diff --git "a/lm_eval_logs/driver_nohup.log" "b/lm_eval_logs/driver_nohup.log"
new file mode 100644--- /dev/null
+++ "b/lm_eval_logs/driver_nohup.log"
@@ -0,0 +1,1213 @@
+[INFO] override: GPU_ID=1
+[INFO] override: RUN_BG=1
+[INFO] override: MUON_LR=2.6e-3
+[INFO] override: ALPHA=1200
+[INFO] override: RUN_TRAIN=1
+[INFO] override: STEERING_COEFFS=15
+[INFO] OUTPUT_DIR      = models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6
+[INFO] PRETRAINED_PATH = /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6
+[INFO] LOG_DIR         = /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6/lm_eval_logs
+[INFO] GPU_ID          = 1 (CUDA_VISIBLE_DEVICES=1 => device cuda:0)
+[INFO] WAIT_SEC        = 300 (seconds)
+[INFO] MAX_RETRIES     = 10 (0 means infinite)
+[INFO] training...
+/egr/research-optml/wangc168/anaconda3/envs/py310/lib/python3.10/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+
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+wandb: Currently logged in as: wangc168 (wcs23187) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run kstsc84q
+wandb: Tracking run with wandb version 0.23.1
+wandb: Run data is saved locally in /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/wandb/run-20260403_161129-kstsc84q
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run muon-v1-rank-models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6
+wandb: ⭐️ View project at https://wandb.ai/wcs23187/Muon-constraint
+wandb: 🚀 View run at https://wandb.ai/wcs23187/Muon-constraint/runs/kstsc84q
+====rmu Config====
+model_name_or_path=HuggingFaceH4/zephyr-7b-beta
+module_str={model_name}.model.layers[{layer_id}]
+output_dir=models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6
+retain_corpora=['wikitext', 'wikitext']
+forget_corpora=['bio_remove_dataset', 'cyber-forget-corpus']
+alpha=[1200.0, 1200.0]
+steering_coeffs=15,15
+muon_lr=0.0026
+muon_momentum=0.95
+muon_weight_decay=0.1
+muon_ns_steps=5
+muon_nesterov=True
+adam_emb_lr=5e-05
+adam_scalar_lr=5e-05
+enable_muon_svd_logging=False
+M_stats_path=
+M_check_T=0
+V1_K=64
+min_len=0
+max_len=2000
+batch_size=4
+max_num_batches=150
+layer_id=7
+layer_ids=[5, 6, 7]
+param_ids=[6]
+seed=42
+verbose=False
+steering_coeff_list=[15.0, 15.0]
+=====
+MUON model.layers.5.mlp.down_proj.weight (4096, 14336)
+MUON model.layers.6.mlp.down_proj.weight (4096, 14336)
+MUON model.layers.7.mlp.down_proj.weight (4096, 14336)
+======= Epoch 0 =======
+
  0%|          | 0/150 [00:00<?, ?it/s]We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
+/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/rmu_wb/unlearn_muon_v1.py:201: UserWarning: Using a target size (torch.Size([1, 1, 4096])) that is different to the input size (torch.Size([4, 512, 4096])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
+  unlearn_loss = torch.nn.functional.mse_loss(
+
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+  unlearn_loss = torch.nn.functional.mse_loss(
+
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 41%|████      | 61/150 [08:02<11:52,  8.00s/it]now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1377 | retain_loss: 0 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.0791 | retain_loss: 4.816e-05 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1426 | unlearn_loss: 0.1416 | retain_loss: 0.001022 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.0791 | retain_loss: 0.0006828 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1387 | unlearn_loss: 0.1377 | retain_loss: 0.0008354 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07861 | retain_loss: 0.0009842 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1357 | unlearn_loss: 0.1357 | retain_loss: 0.0004139 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07861 | retain_loss: 0.0009918 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1406 | retain_loss: 0.0009003 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07861 | retain_loss: 0.0004692 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1367 | retain_loss: 0.001373 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07861 | retain_loss: 0.0004501 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1367 | retain_loss: 0.0005684 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07861 | retain_loss: 0.0007057 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1436 | unlearn_loss: 0.1396 | retain_loss: 0.003998 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08008 | unlearn_loss: 0.07861 | retain_loss: 0.001457 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1367 | retain_loss: 0.001244 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08008 | unlearn_loss: 0.07861 | retain_loss: 0.001556 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1396 | unlearn_loss: 0.1387 | retain_loss: 0.001022 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07812 | retain_loss: 0.0009079 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1406 | unlearn_loss: 0.1377 | retain_loss: 0.003036 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07812 | retain_loss: 0.001228 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1406 | unlearn_loss: 0.1387 | retain_loss: 0.001526 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07812 | retain_loss: 0.0009193 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1406 | unlearn_loss: 0.1396 | retain_loss: 0.001152 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07812 | retain_loss: 0.001007 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1387 | unlearn_loss: 0.1357 | retain_loss: 0.003036 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08008 | unlearn_loss: 0.07812 | retain_loss: 0.001801 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1396 | unlearn_loss: 0.1367 | retain_loss: 0.002579 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07764 | retain_loss: 0.001518 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1367 | retain_loss: 0.001404 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07861 | unlearn_loss: 0.07764 | retain_loss: 0.001099 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1387 | unlearn_loss: 0.1357 | retain_loss: 0.002899 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08008 | unlearn_loss: 0.07812 | retain_loss: 0.002121 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1406 | unlearn_loss: 0.1387 | retain_loss: 0.001907 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07812 | retain_loss: 0.001358 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1367 | retain_loss: 0.004425 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08057 | unlearn_loss: 0.07812 | retain_loss: 0.002625 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1426 | unlearn_loss: 0.1396 | retain_loss: 0.002457 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07764 | retain_loss: 0.001823 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1367 | retain_loss: 0.004456 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08008 | unlearn_loss: 0.07715 | retain_loss: 0.002975 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1455 | unlearn_loss: 0.1416 | retain_loss: 0.003616 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07715 | retain_loss: 0.002594 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1387 | retain_loss: 0.002579 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07715 | retain_loss: 0.002182 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1445 | unlearn_loss: 0.1396 | retain_loss: 0.004578 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08057 | unlearn_loss: 0.07764 | retain_loss: 0.002914 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1377 | retain_loss: 0.00351 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.07715 | retain_loss: 0.002396 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1396 | unlearn_loss: 0.1367 | retain_loss: 0.003021 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07861 | unlearn_loss: 0.07666 | retain_loss: 0.001945 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1377 | unlearn_loss: 0.1348 | retain_loss: 0.002563 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0791 | unlearn_loss: 0.07715 | retain_loss: 0.001762 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1338 | retain_loss: 0.00824 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08252 | unlearn_loss: 0.07617 | retain_loss: 0.006256 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1279 | unlearn_loss: 0.1226 | retain_loss: 0.005066 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08057 | unlearn_loss: 0.07617 | retain_loss: 0.004272 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1406 | unlearn_loss: 0.1309 | retain_loss: 0.009583 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08447 | unlearn_loss: 0.07617 | retain_loss: 0.008179 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1396 | unlearn_loss: 0.1309 | retain_loss: 0.008667 | param_change: 0
+now we use  the mode ..  v1
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+  unlearn_loss = torch.nn.functional.mse_loss(
+
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+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08398 | unlearn_loss: 0.07666 | retain_loss: 0.007538 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1416 | unlearn_loss: 0.1328 | retain_loss: 0.008362 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08301 | unlearn_loss: 0.07568 | retain_loss: 0.007507 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1426 | unlearn_loss: 0.1377 | retain_loss: 0.004974 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07959 | unlearn_loss: 0.0752 | retain_loss: 0.004547 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1367 | unlearn_loss: 0.1318 | retain_loss: 0.004517 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07812 | unlearn_loss: 0.07422 | retain_loss: 0.00386 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1318 | unlearn_loss: 0.127 | retain_loss: 0.004547 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07861 | unlearn_loss: 0.07471 | retain_loss: 0.003769 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1309 | unlearn_loss: 0.127 | retain_loss: 0.00415 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07764 | unlearn_loss: 0.07373 | retain_loss: 0.003708 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1289 | unlearn_loss: 0.125 | retain_loss: 0.004395 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07666 | unlearn_loss: 0.07275 | retain_loss: 0.003891 