@500 (22s) immicro=0.1044 ye2026_sepsis=0.1226 imperial_eskd=0.1063 liu2021=0.1058
+2026-10-03T02:34:28 step 750 train 0.10287 inner-val 0.10798 best 0.10798@750 (34s) immicro=0.1031 ye2026_sepsis=0.1205 imperial_eskd=0.1042 liu2021=0.1041
+2026-10-03T02:34:40 step 1000 train 0.10294 inner-val 0.10644 best 0.10644@1000 (46s) immicro=0.1021 ye2026_sepsis=0.1184 imperial_eskd=0.1025 liu2021=0.1028
+2026-10-03T02:34:52 step 1250 train 0.10286 inner-val 0.10537 best 0.10537@1250 (58s) immicro=0.1012 ye2026_sepsis=0.1168 imperial_eskd=0.1014 liu2021=0.1020
+2026-10-03T02:35:05 step 1500 train 0.10246 inner-val 0.10470 best 0.10470@1500 (70s) immicro=0.1006 ye2026_sepsis=0.1159 imperial_eskd=0.1008 liu2021=0.1015
+2026-10-03T02:35:17 step 1750 train 0.10249 inner-val 0.10428 best 0.10428@1750 (82s) immicro=0.1001 ye2026_sepsis=0.1153 imperial_eskd=0.1004 liu2021=0.1012
+2026-10-03T02:35:29 step 2000 train 0.10213 inner-val 0.10400 best 0.10400@2000 (94s) immicro=0.0998 ye2026_sepsis=0.1150 imperial_eskd=0.1002 liu2021=0.1011
+2026-10-03T02:35:41 step 2250 train 0.10233 inner-val 0.10381 best 0.10381@2250 (107s) immicro=0.0996 ye2026_sepsis=0.1147 imperial_eskd=0.1000 liu2021=0.1010
+2026-10-03T02:35:53 step 2500 train 0.10232 inner-val 0.10367 best 0.10367@2500 (118s) immicro=0.0994 ye2026_sepsis=0.1146 imperial_eskd=0.0998 liu2021=0.1009
+2026-10-03T02:36:05 step 2750 train 0.10233 inner-val 0.10357 best 0.10357@2750 (131s) immicro=0.0993 ye2026_sepsis=0.1144 imperial_eskd=0.0998 liu2021=0.1008
+2026-10-03T02:36:17 step 3000 train 0.10244 inner-val 0.10350 best 0.10350@3000 (143s) immicro=0.0992 ye2026_sepsis=0.1143 imperial_eskd=0.0997 liu2021=0.1008
+2026-10-03T02:36:29 step 3250 train 0.10251 inner-val 0.10345 best 0.10345@3250 (155s) immicro=0.0991 ye2026_sepsis=0.1143 imperial_eskd=0.0996 liu2021=0.1008
+2026-10-03T02:36:41 step 3500 train 0.10224 inner-val 0.10340 best 0.10340@3500 (167s) immicro=0.0990 ye2026_sepsis=0.1142 imperial_eskd=0.0996 liu2021=0.1008
+2026-10-03T02:36:53 step 3750 train 0.10221 inner-val 0.10337 best 0.10337@3750 (179s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0996 liu2021=0.1007
+2026-10-03T02:37:05 step 4000 train 0.10229 inner-val 0.10334 best 0.10334@4000 (191s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:37:17 step 4250 train 0.10248 inner-val 0.10332 best 0.10332@4250 (203s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:37:29 step 4500 train 0.10189 inner-val 0.10330 best 0.10330@4500 (215s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:37:41 step 4750 train 0.10205 inner-val 0.10328 best 0.10328@4750 (227s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:37:53 step 5000 train 0.10225 inner-val 0.10327 best 0.10327@5000 (239s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:38:06 step 5250 train 0.10211 inner-val 0.10325 best 0.10325@5250 (251s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T02:38:18 step 5500 train 0.10192 inner-val 0.10324 best 0.10324@5500 (264s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:38:30 step 5750 train 0.10204 inner-val 0.10323 best 0.10323@5750 (276s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:38:42 step 6000 train 0.10227 inner-val 0.10322 best 0.10322@6000 (288s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:38:54 step 6250 train 0.10230 inner-val 0.10321 best 0.10321@6250 (300s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:39:06 step 6500 train 0.10201 inner-val 0.10321 best 0.10321@6500 (312s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:39:18 step 6750 train 0.10199 inner-val 0.10320 best 0.10320@6750 (324s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:39:30 step 7000 train 0.10211 inner-val 0.10319 best 0.10319@7000 (336s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:39:42 step 7250 train 0.10215 inner-val 0.10318 best 0.10318@7250 (348s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-03T02:39:54 step 7500 train 0.10208 inner-val 0.10318 best 0.10318@7500 (360s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T02:40:07 step 7750 train 0.10206 inner-val 0.10317 best 0.10317@7750 (372s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T02:40:19 step 8000 train 0.10176 inner-val 0.10317 best 0.10317@8000 (385s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T02:40:31 step 8250 train 0.10206 inner-val 0.10316 best 0.10316@8250 (397s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T02:40:44 step 8500 train 0.10170 inner-val 0.10316 best 0.10316@8500 (409s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T02:40:56 step 8750 train 0.10224 inner-val 0.10315 best 0.10315@8750 (421s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:41:08 step 9000 