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/lora16/train.log

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+2026-10-02T22:14:02 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
+2026-10-02T22:14:04 step 0 inner-val loss 0.11207 (pretrained) {'immicro': 0.10636159498244524, 'ye2026_sepsis': 0.12489082664251328, 'imperial_eskd': 0.10889218679883263, 'liu2021': 0.1081176083534956}
+2026-10-02T22:14:15 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
+2026-10-02T22:14:26 step 500 train 0.10326 inner-val 0.10979 best 0.10979@500 (21s) immicro=0.1044 ye2026_sepsis=0.1226 imperial_eskd=0.1063 liu2021=0.1058
+2026-10-02T22:14:36 step 750 train 0.10287 inner-val 0.10798 best 0.10798@750 (32s) immicro=0.1031 ye2026_sepsis=0.1205 imperial_eskd=0.1042 liu2021=0.1041
+2026-10-02T22:14:47 step 1000 train 0.10294 inner-val 0.10644 best 0.10644@1000 (42s) immicro=0.1021 ye2026_sepsis=0.1184 imperial_eskd=0.1025 liu2021=0.1028
+2026-10-02T22:14:57 step 1250 train 0.10286 inner-val 0.10537 best 0.10537@1250 (53s) immicro=0.1012 ye2026_sepsis=0.1168 imperial_eskd=0.1014 liu2021=0.1020
+2026-10-02T22:15:09 step 1500 train 0.10246 inner-val 0.10470 best 0.10470@1500 (64s) immicro=0.1006 ye2026_sepsis=0.1159 imperial_eskd=0.1008 liu2021=0.1015
+2026-10-02T22:15:21 step 1750 train 0.10249 inner-val 0.10428 best 0.10428@1750 (76s) immicro=0.1001 ye2026_sepsis=0.1153 imperial_eskd=0.1004 liu2021=0.1012
+2026-10-02T22:15:33 step 2000 train 0.10213 inner-val 0.10400 best 0.10400@2000 (88s) immicro=0.0998 ye2026_sepsis=0.1150 imperial_eskd=0.1002 liu2021=0.1011
+2026-10-02T22:15:44 step 2250 train 0.10233 inner-val 0.10381 best 0.10381@2250 (100s) immicro=0.0996 ye2026_sepsis=0.1147 imperial_eskd=0.1000 liu2021=0.1010
+2026-10-02T22:15:56 step 2500 train 0.10232 inner-val 0.10367 best 0.10367@2500 (112s) immicro=0.0994 ye2026_sepsis=0.1146 imperial_eskd=0.0998 liu2021=0.1009
+2026-10-02T22:16:08 step 2750 train 0.10233 inner-val 0.10357 best 0.10357@2750 (124s) immicro=0.0993 ye2026_sepsis=0.1144 imperial_eskd=0.0998 liu2021=0.1008
+2026-10-02T22:16:19 step 3000 train 0.10244 inner-val 0.10350 best 0.10350@3000 (135s) immicro=0.0992 ye2026_sepsis=0.1143 imperial_eskd=0.0997 liu2021=0.1008
+2026-10-02T22:16:30 step 3250 train 0.10251 inner-val 0.10345 best 0.10345@3250 (146s) immicro=0.0991 ye2026_sepsis=0.1143 imperial_eskd=0.0996 liu2021=0.1008
+2026-10-02T22:16:40 step 3500 train 0.10224 inner-val 0.10340 best 0.10340@3500 (156s) immicro=0.0990 ye2026_sepsis=0.1142 imperial_eskd=0.0996 liu2021=0.1008
+2026-10-02T22:16:51 step 3750 train 0.10221 inner-val 0.10337 best 0.10337@3750 (167s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0996 liu2021=0.1007
+2026-10-02T22:17:03 step 4000 train 0.10229 inner-val 0.10334 best 0.10334@4000 (179s) immicro=0.0990 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:17:15 step 4250 train 0.10248 inner-val 0.10332 best 0.10332@4250 (190s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:17:27 step 4500 train 0.10189 inner-val 0.10330 best 0.10330@4500 (203s) immicro=0.0989 ye2026_sepsis=0.1141 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:17:38 step 4750 train 0.10205 inner-val 0.10328 best 0.10328@4750 (213s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:17:48 step 5000 train 0.10225 inner-val 0.10327 best 0.10327@5000 (224s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:18:00 step 5250 train 0.10211 inner-val 0.10325 best 0.10325@5250 (235s) immicro=0.0989 ye2026_sepsis=0.1140 imperial_eskd=0.0995 liu2021=0.1007
+2026-10-02T22:18:11 step 5500 train 0.10192 inner-val 0.10324 best 0.10324@5500 (247s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:18:23 step 5750 train 0.10204 inner-val 0.10323 best 0.10323@5750 (259s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:18:36 step 6000 train 0.10227 inner-val 0.10322 best 0.10322@6000 (271s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:18:48 step 6250 train 0.10230 inner-val 0.10321 best 0.10321@6250 (283s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:19:00 step 6500 train 0.10201 inner-val 0.10321 best 0.10321@6500 (295s) immicro=0.0988 ye2026_sepsis=0.1140 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:19:12 step 6750 train 0.10199 inner-val 0.10320 best 0.10320@6750 (308s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:19:24 step 7000 train 0.10211 inner-val 0.10319 best 0.10319@7000 (320s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:19:36 step 7250 train 0.10215 inner-val 0.10318 best 0.10318@7250 (332s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1007
+2026-10-02T22:19:48 step 7500 train 0.10208 inner-val 0.10318 best 0.10318@7500 (344s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-02T22:20:00 step 7750 train 0.10206 inner-val 0.10317 best 0.10317@7750 (356s) immicro=0.0988 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-02T22:20:12 step 8000 train 0.10176 inner-val 0.10317 best 0.10317@8000 (368s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-02T22:20:24 step 8250 train 0.10206 inner-val 0.10316 best 0.10316@8250 (380s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-02T22:20:36 step 8500 train 0.10170 inner-val 0.10316 best 0.10316@8500 (392s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0994 liu2021=0.1006