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1357 | unlearn_loss: 0.1279 | retain_loss: 0.007477 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07715 | unlearn_loss: 0.07178 | retain_loss: 0.005463 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1318 | unlearn_loss: 0.126 | retain_loss: 0.005585 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07568 | unlearn_loss: 0.07129 | retain_loss: 0.004608 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1289 | unlearn_loss: 0.1245 | retain_loss: 0.004852 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07764 | unlearn_loss: 0.07324 | retain_loss: 0.004211 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.124 | unlearn_loss: 0.1196 | retain_loss: 0.004608 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07471 | unlearn_loss: 0.07031 | retain_loss: 0.004242 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.126 | unlearn_loss: 0.1196 | retain_loss: 0.006012 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07422 | unlearn_loss: 0.06934 | retain_loss: 0.004913 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1309 | unlearn_loss: 0.127 | retain_loss: 0.004333 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07275 | unlearn_loss: 0.06885 | retain_loss: 0.003906 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1216 | unlearn_loss: 0.1177 | retain_loss: 0.003967 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07129 | unlearn_loss: 0.06787 | retain_loss: 0.003448 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1245 | unlearn_loss: 0.1211 | retain_loss: 0.003632 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07129 | unlearn_loss: 0.06787 | retain_loss: 0.003189 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1162 | unlearn_loss: 0.1128 | retain_loss: 0.003662 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07178 | unlearn_loss: 0.06885 | retain_loss: 0.003113 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.125 | unlearn_loss: 0.1191 | retain_loss: 0.006256 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.0708 | unlearn_loss: 0.06592 | retain_loss: 0.004822 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1172 | unlearn_loss: 0.1113 | retain_loss: 0.005798 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07324 | unlearn_loss: 0.06836 | retain_loss: 0.004913 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.126 | unlearn_loss: 0.1206 | retain_loss: 0.005646 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.07178 | unlearn_loss: 0.06689 | retain_loss: 0.004944 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.124 | unlearn_loss: 0.1196 | retain_loss: 0.004517 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06787 | unlearn_loss: 0.06396 | retain_loss: 0.00386 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1191 | unlearn_loss: 0.1128 | retain_loss: 0.006439 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06982 | unlearn_loss: 0.06445 | retain_loss: 0.005249 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1167 | unlearn_loss: 0.1123 | retain_loss: 0.004242 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06592 | unlearn_loss: 0.0625 | retain_loss: 0.003296 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1104 | unlearn_loss: 0.1055 | retain_loss: 0.0047 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06689 | unlearn_loss: 0.06299 | retain_loss: 0.003937 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.106 | unlearn_loss: 0.1021 | retain_loss: 0.004059 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06177 | unlearn_loss: 0.05811 | retain_loss: 0.003555 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1157 | unlearn_loss: 0.1113 | retain_loss: 0.004517 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06079 | unlearn_loss: 0.05688 | retain_loss: 0.00383 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.105 | unlearn_loss: 0.1011 | retain_loss: 0.00412 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06055 | unlearn_loss: 0.05688 | retain_loss: 0.00354 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1094 | unlearn_loss: 0.105 | retain_loss: 0.004242 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06006 | unlearn_loss: 0.05615 | retain_loss: 0.003815 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.104 | unlearn_loss: 0.1001 | retain_loss: 0.003906 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05859 | unlearn_loss: 0.05518 | retain_loss: 0.003357 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1128 | unlearn_loss: 0.1084 | retain_loss: 0.004456 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05664 | unlearn_loss: 0.05298 | retain_loss: 0.003769 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1123 | unlearn_loss: 0.1084 | retain_loss: 0.003708 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05615 | unlearn_loss: 0.05298 | retain_loss: 0.003235 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.124 | unlearn_loss: 0.1191 | retain_loss: 0.004852 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05591 | unlearn_loss: 0.05176 | retain_loss: 0.004211 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+
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+wandb: updating run metadata
+wandb: uploading config.yaml