train 0.10217 inner-val 0.10315 best 0.10315@9000 (434s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:41:20 step 9250 train 0.10210 inner-val 0.10314 best 0.10314@9250 (446s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:41:32 step 9500 train 0.10174 inner-val 0.10314 best 0.10314@9500 (458s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:41:45 step 9750 train 0.10168 inner-val 0.10314 best 0.10314@9750 (470s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:41:57 step 10000 train 0.10180 inner-val 0.10314 best 0.10314@10000 (482s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:42:08 step 10250 train 0.10190 inner-val 0.10313 best 0.10313@10250 (494s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:42:18 step 10500 train 0.10201 inner-val 0.10313 best 0.10313@10500 (504s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:42:29 step 10750 train 0.10181 inner-val 0.10313 best 0.10313@10750 (514s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:42:39 step 11000 train 0.10200 inner-val 0.10312 best 0.10312@11000 (525s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:42:50 step 11250 train 0.10161 inner-val 0.10312 best 0.10312@11250 (536s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:00 step 11500 train 0.10193 inner-val 0.10312 best 0.10312@11500 (546s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:11 step 11750 train 0.10193 inner-val 0.10311 best 0.10311@11750 (557s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:21 step 12000 train 0.10151 inner-val 0.10311 best 0.10311@12000 (567s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:32 step 12250 train 0.10195 inner-val 0.10311 best 0.10311@12000 (578s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:42 step 12500 train 0.10182 inner-val 0.10311 best 0.10311@12000 (588s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:43:53 step 12750 train 0.10191 inner-val 0.10312 best 0.10311@12000 (598s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:44:03 step 13000 train 0.10211 inner-val 0.10311 best 0.10311@12000 (609s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:44:14 step 13250 train 0.10196 inner-val 0.10311 best 0.10311@13250 (620s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:44:24 step 13500 train 0.10179 inner-val 0.10311 best 0.10311@13500 (630s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:44:35 step 13750 train 0.10206 inner-val 0.10310 best 0.10310@13750 (641s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T02:44:45 step 14000 train 0.10163 inner-val 0.10310 best 0.10310@14000 (651s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1005
+2026-10-03T02:44:56 step 14250 train 0.10164 inner-val 0.10310 best 0.10310@14250 (662s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:06 step 14500 train 0.10170 inner-val 0.10309 best 0.10309@14500 (672s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:17 step 14750 train 0.10187 inner-val 0.10309 best 0.10309@14750 (682s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:27 step 15000 train 0.10196 inner-val 0.10309 best 0.10309@15000 (693s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:38 step 15250 train 0.10168 inner-val 0.10309 best 0.10309@15250 (704s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:49 step 15500 train 0.10220 inner-val 0.10308 best 0.10308@15500 (714s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:45:59 step 15750 train 0.10169 inner-val 0.10308 best 0.10308@15750 (725s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:46:10 step 16000 train 0.10160 inner-val 0.10308 best 0.10308@15750 (736s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:46:21 step 16250 train 0.10189 inner-val 0.10308 best 0.10308@16250 (747s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:46:33 step 16500 train 0.10181 inner-val 0.10307 best 0.10307@16500 (759s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:46:46 step 16750 train 0.10181 inner-val 0.10307 best 0.10307@16500 (771s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:46:57 step 17000 train 0.10196 inner-val 0.10307 best 0.10307@17000 (783s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:47:09 step 17250 train 0.10200 inner-val 0.10307 best 0.10307@17250 (795s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:47:21 step 17500 train 0.10172 inner-val 0.10306 best 0.10306@17500 (807s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:47:33 step 17750 train 0.10152 inner-val 0.10306 best 0.10306@17750 (819s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:47:45 step 18000 train 0.10205 inner-val 0.10306 best 0.10306@17750 (831s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:47:57 step 18250 train 0.10159 inner-val 