+2026-10-02T22:20:48 step 8750 train 0.10224 inner-val 0.10315 best 0.10315@8750 (404s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:21:00 step 9000 train 0.10217 inner-val 0.10315 best 0.10315@9000 (416s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:21:12 step 9250 train 0.10210 inner-val 0.10314 best 0.10314@9250 (428s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:21:25 step 9500 train 0.10174 inner-val 0.10314 best 0.10314@9500 (440s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:21:37 step 9750 train 0.10168 inner-val 0.10314 best 0.10314@9750 (452s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:21:49 step 10000 train 0.10180 inner-val 0.10314 best 0.10314@10000 (464s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:22:01 step 10250 train 0.10190 inner-val 0.10313 best 0.10313@10250 (477s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:22:13 step 10500 train 0.10201 inner-val 0.10313 best 0.10313@10500 (489s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:22:25 step 10750 train 0.10181 inner-val 0.10313 best 0.10313@10750 (501s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:22:37 step 11000 train 0.10200 inner-val 0.10312 best 0.10312@11000 (513s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:22:49 step 11250 train 0.10161 inner-val 0.10312 best 0.10312@11250 (525s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:23:02 step 11500 train 0.10193 inner-val 0.10312 best 0.10312@11500 (537s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:23:14 step 11750 train 0.10193 inner-val 0.10311 best 0.10311@11750 (550s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:23:26 step 12000 train 0.10151 inner-val 0.10311 best 0.10311@12000 (562s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:23:38 step 12250 train 0.10195 inner-val 0.10311 best 0.10311@12000 (574s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:23:50 step 12500 train 0.10182 inner-val 0.10311 best 0.10311@12000 (586s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:24:02 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
+2026-10-02T22:24:14 step 13000 train 0.10211 inner-val 0.10311 best 0.10311@12000 (610s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:24:27 step 13250 train 0.10196 inner-val 0.10311 best 0.10311@13250 (622s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:24:39 step 13500 train 0.10179 inner-val 0.10311 best 0.10311@13500 (634s) immicro=0.0987 ye2026_sepsis=0.1140 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:24:51 step 13750 train 0.10206 inner-val 0.10310 best 0.10310@13750 (646s) immicro=0.0987 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1006
+2026-10-02T22:25:03 step 14000 train 0.10163 inner-val 0.10310 best 0.10310@14000 (658s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0993 liu2021=0.1005
+2026-10-02T22:25:15 step 14250 train 0.10164 inner-val 0.10310 best 0.10310@14250 (670s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:25:27 step 14500 train 0.10170 inner-val 0.10309 best 0.10309@14500 (682s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:25:39 step 14750 train 0.10187 inner-val 0.10309 best 0.10309@14750 (695s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:25:51 step 15000 train 0.10196 inner-val 0.10309 best 0.10309@15000 (707s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:26:04 step 15250 train 0.10168 inner-val 0.10309 best 0.10309@15250 (719s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:26:16 step 15500 train 0.10220 inner-val 0.10308 best 0.10308@15500 (732s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:26:28 step 15750 train 0.10169 inner-val 0.10308 best 0.10308@15750 (744s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:26:40 step 16000 train 0.10160 inner-val 0.10308 best 0.10308@15750 (756s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:26:52 step 16250 train 0.10189 inner-val 0.10308 best 0.10308@16250 (768s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:27:05 step 16500 train 0.10181 inner-val 0.10307 best 0.10307@16500 (780s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:27:17 step 16750 train 0.10181 inner-val 0.10307 best 0.10307@16500 (792s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:27:29 step 17000 train 0.10196 inner-val 0.10307 best 0.10307@17000 (804s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:27:41 step 17250 train 0.10200 inner-val 0.10307 best 0.10307@17250 (817s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:27:53 step 17500 train 0.10172 inner-val 0.10306 best 0.10306@17500 (829s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:04 step 17750 train 0.10152 inner-val 0.10306 best 0.10306@17750 (839s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:14 step 18000 train 0.10205 inner-val 0.10306 best 0.10306@17750 (850s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:24 step 18250 train 0.10159 inner-val 0.10306 best 0.10306@18250 (860s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:35 step 18500 train 0.10148 inner-val 0.10306 best 0.10306@18250 (871s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:45 step 18750 train 0.10181 inner-val 0.10306 best 0.10306@18750 (881s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:28:56 step 19000 train 0.10185 inner-val 0.10306 best 0.10306@18750 (891s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:29:06 step 19250 train 0.10184 inner-val 0.10306 best 0.10306@19250 (902s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:29:17 step 19500 train 0.10163 inner-val 0.10305 best 0.10305@19500 (912s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:29:27 step 19750 train 0.10159 inner-val 0.10305 best 0.10305@19750 (923s) immicro=0.0986 ye2026_sepsis=0.1140 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:29:38 step 20000 train 0.10176 inner-val 0.10305 best 0.10305@20000 (934s) immicro=0.0986 ye2026_sepsis=0.1139 imperial_eskd=0.0992 liu2021=0.1005
+2026-10-02T22:29:42 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora16/checkpoint_ft.pt (selected step 20000, inner-val 0.10305 vs pretrained 0.11207); sha 6763c22fbbc0