+wandb: 
+wandb: Run history:
+wandb:         loss ▄▄▄▇▄██▄▄▄▄▃▄██▄█▄▄█▇▃▇▃▆▃▆▇▆▃▂▅▆▂▂▁▂▅▁▁
+wandb: param_change ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:  retain_loss ▁▁▁▁▁▂▁▂▂▂▂▂▂▁▂▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂█
+wandb:         step ▁▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇███
+wandb: unlearn_loss ▃█▃█▃▃██▃█▃▃▃█▃██▃▃▇▇▆▃▃▃▆▃▅▅▂▂▅▅▆▆▄▁▅▄▄
+wandb: 
+wandb: Run summary:
+wandb:         loss 0.06348
+wandb: param_change 0
+wandb:  retain_loss 0.0249
+wandb:         step 149
+wandb: unlearn_loss 0.03882
+wandb: 
+wandb: 🚀 View run muon-v1-rank-models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6 at: https://wandb.ai/wcs23187/Muon-constraint/runs/kstsc84q
+wandb: ⭐️ View project at: https://wandb.ai/wcs23187/Muon-constraint
+wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20260403_161129-kstsc84q/logs
+loss: 0.103 | unlearn_loss: 0.09912 | retain_loss: 0.003784 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05322 | unlearn_loss: 0.05005 | retain_loss: 0.003296 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1099 | unlearn_loss: 0.1055 | retain_loss: 0.004242 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05298 | unlearn_loss: 0.04956 | retain_loss: 0.003494 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1064 | unlearn_loss: 0.1025 | retain_loss: 0.003967 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05298 | unlearn_loss: 0.04956 | retain_loss: 0.003494 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.09619 | unlearn_loss: 0.0918 | retain_loss: 0.004456 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05127 | unlearn_loss: 0.04736 | retain_loss: 0.003891 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1006 | unlearn_loss: 0.09619 | retain_loss: 0.004608 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05029 | unlearn_loss: 0.04663 | retain_loss: 0.003723 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.09863 | unlearn_loss: 0.09473 | retain_loss: 0.003815 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.05273 | unlearn_loss: 0.04956 | retain_loss: 0.00325 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.09961 | unlearn_loss: 0.09619 | retain_loss: 0.003571 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04468 | unlearn_loss: 0.04175 | retain_loss: 0.002975 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.09668 | unlearn_loss: 0.09229 | retain_loss: 0.004425 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04419 | unlearn_loss: 0.04053 | retain_loss: 0.003708 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1025 | unlearn_loss: 0.09863 | retain_loss: 0.003754 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04395 | unlearn_loss: 0.04077 | retain_loss: 0.003204 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.08496 | unlearn_loss: 0.08105 | retain_loss: 0.00386 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04517 | unlearn_loss: 0.04199 | retain_loss: 0.003098 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.09717 | unlearn_loss: 0.09326 | retain_loss: 0.004089 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04492 | unlearn_loss: 0.0415 | retain_loss: 0.003464 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1064 | unlearn_loss: 0.1011 | retain_loss: 0.005493 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04321 | unlearn_loss: 0.03882 | retain_loss: 0.004303 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.1064 | unlearn_loss: 0.1006 | retain_loss: 0.005829 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.04443 | unlearn_loss: 0.03955 | retain_loss: 0.004913 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.125 | unlearn_loss: 0.09082 | retain_loss: 0.03394 | param_change: 0
+now we use  the mode ..  v1
+k_eff 64
+k_eff 64
+k_eff 64
+loss: 0.06348 | unlearn_loss: 0.03882 | retain_loss: 0.0249 | param_change: 0
+Saved model to models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6
+Saved model path to /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/model_dir.txt with index 298
+[INFO] lm_eval wmdp (with retry)...
+[INFO] (wmdp) attempt=1 running: CUDA_VISIBLE_DEVICES=1 lm_eval --model hf --model_args pretrained=/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6 --device cuda:0 --batch_size 16 --log_samples --tasks wmdp --output_path "/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6/lm_eval_logs/wmdp_results.json"
+/egr/research-optml/wangc168/anaconda3/envs/SOUL/lib/python3.9/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+2026-04-03:16:33:15,348 INFO     [__main__.py:279] Verbosity set to INFO
+2026-04-03:16:33:16,808 INFO     [__init__.py:491] `group` and `group_alias` keys in TaskConfigs are deprecated and will be removed in v0.4.5 of lm_eval. The new `tag` field will be used to allow for a shortcut to a group of tasks one does not wish to aggregate metrics across. `group`s which aggregate across subtasks must be only defined in a separate group config file, which will be the official way to create groups that support cross-task aggregation as in `mmlu`. Please see the v0.4.4 patch notes and our documentation: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md#advanced-group-configs for more information.