0.10306 best 0.10306@18250 (843s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:48:09 step 18500 train 0.10148 inner-val 0.10306 best 0.10306@18250 (855s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:48:21 step 18750 train 0.10181 inner-val 0.10306 best 0.10306@18750 (866s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:48:33 step 19000 train 0.10185 inner-val 0.10306 best 0.10306@18750 (878s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:48:44 step 19250 train 0.10184 inner-val 0.10306 best 0.10306@19250 (890s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:48:56 step 19500 train 0.10163 inner-val 0.10305 best 0.10305@19500 (902s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:49:08 step 19750 train 0.10159 inner-val 0.10305 best 0.10305@19750 (914s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:49:20 step 20000 train 0.10176 inner-val 0.10305 best 0.10305@20000 (926s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:49:22 block end 20000: best 0.10305@20000; block improvement 0.009017 (100.00 % of total 0.009017) -> continue
+2026-10-03T02:49:34 step 20250 train 0.10166 inner-val 0.10305 best 0.10305@20000 (940s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:49:46 step 20500 train 0.10145 inner-val 0.10304 best 0.10304@20500 (952s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:49:58 step 20750 train 0.10178 inner-val 0.10304 best 0.10304@20750 (963s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:50:09 step 21000 train 0.10180 inner-val 0.10304 best 0.10304@20750 (975s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:50:21 step 21250 train 0.10165 inner-val 0.10304 best 0.10304@21250 (987s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:50:33 step 21500 train 0.10152 inner-val 0.10304 best 0.10304@21500 (999s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:50:45 step 21750 train 0.10169 inner-val 0.10304 best 0.10304@21500 (1011s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:50:57 step 22000 train 0.10192 inner-val 0.10304 best 0.10304@21500 (1023s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:51:09 step 22250 train 0.10166 inner-val 0.10304 best 0.10304@22250 (1035s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:51:21 step 22500 train 0.10185 inner-val 0.10304 best 0.10304@22250 (1047s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:51:33 step 22750 train 0.10164 inner-val 0.10304 best 0.10304@22750 (1058s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:51:45 step 23000 train 0.10207 inner-val 0.10304 best 0.10304@22750 (1070s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:51:57 step 23250 train 0.10183 inner-val 0.10304 best 0.10304@22750 (1082s) immicro=0.0985 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:52:08 step 23500 train 0.10162 inner-val 0.10303 best 0.10303@23500 (1094s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:52:20 step 23750 train 0.10164 inner-val 0.10303 best 0.10303@23750 (1106s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:52:32 step 24000 train 0.10183 inner-val 0.10303 best 0.10303@24000 (1118s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:52:44 step 24250 train 0.10151 inner-val 0.10303 best 0.10303@24250 (1129s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:52:56 step 24500 train 0.10136 inner-val 0.10303 best 0.10303@24500 (1141s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:53:07 step 24750 train 0.10145 inner-val 0.10302 best 0.10302@24750 (1153s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:53:19 step 25000 train 0.10169 inner-val 0.10302 best 0.10302@25000 (1165s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:53:31 step 25250 train 0.10162 inner-val 0.10302 best 0.10302@25000 (1177s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:53:43 step 25500 train 0.10171 inner-val 0.10302 best 0.10302@25000 (1189s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:53:55 step 25750 train 0.10169 inner-val 0.10302 best 0.10302@25000 (1200s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T02:54:07 step 26000 train 0.10176 inner-val 0.10302 best 0.10302@26000 (1212s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:54:19 step 26250 train 0.10180 inner-val 0.10302 best 0.10302@26250 (1224s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:54:31 step 26500 train 0.10184 inner-val 0.10301 best 0.10301@26500 (1236s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:54:43 step 26750 train 0.10154 inner-val 0.10301 best 0.10301@26750 (1249s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:54:55 step 27000 train 0.10196 inner-val 0.10301 best 0.10301@27000 (1261s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:55:07 