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ji2025_chili/K1000/lora16sat/RUN.json

@@ -0,0 +1,584 @@
+{
+ "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_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": {
+  "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": 16,
+  "lora_alpha": 16.0,
+  "n_trainable": 1296224,
+  "n_total_base": 54185184,
+  "trainable_tensors": 44
+ },
+ "lr": 0.0005,
+ "warmup": 200,
+ "batch_size": 256,
+ "ema_decay": 0.999,
+ "max_steps": 200000,
+ "steps_run": 40000,
+ "eval_every": 250,
+ "patience": 8,
+ "selected_step": 38250,
+ "selected_inner_val_loss": 0.1029594385728353,
+ "pretrained_inner_val_loss": 0.11206555419432168,
+ "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",
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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/lora16sat/history.json

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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/lora16sat/milestones.json

@@ -0,0 +1,22 @@
+[
+ {
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+  "selected_step": 20000,
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+  "reason": "block_end",
+  "checkpoint": "/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_s20000.pt",
+  "checkpoint_sha256": "b43a23a338a13ebebc3d3172cc90aa5fb5be5a605a025c974cb924607902d726"
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+ {
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+  "checkpoint_sha256": "13be590810a16d828a5ea0da0dda52a2a48edd60cddc221e2c456fcbbaa2ccb2"
+ }
+]