/opt/conda/envs/copygen/lib/python3.12/site-packages/requests/__init__.py:113: RequestsDependencyWarning: urllib3 (2.6.3) or chardet (6.0.0.post1)/charset_normalizer (3.4.4) doesn't match a supported version!
  warnings.warn(
[2026-07-22 16:22:07,461][evaluator][INFO] - Evaluations stored in the experiment directory: ./saves/eval/AltPO_lr2e-05_beta0.05_alpha2_epoch5
[2026-07-22 16:22:07,462][evaluator][INFO] - ***** Running TOFU evaluation suite *****
[2026-07-22 16:22:07,462][evaluator][INFO] - Fine-grained evaluations will be saved to: ./saves/eval/AltPO_lr2e-05_beta0.05_alpha2_epoch5/TOFU_EVAL.json
[2026-07-22 16:22:07,462][evaluator][INFO] - Aggregated evaluations will be summarised in: ./saves/eval/AltPO_lr2e-05_beta0.05_alpha2_epoch5/TOFU_SUMMARY.json
[2026-07-22 16:22:08,141][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob

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Calculating loss: 100%|██████████| 13/13 [00:03<00:00,  4.17it/s]
Evaluated 400 examples
[2026-07-22 16:22:11,576][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob

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Calculating loss: 100%|██████████| 13/13 [00:14<00:00,  1.11s/it]
Evaluated 400 examples
[2026-07-22 16:22:26,017][metrics][INFO] - Evaluating forget_truth_ratio
[2026-07-22 16:22:26,018][evaluator][INFO] - Result for metric forget_truth_ratio:	0.6896102534211721
[2026-07-22 16:22:26,022][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated.
[2026-07-22 16:22:26,022][metrics][INFO] - Loading evaluations from /workspace/jspace-unlearning/refs/tofu_Llama-3.2-1B-Instruct_retain90__TOFU_EVAL.json
[2026-07-22 16:22:26,029][metrics][INFO] - Evaluating forget_quality
[2026-07-22 16:22:26,029][evaluator][INFO] - Result for metric forget_quality:	0.07829787069468755
[2026-07-22 16:22:26,361][metrics][INFO] - Evaluating forget_Q_A_Prob

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Calculating loss: 100%|██████████| 13/13 [00:02<00:00,  4.54it/s]
Evaluated 400 examples
[2026-07-22 16:22:29,224][evaluator][INFO] - Result for metric forget_Q_A_Prob:	0.22479034423828126
[2026-07-22 16:22:29,471][metrics][INFO] - Evaluating forget_Q_A_ROUGE

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Calculating text similarity: 100%|██████████| 13/13 [00:05<00:00,  2.17it/s]
Evaluated 400 examples
[2026-07-22 16:22:35,449][evaluator][INFO] - Result for metric forget_Q_A_ROUGE:	0.33533202226410636
[2026-07-22 16:22:35,847][metrics][INFO] - Evaluating retain_Q_A_Prob

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Calculating loss: 100%|██████████| 13/13 [00:02<00:00,  4.69it/s]
Evaluated 400 examples
[2026-07-22 16:22:38,953][metrics][INFO] - Evaluating retain_Q_A_ROUGE

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Calculating text similarity: 100%|██████████| 13/13 [00:04<00:00,  2.78it/s]
Evaluated 400 examples
[2026-07-22 16:22:43,869][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob

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Calculating loss: 100%|██████████| 13/13 [00:02<00:00,  4.58it/s]
Evaluated 400 examples
[2026-07-22 16:22:46,946][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob

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Calculating loss: 100%|██████████| 13/13 [00:14<00:00,  1.08s/it]
Evaluated 400 examples
[2026-07-22 16:23:00,999][metrics][INFO] - Evaluating retain_Truth_Ratio
[2026-07-22 16:23:01,348][metrics][INFO] - Evaluating ra_Q_A_Prob

Calculating loss:   0%|          | 0/4 [00:00<?, ?it/s]
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Calculating loss: 100%|██████████| 4/4 [00:00<00:00,  7.39it/s]
Evaluated 100 examples
[2026-07-22 16:23:02,142][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob

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Calculating loss: 100%|██████████| 4/4 [00:01<00:00,  2.49it/s]
Evaluated 100 examples
[2026-07-22 16:23:03,749][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised
[2026-07-22 16:23:03,999][metrics][INFO] - Evaluating ra_Q_A_ROUGE

Calculating text similarity:   0%|          | 0/4 [00:00<?, ?it/s]
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Calculating text similarity: 100%|██████████| 4/4 [00:01<00:00,  4.29it/s]
Calculating text similarity: 100%|██████████| 4/4 [00:01<00:00,  3.95it/s]
Evaluated 100 examples
[2026-07-22 16:23:05,013][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated.
[2026-07-22 16:23:05,013][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated.
[2026-07-22 16:23:05,013][metrics][INFO] - Evaluating ra_Truth_Ratio
[2026-07-22 16:23:05,255][metrics][INFO] - Evaluating wf_Q_A_Prob

Calculating loss:   0%|          | 0/4 [00:00<?, ?it/s]
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Calculating loss: 100%|██████████| 4/4 [00:00<00:00,  8.12it/s]
Evaluated 117 examples
[2026-07-22 16:23:05,993][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob

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Calculating loss: 100%|██████████| 4/4 [00:01<00:00,  2.59it/s]
Evaluated 117 examples
[2026-07-22 16:23:07,539][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised
[2026-07-22 16:23:07,844][metrics][INFO] - Evaluating wf_Q_A_ROUGE

Calculating text similarity:   0%|          | 0/4 [00:00<?, ?it/s]
Calculating text similarity:  25%|██▌       | 1/4 [00:00<00:00,  4.51it/s]
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Calculating text similarity: 100%|██████████| 4/4 [00:00<00:00,  4.30it/s]
Calculating text similarity: 100%|██████████| 4/4 [00:00<00:00,  4.27it/s]
Evaluated 117 examples
[2026-07-22 16:23:08,780][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated.
[2026-07-22 16:23:08,780][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated.
[2026-07-22 16:23:08,781][metrics][INFO] - Evaluating wf_Truth_Ratio
[2026-07-22 16:23:08,781][metrics][INFO] - Evaluating model_utility
[2026-07-22 16:23:08,781][evaluator][INFO] - Result for metric model_utility:	0.46206320826459485
[2026-07-22 16:23:09,269][metrics][INFO] - Loading evaluations from /workspace/jspace-unlearning/refs/tofu_Llama-3.2-1B-Instruct_retain90__TOFU_EVAL.json
[2026-07-22 16:23:09,280][metrics][INFO] - Evaluating mia_min_k

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[2026-07-22 16:23:11,148][metrics][INFO] - Loading evaluations from /workspace/jspace-unlearning/refs/tofu_Llama-3.2-1B-Instruct_retain90__TOFU_EVAL.json
[2026-07-22 16:23:11,154][metrics][INFO] - Evaluating privleak
[2026-07-22 16:23:11,155][evaluator][INFO] - Result for metric privleak:	-50.34166142814009
[2026-07-22 16:23:11,468][metrics][INFO] - Evaluating extraction_strength

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Calculating ES:  92%|█████████▏| 12/13 [00:01<00:00, 10.73it/s]
Calculating ES: 100%|██████████| 13/13 [00:01<00:00, 11.20it/s]
Evaluated 400 examples
[2026-07-22 16:23:12,629][evaluator][INFO] - Result for metric extraction_strength:	0.053313347966882196