step 27250 train 0.10187 inner-val 0.10300 best 0.10300@27250 (1272s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:55:18 step 27500 train 0.10165 inner-val 0.10300 best 0.10300@27500 (1284s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:55:30 step 27750 train 0.10163 inner-val 0.10300 best 0.10300@27500 (1296s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:55:42 step 28000 train 0.10181 inner-val 0.10300 best 0.10300@28000 (1308s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:55:54 step 28250 train 0.10158 inner-val 0.10300 best 0.10300@28250 (1320s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:56:06 step 28500 train 0.10163 inner-val 0.10300 best 0.10300@28500 (1332s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:56:18 step 28750 train 0.10155 inner-val 0.10300 best 0.10300@28500 (1344s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:56:30 step 29000 train 0.10147 inner-val 0.10300 best 0.10300@28500 (1355s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:56:42 step 29250 train 0.10170 inner-val 0.10300 best 0.10300@29250 (1367s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:56:54 step 29500 train 0.10126 inner-val 0.10300 best 0.10300@29250 (1380s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:57:06 step 29750 train 0.10162 inner-val 0.10300 best 0.10300@29250 (1391s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:57:18 step 30000 train 0.10171 inner-val 0.10300 best 0.10300@29250 (1403s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:57:30 step 30250 train 0.10144 inner-val 0.10300 best 0.10300@29250 (1415s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:57:41 step 30500 train 0.10142 inner-val 0.10300 best 0.10300@29250 (1427s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:57:53 step 30750 train 0.10157 inner-val 0.10299 best 0.10300@29250 (1439s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:58:05 step 31000 train 0.10153 inner-val 0.10299 best 0.10299@31000 (1451s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:58:17 step 31250 train 0.10188 inner-val 0.10299 best 0.10299@31250 (1463s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:58:29 step 31500 train 0.10187 inner-val 0.10299 best 0.10299@31250 (1475s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:58:40 step 31750 train 0.10188 inner-val 0.10299 best 0.10299@31750 (1486s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T02:58:50 step 32000 train 0.10142 inner-val 0.10298 best 0.10298@32000 (1496s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
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+2026-10-03T02:59:12 step 32500 train 0.10140 inner-val 0.10298 best 0.10298@32500 (1517s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
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+2026-10-03T02:59:33 step 33000 train 0.10154 inner-val 0.10297 best 0.10297@32750 (1538s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:59:43 step 33250 train 0.10172 inner-val 0.10297 best 0.10297@32750 (1549s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T02:59:54 step 33500 train 0.10158 inner-val 0.10297 best 0.10297@33500 (1559s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:00:04 step 33750 train 0.10141 inner-val 0.10297 best 0.10297@33500 (1570s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:00:14 step 34000 train 0.10180 inner-val 0.10297 best 0.10297@33500 (1580s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:00:25 step 34250 train 0.10192 inner-val 0.10297 best 0.10297@33500 (1591s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1004
+2026-10-03T03:00:36 step 34500 train 0.10174 inner-val 0.10297 best 0.10297@33500 (1601s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:00:46 step 34750 train 0.10159 inner-val 0.10297 best 0.10297@33500 (1612s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:00:57 step 35000 train 0.10167 inner-val 0.10297 best 0.10297@35000 (1622s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:01:07 step 35250 train 0.10163 inner-val 0.10297 best 0.10297@35000 (1633s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:01:18 step 35500 train 0.10176 inner-val 0.10297 best 0.10297@35500 (1643s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:01:28 step 35750 train 0.10164 inner-val 0.10297 best 0.10297@35500 (1654s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:01:38 step 36000 train 0.10166 inner-val 0.10297 best 0.10297@35500 (1664s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:01:49 step 36250 train 0.10169 inner-val 0.10297 best 0.10297@36250 (1675s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:02:00 step 36500 train 0.10163 inner-val 0.10296 best 0.10296@36500 (1685s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:02:10 step 36750 train 0.10164 inner-val 0.10296 best 0.10296@36500 (1696s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:02:21 step 37000 train 0.10135 inner-val 0.10296 best 0.10296@36500 (1706s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:02:31 step 37250 train 0.10134 inner-val 0.10296 best 0.10296@37250 (1717s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1003