+2026-04-03:16:33:40,965 INFO     [__main__.py:376] Selected Tasks: ['wmdp']
+2026-04-03:16:33:40,998 INFO     [evaluator.py:161] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2026-04-03:16:33:40,998 INFO     [evaluator.py:198] Initializing hf model, with arguments: {'pretrained': '/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6'}
+2026-04-03:16:33:41,195 INFO     [huggingface.py:130] Using device 'cuda:0'
+2026-04-03:16:33:41,721 INFO     [huggingface.py:366] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}
+
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+2026-04-03:16:34:15,990 WARNING  [task.py:337] [Task: wmdp_cyber] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:15,990 WARNING  [task.py:337] [Task: wmdp_cyber] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:17,327 WARNING  [task.py:337] [Task: wmdp_chem] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:17,327 WARNING  [task.py:337] [Task: wmdp_chem] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:18,114 WARNING  [task.py:337] [Task: wmdp_bio] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:18,114 WARNING  [task.py:337] [Task: wmdp_bio] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2026-04-03:16:34:18,191 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:34:18,191 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:34:18,191 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:34:18,191 WARNING  [model.py:422] model.chat_template was called with the chat_template set to False or None. Therefore no chat template will be applied. Make sure this is an intended behavior.
+2026-04-03:16:34:18,192 INFO     [task.py:423] Building contexts for wmdp_bio on rank 0...
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+2026-04-03:16:35:29,404 WARNING  [huggingface.py:1344] Failed to get model SHA for /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6 at revision main. Error: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6'. Use `repo_type` argument if needed.
+2026-04-03:16:35:44,231 INFO     [evaluation_tracker.py:206] Saving results aggregated
+2026-04-03:16:35:44,240 INFO     [evaluation_tracker.py:287] Saving per-sample results for: wmdp_bio
+2026-04-03:16:35:45,155 INFO     [evaluation_tracker.py:287] Saving per-sample results for: wmdp_chem
+2026-04-03:16:35:45,449 INFO     [evaluation_tracker.py:287] Saving per-sample results for: wmdp_cyber
+hf (pretrained=/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
+|    Tasks    |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
+|-------------|------:|------|-----:|------|---|-----:|---|-----:|
+|wmdp         |      1|none  |      |acc   |↑  |0.3132|±  |0.0076|
+| - wmdp_bio  |      1|none  |     0|acc   |↑  |0.3009|±  |0.0129|
+| - wmdp_chem |      1|none  |     0|acc   |↑  |0.4583|±  |0.0247|
+| - wmdp_cyber|      1|none  |     0|acc   |↑  |0.2914|±  |0.0102|
+
+|Groups|Version|Filter|n-shot|Metric|   |Value |   |Stderr|
+|------|------:|------|------|------|---|-----:|---|-----:|
+|wmdp  |      1|none  |      |acc   |↑  |0.3132|±  |0.0076|
+
+[INFO] (wmdp) success (found result table and non-empty json).
+[INFO] lm_eval mmlu (with retry)...
+[INFO] (mmlu) attempt=1 running: CUDA_VISIBLE_DEVICES=1 lm_eval --model hf --model_args pretrained=/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6 --device cuda:0 --batch_size 16 --log_samples --tasks mmlu --output_path "/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6/lm_eval_logs/mmlu_results.json"
+/egr/research-optml/wangc168/anaconda3/envs/SOUL/lib/python3.9/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+2026-04-03:16:35:59,252 INFO     [__main__.py:279] Verbosity set to INFO
+2026-04-03:16:35:59,682 INFO     [__init__.py:491] `group` and `group_alias` keys in TaskConfigs are deprecated and will be removed in v0.4.5 of lm_eval. The new `tag` field will be used to allow for a shortcut to a group of tasks one does not wish to aggregate metrics across. `group`s which aggregate across subtasks must be only defined in a separate group config file, which will be the official way to create groups that support cross-task aggregation as in `mmlu`. Please see the v0.4.4 patch notes and our documentation: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md#advanced-group-configs for more information.
