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_giroux2022/K1000/lora16sat/history.json

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+  "train_loss": 0.10258095562458039,
+  "sec": 1703.3
+ }
+]

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora16sat/milestones.json

@@ -0,0 +1,22 @@
+[
+ {
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+  "selected_step": 19500,
+  "selected_inner_val_loss": 0.1034145244383112,
+  "block_improvement": 0.009083397425428935,
+  "total_improvement": 0.009083397425428935,
+  "reason": "block_end",
+  "checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora16sat/checkpoint_ft_s20000.pt",
+  "checkpoint_sha256": "0d8b718f725a92e0be92e7b50e80633e75157618962172db3ef4cfc261ed35b6"
+ },
+ {
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+  "block_improvement": 4.508868894642637e-05,
+  "total_improvement": 0.009128486114375362,
+  "reason": "early_stop",
+  "checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora16sat/checkpoint_ft_s35750.pt",
+  "checkpoint_sha256": "a24d1736b10ac89309dbaa03088e3baebce839011792c82ef7fde92c106a3e75"
+ }
+]

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora16sat/train.log

@@ -0,0 +1,148 @@
+2026-10-03T02:01:51 split=hoL_giroux2022 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; liu2021: train 40 rows / 147,532 cells, inner 4
+2026-10-03T02:01:53 step 0 inner-val loss 0.11250 (pretrained) {'immicro': 0.10636159498244524, 'ye2026_sepsis': 0.12489082664251328, 'imperial_eskd': 0.10889218679883263, 'liu2021': 0.10984707903116941}
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+2026-10-03T02:17:46 block end 20000: best 0.10341@19500; block improvement 0.009083 (100.00 % of total 0.009083) -> continue
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+2026-10-03T02:20:21 step 23250 train 0.10296 inner-val 0.10340 best 0.10340@23250 (1108s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1022
+2026-10-03T02:20:33 step 23500 train 0.10266 inner-val 0.10340 best 0.10340@23500 (1120s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1022
+2026-10-03T02:20:45 step 23750 train 0.10261 inner-val 0.10340 best 0.10340@23750 (1132s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1021
+2026-10-03T02:20:57 step 24000 train 0.10267 inner-val 0.10340 best 0.10340@23750 (1144s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1022
+2026-10-03T02:21:09 step 24250 train 0.10244 inner-val 0.10340 best 0.10340@23750 (1156s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1022
+2026-10-03T02:21:21 step 24500 train 0.10247 inner-val 0.10340 best 0.10340@23750 (1168s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0992 liu2021=0.1022
+2026-10-03T02:21:33 step 24750 train 0.10256 inner-val 0.10340 best 0.10340@23750 (1180s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
+2026-10-03T02:21:45 step 25000 train 0.10234 inner-val 0.10340 best 0.10340@25000 (1192s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
+2026-10-03T02:21:57 step 25250 train 0.10244 inner-val 0.10340 best 0.10340@25000 (1204s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
+2026-10-03T02:22:09 step 25500 train 0.10268 inner-val 0.10340 best 0.10340@25000 (1216s) immicro=0.0985 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
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+2026-10-03T02:25:20 step 29500 train 0.10237 inner-val 0.10339 best 0.10339@29000 (1407s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
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+2026-10-03T02:25:56 step 30250 train 0.10228 inner-val 0.10338 best 0.10338@30250 (1442s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
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+2026-10-03T02:27:07 step 31750 train 0.10270 inner-val 0.10338 best 0.10338@31500 (1514s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1022
+2026-10-03T02:27:19 step 32000 train 0.10259 inner-val 0.10337 best 0.10337@32000 (1526s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
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+2026-10-03T02:28:06 step 33000 train 0.10253 inner-val 0.10337 best 0.10337@33000 (1573s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
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+2026-10-03T02:28:30 step 33500 train 0.10248 inner-val 0.10337 best 0.10337@33000 (1597s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:28:42 step 33750 train 0.10254 inner-val 0.10337 best 0.10337@33750 (1608s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:28:53 step 34000 train 0.10266 inner-val 0.10337 best 0.10337@33750 (1620s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:29:05 step 34250 train 0.10256 inner-val 0.10337 best 0.10337@33750 (1632s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:29:17 step 34500 train 0.10259 inner-val 0.10337 best 0.10337@33750 (1644s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:29:29 step 34750 train 0.10266 inner-val 0.10337 best 0.10337@33750 (1656s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:29:41 step 35000 train 0.10273 inner-val 0.10337 best 0.10337@33750 (1668s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:29:53 step 35250 train 0.10225 inner-val 0.10337 best 0.10337@33750 (1680s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:30:05 step 35500 train 0.10278 inner-val 0.10337 best 0.10337@33750 (1691s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:30:16 step 35750 train 0.10258 inner-val 0.10337 best 0.10337@33750 (1703s) immicro=0.0984 ye2026_sepsis=0.1138 imperial_eskd=0.0991 liu2021=0.1021
+2026-10-03T02:30:16 early stop: no improvement in 8 evaluations
+2026-10-03T02:30:22 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora16sat/checkpoint_ft.pt (selected step 33750, inner-val 0.10337 vs pretrained 0.11250); sha b56ff0f93c65

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_giroux2022/K1000/lora64/RUN.json

@@ -0,0 +1,517 @@
+{
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+ "source_checkpoint_sha256": "8c7cd0e83e2bc19fe03dfcbc084919a1e2f61be8cc8bc12f4caf543dfda91633",
+ "split": "hoL_giroux2022",
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+ "split_sha256": "3c26e00bd5691416d6ad89c8a5335dcdc42a252198ba0966b80d2a0d2b68495a",
+ "held_out": "giroux2022",
+ "recipe_k": 1000,
+ "balance": "cohort",
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+   "up_blocks.0.input_projection",
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+  {
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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",
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