+2026-10-03T03:02:42 step 37500 train 0.10147 inner-val 0.10296 best 0.10296@37250 (1727s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:02:52 step 37750 train 0.10145 inner-val 0.10296 best 0.10296@37250 (1738s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:02 step 38000 train 0.10130 inner-val 0.10296 best 0.10296@37250 (1748s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:13 step 38250 train 0.10171 inner-val 0.10296 best 0.10296@38250 (1758s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:23 step 38500 train 0.10201 inner-val 0.10296 best 0.10296@38250 (1769s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:34 step 38750 train 0.10170 inner-val 0.10296 best 0.10296@38250 (1780s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:44 step 39000 train 0.10143 inner-val 0.10296 best 0.10296@38250 (1790s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:03:55 step 39250 train 0.10151 inner-val 0.10296 best 0.10296@38250 (1801s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:04:06 step 39500 train 0.10176 inner-val 0.10297 best 0.10296@38250 (1811s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:04:16 step 39750 train 0.10156 inner-val 0.10297 best 0.10296@38250 (1822s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:04:26 step 40000 train 0.10165 inner-val 0.10297 best 0.10296@38250 (1832s) immicro=0.0985 ye2026_sepsis=0.1139 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T03:04:28 block end 40000: best 0.10296@38250; block improvement 0.000089 (0.98 % of total 0.009106) -> saturated, stop
+2026-10-03T03:04:31 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora16sat/checkpoint_ft.pt (selected step 38250, inner-val 0.10296 vs pretrained 0.11207); sha d07c221e650c

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora64/RUN.json

@@ -0,0 +1,558 @@
+{
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+ "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_ji2025_chili",
+ "split_file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/splits/hoL_ji2025_chili.json",
+ "split_sha256": "1efe2e8a540137380f689ea29d64f99189981b8fcc636824c7ae858db989f787",
+ "held_out": "ji2025_chili",
+ "recipe_k": 1000,
+ "balance": "cohort",
+ "mode": "lora",
+ "trainable": {
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+  "lora_layers": [
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+   "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"
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+  "lora_rank": 64,
+  "lora_alpha": 64.0,
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+  "n_total_base": 54185184,
+  "trainable_tensors": 44
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+ "lr": 0.0005,
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+ "selected_inner_val_loss": 0.1027188166279835,
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+   "inner_val_rows": [
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+    "HS109"
+   ]
+  },
+  {
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+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/ye2026_sepsis/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "ce8b61d7dcd3e62b9e63d52189cfb5bba881f87a661384277a7949a629231cb4",
+   "train_rows": [
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+   "inner_val_rows": [
+    "Abd-PS23",
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+    "HC01",
+    "IC19",
+    "Res-PS16",
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+   ]
+  },
+  {
+   "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": [
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora64/history.json

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+ {
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+  "sec": 961.9
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+ {
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+  "inner_val_loss": 0.1027188166279835,
+  "inner_val_per_cohort": {
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+   "ye2026_sepsis": 0.11362554215722614,
+   "imperial_eskd": 0.09893400459126993,
+   "liu2021": 0.100155308842659
+  },
+  "train_loss": 0.10139589926600456,
+  "sec": 974.2
+ }
+]

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora64/train.log

@@ -0,0 +1,83 @@