+2026-04-03:16:36:07,612 INFO     [__main__.py:376] Selected Tasks: ['mmlu']
+2026-04-03:16:36:07,615 INFO     [evaluator.py:161] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2026-04-03:16:36:07,615 INFO     [evaluator.py:198] Initializing hf model, with arguments: {'pretrained': '/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6'}
+2026-04-03:16:36:07,760 INFO     [huggingface.py:130] Using device 'cuda:0'
+2026-04-03:16:36:07,992 INFO     [huggingface.py:366] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}
+
Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]
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+2026-04-03:16:37:53,518 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:37:53,518 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:37:53,518 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
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+2026-04-03:16:37:53,519 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
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+2026-04-03:16:37:53,519 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
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+2026-04-03:16:37:53,520 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:37:53,520 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
+2026-04-03:16:37:53,520 INFO     [evaluator.py:279] Setting fewshot random generator seed to 1234
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+2026-04-03:16:37:53,521 WARNING  [model.py:422] model.chat_template was called with the chat_template set to False or None. Therefore no chat template will be applied. Make sure this is an intended behavior.
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+2026-04-03:16:40:08,272 WARNING  [huggingface.py:1344] Failed to get model SHA for /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6 at revision main. Error: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6'. Use `repo_type` argument if needed.
+2026-04-03:16:40:13,621 INFO     [evaluation_tracker.py:206] Saving results aggregated
+2026-04-03:16:40:13,641 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_abstract_algebra
+2026-04-03:16:40:13,711 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_anatomy
+2026-04-03:16:40:13,809 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_astronomy
+2026-04-03:16:40:13,927 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_business_ethics
+2026-04-03:16:40:14,011 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_clinical_knowledge
+2026-04-03:16:40:14,215 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_biology
+2026-04-03:16:40:14,331 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_chemistry
+2026-04-03:16:40:14,423 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_computer_science
+2026-04-03:16:40:14,517 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_mathematics
+2026-04-03:16:40:14,607 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_medicine
+2026-04-03:16:40:14,749 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_college_physics
+2026-04-03:16:40:14,828 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_computer_security
+2026-04-03:16:40:14,915 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_conceptual_physics
+2026-04-03:16:40:15,093 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_econometrics
+2026-04-03:16:40:15,183 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_electrical_engineering
+2026-04-03:16:40:15,308 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_elementary_mathematics
+2026-04-03:16:40:15,632 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_formal_logic
+2026-04-03:16:40:15,732 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_global_facts
+2026-04-03:16:40:15,808 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_biology
+2026-04-03:16:40:16,059 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_chemistry
+2026-04-03:16:40:16,266 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_computer_science
+2026-04-03:16:40:16,375 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_european_history
+2026-04-03:16:40:16,583 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_geography
+2026-04-03:16:40:16,806 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_government_and_politics
+2026-04-03:16:40:17,022 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_macroeconomics
+2026-04-03:16:40:17,385 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_mathematics
+2026-04-03:16:40:17,684 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_microeconomics
+2026-04-03:16:40:17,929 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_physics
+2026-04-03:16:40:18,109 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_psychology