+2026-10-03T01:25:57 split=hoL_ji2025_chili K=1000 mode=lora trainable=5,184,896 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; ye2026_sepsis: train 73 rows / 529,816 cells, inner 9; 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
+2026-10-03T01:25:59 step 0 inner-val loss 0.11207 (pretrained) {'immicro': 0.10636159498244524, 'ye2026_sepsis': 0.12489082664251328, 'imperial_eskd': 0.10889218679883263, 'liu2021': 0.1081176083534956}
+2026-10-03T01:26:10 step 250 train 0.10514 inner-val 0.11108 best 0.11108@250 (11s) immicro=0.1055 ye2026_sepsis=0.1240 imperial_eskd=0.1078 liu2021=0.1071
+2026-10-03T01:26:21 step 500 train 0.10291 inner-val 0.10926 best 0.10926@500 (22s) immicro=0.1040 ye2026_sepsis=0.1220 imperial_eskd=0.1057 liu2021=0.1054
+2026-10-03T01:26:32 step 750 train 0.10261 inner-val 0.10736 best 0.10736@750 (33s) immicro=0.1026 ye2026_sepsis=0.1196 imperial_eskd=0.1035 liu2021=0.1037
+2026-10-03T01:26:43 step 1000 train 0.10272 inner-val 0.10587 best 0.10587@1000 (44s) immicro=0.1016 ye2026_sepsis=0.1175 imperial_eskd=0.1019 liu2021=0.1025
+2026-10-03T01:26:56 step 1250 train 0.10265 inner-val 0.10493 best 0.10493@1250 (56s) immicro=0.1008 ye2026_sepsis=0.1162 imperial_eskd=0.1009 liu2021=0.1017
+2026-10-03T01:27:08 step 1500 train 0.10225 inner-val 0.10434 best 0.10434@1500 (69s) immicro=0.1002 ye2026_sepsis=0.1154 imperial_eskd=0.1004 liu2021=0.1013
+2026-10-03T01:27:21 step 1750 train 0.10229 inner-val 0.10396 best 0.10396@1750 (81s) immicro=0.0998 ye2026_sepsis=0.1149 imperial_eskd=0.1001 liu2021=0.1011
+2026-10-03T01:27:32 step 2000 train 0.10193 inner-val 0.10371 best 0.10371@2000 (92s) immicro=0.0995 ye2026_sepsis=0.1146 imperial_eskd=0.0998 liu2021=0.1009
+2026-10-03T01:27:44 step 2250 train 0.10215 inner-val 0.10355 best 0.10355@2250 (105s) immicro=0.0993 ye2026_sepsis=0.1144 imperial_eskd=0.0997 liu2021=0.1008
+2026-10-03T01:27:56 step 2500 train 0.10213 inner-val 0.10342 best 0.10342@2500 (117s) immicro=0.0991 ye2026_sepsis=0.1142 imperial_eskd=0.0996 liu2021=0.1008
+2026-10-03T01:28:09 step 2750 train 0.10214 inner-val 0.10334 best 0.10334@2750 (130s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T01:28:21 step 3000 train 0.10225 inner-val 0.10328 best 0.10328@3000 (142s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-03T01:28:34 step 3250 train 0.10233 inner-val 0.10324 best 0.10324@3250 (154s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T01:28:46 step 3500 train 0.10205 inner-val 0.10320 best 0.10320@3500 (167s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T01:28:58 step 3750 train 0.10202 inner-val 0.10317 best 0.10317@3750 (179s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T01:29:11 step 4000 train 0.10210 inner-val 0.10315 best 0.10315@4000 (191s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-03T01:29:23 step 4250 train 0.10229 inner-val 0.10313 best 0.10313@4250 (204s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T01:29:35 step 4500 train 0.10168 inner-val 0.10311 best 0.10311@4500 (216s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T01:29:48 step 4750 train 0.10184 inner-val 0.10310 best 0.10310@4750 (229s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-03T01:30:00 step 5000 train 0.10204 inner-val 0.10308 best 0.10308@5000 (241s) immicro=0.0987 ye2026_sepsis=0.1138 imperial_eskd=0.0993 liu2021=0.1005
+2026-10-03T01:30:13 step 5250 train 0.10191 inner-val 0.10307 best 0.10307@5250 (253s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0993 liu2021=0.1005
+2026-10-03T01:30:25 step 5500 train 0.10172 inner-val 0.10305 best 0.10305@5500 (266s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:30:38 step 5750 train 0.10181 inner-val 0.10304 best 0.10304@5750 (278s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:30:50 step 6000 train 0.10203 inner-val 0.10303 best 0.10303@6000 (291s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:31:02 step 6250 train 0.10207 inner-val 0.10302 best 0.10302@6250 (303s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:31:15 step 6500 train 0.10179 inner-val 0.10302 best 0.10302@6500 (316s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:31:27 step 6750 train 0.10176 inner-val 0.10301 best 0.10301@6750 (328s) immicro=0.0986 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
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+2026-10-03T01:31:52 step 7250 train 0.10191 inner-val 0.10299 best 0.10299@7250 (353s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-03T01:32:04 step 7500 train 0.10184 inner-val 0.10298 best 0.10298@7500 (365s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1005
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+2026-10-03T01:32:29 step 8000 train 0.10151 inner-val 0.10297 best 0.10297@8000 (390s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:32:42 step 8250 train 0.10181 inner-val 0.10296 best 0.10296@8250 (403s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:32:54 step 8500 train 0.10145 inner-val 0.10296 best 0.10296@8500 (415s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:33:07 step 8750 train 0.10200 inner-val 0.10295 best 0.10295@8750 (428s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:33:19 step 9000 train 0.10191 inner-val 0.10294 best 0.10294@9000 (440s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:33:32 step 9250 train 0.10184 inner-val 0.10293 best 0.10293@9250 (452s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:33:44 step 9500 train 0.10146 inner-val 0.10293 best 0.10293@9500 (465s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
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+2026-10-03T01:34:09 step 10000 train 0.10154 inner-val 0.10292 best 0.10292@10000 (490s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:34:21 step 10250 train 0.10164 inner-val 0.10291 best 0.10291@10250 (502s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:34:33 step 10500 train 0.10175 inner-val 0.10291 best 0.10291@10500 (514s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
+2026-10-03T01:34:46 step 10750 train 0.10154 inner-val 0.10290 best 0.10290@10750 (527s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1004
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+2026-10-03T01:36:38 step 13000 train 0.10181 inner-val 0.10286 best 0.10286@13000 (638s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
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+2026-10-03T01:37:03 step 13500 train 0.10150 inner-val 0.10284 best 0.10284@13500 (663s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
+2026-10-03T01:37:15 step 13750 train 0.10177 inner-val 0.10284 best 0.10284@13750 (676s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
+2026-10-03T01:37:28 step 14000 train 0.10133 inner-val 0.10283 best 0.10283@14000 (688s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
+2026-10-03T01:37:40 step 14250 train 0.10135 inner-val 0.10283 best 0.10283@14250 (701s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
+2026-10-03T01:37:52 step 14500 train 0.10138 inner-val 0.10282 best 0.10282@14500 (713s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
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+2026-10-03T01:38:26 step 15250 train 0.10137 inner-val 0.10281 best 0.10281@15250 (747s) immicro=0.0983 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1003
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+2026-10-03T01:38:59 step 16000 train 0.10129 inner-val 0.10279 best 0.10279@16000 (779s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1002
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+2026-10-03T01:39:45 step 17000 train 0.10163 inner-val 0.10277 best 0.10277@17000 (826s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1002
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+2026-10-03T01:40:10 step 17500 train 0.10140 inner-val 0.10276 best 0.10276@17500 (850s) immicro=0.0982 ye2026_sepsis=0.1136 imperial_eskd=0.0990 liu2021=0.1002
+2026-10-03T01:40:22 step 17750 train 0.10119 inner-val 0.10275 best 0.10275@17750 (863s) immicro=0.0982 ye2026_sepsis=0.1136 imperial_eskd=0.0990 liu2021=0.1002
+2026-10-03T01:40:35 step 18000 train 0.10172 inner-val 0.10275 best 0.10275@18000 (875s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0990 liu2021=0.1002
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+2026-10-03T01:41:12 step 18750 train 0.10147 inner-val 0.10275 best 0.10275@18750 (912s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:41:24 step 19000 train 0.10153 inner-val 0.10274 best 0.10274@19000 (925s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:41:36 step 19250 train 0.10149 inner-val 0.10274 best 0.10274@19250 (937s) immicro=0.0982 ye2026_sepsis=0.1137 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:41:49 step 19500 train 0.10127 inner-val 0.10273 best 0.10273@19500 (950s) immicro=0.0982 ye2026_sepsis=0.1136 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:42:01 step 19750 train 0.10124 inner-val 0.10272 best 0.10272@19750 (962s) immicro=0.0982 ye2026_sepsis=0.1136 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:42:14 step 20000 train 0.10140 inner-val 0.10272 best 0.10272@20000 (974s) immicro=0.0982 ye2026_sepsis=0.1136 imperial_eskd=0.0989 liu2021=0.1002
+2026-10-03T01:42:18 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora64/checkpoint_ft.pt (selected step 20000, inner-val 0.10272 vs pretrained 0.11207); sha 7bccb6035b6a