+2026-04-03:16:40:18,727 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_statistics
+2026-04-03:16:40:18,908 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_us_history
+2026-04-03:16:40:19,537 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_high_school_world_history
+2026-04-03:16:40:19,726 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_human_aging
+2026-04-03:16:40:19,895 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_human_sexuality
+2026-04-03:16:40:20,007 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_international_law
+2026-04-03:16:40:20,097 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_jurisprudence
+2026-04-03:16:40:20,195 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_logical_fallacies
+2026-04-03:16:40:20,341 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_machine_learning
+2026-04-03:16:40:20,436 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_management
+2026-04-03:16:40:20,524 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_marketing
+2026-04-03:16:40:20,789 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_medical_genetics
+2026-04-03:16:40:20,897 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_miscellaneous
+2026-04-03:16:40:21,555 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_moral_disputes
+2026-04-03:16:40:21,850 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_moral_scenarios
+2026-04-03:16:40:22,676 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_nutrition
+2026-04-03:16:40:22,944 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_philosophy
+2026-04-03:16:40:23,198 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_prehistory
+2026-04-03:16:40:23,473 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_professional_accounting
+2026-04-03:16:40:23,719 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_professional_law
+2026-04-03:16:40:25,243 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_professional_medicine
+2026-04-03:16:40:25,522 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_professional_psychology
+2026-04-03:16:40:26,010 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_public_relations
+2026-04-03:16:40:26,094 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_security_studies
+2026-04-03:16:40:26,264 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_sociology
+2026-04-03:16:40:26,404 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_us_foreign_policy
+2026-04-03:16:40:26,475 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_virology
+2026-04-03:16:40:26,587 INFO     [evaluation_tracker.py:287] Saving per-sample results for: mmlu_world_religions
+hf (pretrained=/egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
+|                 Tasks                 |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
+|---------------------------------------|------:|------|-----:|------|---|-----:|---|-----:|
+|mmlu                                   |      2|none  |      |acc   |↑  |0.5713|±  |0.0040|
+| - humanities                          |      2|none  |      |acc   |↑  |0.5186|±  |0.0069|
+|  - formal_logic                       |      1|none  |     0|acc   |↑  |0.3810|±  |0.0434|
+|  - high_school_european_history       |      1|none  |     0|acc   |↑  |0.6970|±  |0.0359|
+|  - high_school_us_history             |      1|none  |     0|acc   |↑  |0.7500|±  |0.0304|
+|  - high_school_world_history          |      1|none  |     0|acc   |↑  |0.7384|±  |0.0286|
+|  - international_law                  |      1|none  |     0|acc   |↑  |0.7107|±  |0.0414|
+|  - jurisprudence                      |      1|none  |     0|acc   |↑  |0.6944|±  |0.0445|
+|  - logical_fallacies                  |      1|none  |     0|acc   |↑  |0.6810|±  |0.0366|
+|  - moral_disputes                     |      1|none  |     0|acc   |↑  |0.6590|±  |0.0255|
+|  - moral_scenarios                    |      1|none  |     0|acc   |↑  |0.2804|±  |0.0150|
+|  - philosophy                         |      1|none  |     0|acc   |↑  |0.6463|±  |0.0272|
+|  - prehistory                         |      1|none  |     0|acc   |↑  |0.6451|±  |0.0266|
+|  - professional_law                   |      1|none  |     0|acc   |↑  |0.4231|±  |0.0126|
+|  - world_religions                    |      1|none  |     0|acc   |↑  |0.8129|±  |0.0299|
+| - other                               |      2|none  |      |acc   |↑  |0.6350|±  |0.0082|
+|  - business_ethics                    |      1|none  |     0|acc   |↑  |0.5200|±  |0.0502|
+|  - clinical_knowledge                 |      1|none  |     0|acc   |↑  |0.6491|±  |0.0294|
+|  - college_medicine                   |      1|none  |     0|acc   |↑  |0.5896|±  |0.0375|
+|  - global_facts                       |      1|none  |     0|acc   |↑  |0.3000|±  |0.0461|
+|  - human_aging                        |      1|none  |     0|acc   |↑  |0.6368|±  |0.0323|
+|  - management                         |      1|none  |     0|acc   |↑  |0.7476|±  |0.0430|
+|  - marketing                          |      1|none  |     0|acc   |↑  |0.8248|±  |0.0249|
+|  - medical_genetics                   |      1|none  |     0|acc   |↑  |0.6400|±  |0.0482|
+|  - miscellaneous                      |      1|none  |     0|acc   |↑  |0.7778|±  |0.0149|
+|  - nutrition                          |      1|none  |     0|acc   |↑  |0.6601|±  |0.0271|
+|  - professional_accounting            |      1|none  |     0|acc   |↑  |0.4149|±  |0.0294|
+|  - professional_medicine              |      1|none  |     0|acc   |↑  |0.5699|±  |0.0301|
+|  - virology                           |      1|none  |     0|acc   |↑  |0.3494|±  |0.0371|
+| - social sciences                     |      2|none  |      |acc   |↑  |0.6783|±  |0.0082|
+|  - econometrics                       |      1|none  |     0|acc   |↑  |0.4298|±  |0.0466|
+|  - high_school_geography              |      1|none  |     0|acc   |↑  |0.7172|±  |0.0321|
+|  - high_school_government_and_politics|      1|none  |     0|acc   |↑  |0.8290|±  |0.0272|
+|  - high_school_macroeconomics         |      1|none  |     0|acc   |↑  |0.5795|±  |0.0250|
+|  - high_school_microeconomics         |      1|none  |     0|acc   |↑  |0.6050|±  |0.0318|
+|  - high_school_psychology             |      1|none  |     0|acc   |↑  |0.7890|±  |0.0175|
+|  - human_sexuality                    |      1|none  |     0|acc   |↑  |0.6565|±  |0.0416|
+|  - professional_psychology            |      1|none  |     0|acc   |↑  |0.5997|±  |0.0198|
+|  - public_relations                   |      1|none  |     0|acc   |↑  |0.6545|±  |0.0455|
+|  - security_studies                   |      1|none  |     0|acc   |↑  |0.6490|±  |0.0306|
+|  - sociology                          |      1|none  |     0|acc   |↑  |0.8557|±  |0.0248|
+|  - us_foreign_policy                  |      1|none  |     0|acc   |↑  |0.8000|±  |0.0402|
+| - stem                                |      2|none  |      |acc   |↑  |0.4827|±  |0.0087|
+|  - abstract_algebra                   |      1|none  |     0|acc   |↑  |0.3000|±  |0.0461|
+|  - anatomy                            |      1|none  |     0|acc   |↑  |0.5630|±  |0.0428|
+|  - astronomy                          |      1|none  |     0|acc   |↑  |0.5855|±  |0.0401|
+|  - college_biology                    |      1|none  |     0|acc   |↑  |0.6389|±  |0.0402|
+|  - college_chemistry                  |      1|none  |     0|acc   |↑  |0.4900|±  |0.0502|
+|  - college_computer_science           |      1|none  |     0|acc   |↑  |0.5100|±  |0.0502|
+|  - college_mathematics                |      1|none  |     0|acc   |↑  |0.3700|±  |0.0485|
+|  - college_physics                    |      1|none  |     0|acc   |↑  |0.5294|±  |0.0497|
+|  - computer_security                  |      1|none  |     0|acc   |↑  |0.4800|±  |0.0502|
+|  - conceptual_physics                 |      1|none  |     0|acc   |↑  |0.4936|±  |0.0327|
+|  - electrical_engineering             |      1|none  |     0|acc   |↑  |0.5310|±  |0.0416|
+|  - elementary_mathematics             |      1|none  |     0|acc   |↑  |0.3995|±  |0.0252|
+|  - high_school_biology                |      1|none  |     0|acc   |↑  |0.6581|±  |0.0270|
+|  - high_school_chemistry              |      1|none  |     0|acc   |↑  |0.4828|±  |0.0352|
+|  - high_school_computer_science       |      1|none  |     0|acc   |↑  |0.5700|±  |0.0498|
+|  - high_school_mathematics            |      1|none  |     0|acc   |↑  |0.3370|±  |0.0288|
+|  - high_school_physics                |      1|none  |     0|acc   |↑  |0.2914|±  |0.0371|
+|  - high_school_statistics             |      1|none  |     0|acc   |↑  |0.4907|±  |0.0341|
+|  - machine_learning                   |      1|none  |     0|acc   |↑  |0.4643|±  |0.0473|
+
+|      Groups      |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
+|------------------|------:|------|------|------|---|-----:|---|-----:|
+|mmlu              |      2|none  |      |acc   |↑  |0.5713|±  |0.0040|
+| - humanities     |      2|none  |      |acc   |↑  |0.5186|±  |0.0069|
+| - other          |      2|none  |      |acc   |↑  |0.6350|±  |0.0082|
+| - social sciences|      2|none  |      |acc   |↑  |0.6783|±  |0.0082|
+| - stem           |      2|none  |      |acc   |↑  |0.4827|±  |0.0087|
+
+[INFO] (mmlu) success (found result table and non-empty json).
+[DONE] logs saved under: /egr/research-optml/wangc168/Muon_wmdp/rmu_wmdp/models/muon_v1_test_V1_K64_batches150_bs4_alpha1200-1200_steer15-15_muonlr2.6e-3_muonmom0.95_muonwd0.1_adamlr5e-5_seed42_layer7_layers5-6-7_params6/lm_eval_logs