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<|endoftext|>\n32 -100 A\n8633 -100 factory\n18644 -100 produces\n220 -100 \n18 -100 3\n20 -100 5\n15 -100 0\n42632 -100 cans\n315 -100 of\n38862 -100 soda\n817 -100 per\n6460 -100 hour\n13 -100 .\n1416 -100 If\n279 -100 the\n8633 -100 factory\n26057 -100 operates\n369 -100 for\n220 -100 \n16 -100 1\n15 -100 0\n4115 -100 hours\n264 -100 a\n1899 -100 day\n323 -100 and\n4278 -100 works\n220 -100 \n20 -100 5\n2849 -100 days\n264 -100 a\n2003 -100 week\n11 -100 ,\n1246 -100 how\n1657 -100 many\n42632 -100 cans\n315 -100 of\n38862 -100 soda\n1558 -100 does\n432 -100 it\n8193 -100 produce\n304 -100 in\n264 -100 a\n2003 -100 week\n5267 -100 ?\n\n151669 -100 <|start-latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n16 16 1\n22 22 7\n20 20 5\n15 15 0\n15 15 0\n151643 151643 <|endoftext|>\n========================================\n151643 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2\n15 -100 0\n4 -100 %\n311 -100 to\n34645 -100 Karen\n11 -100 ,\n323 -100 and\n279 -100 the\n2732 -100 rest\n311 -100 to\n50106 -100 Joan\n13 -100 .\n2585 -100 How\n1657 -100 many\n8279 -100 cookies\n686 -100 will\n50106 -100 Joan\n633 -100 get\n5267 -100 ?\n\n151669 -100 <|start-latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n21 21 6\n21 21 6\n151643 151643 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n========================================\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 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###\n220 220 \n17 17 2\n15 15 0\n15 15 0\n151643 151643 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n========================================\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 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<|endoftext|>\n32 -100 A\n8633 -100 factory\n18644 -100 produces\n220 -100 \n18 -100 3\n20 -100 5\n15 -100 0\n42632 -100 cans\n315 -100 of\n38862 -100 soda\n817 -100 per\n6460 -100 hour\n13 -100 .\n1416 -100 If\n279 -100 the\n8633 -100 factory\n26057 -100 operates\n369 -100 for\n220 -100 \n16 -100 1\n15 -100 0\n4115 -100 hours\n264 -100 a\n1899 -100 day\n323 -100 and\n4278 -100 works\n220 -100 \n20 -100 5\n2849 -100 days\n264 -100 a\n2003 -100 week\n11 -100 ,\n1246 -100 how\n1657 -100 many\n42632 -100 cans\n315 -100 of\n38862 -100 soda\n1558 -100 does\n432 -100 it\n8193 -100 produce\n304 -100 in\n264 -100 a\n2003 -100 week\n5267 -100 ?\n\n151669 -100 <|start-latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n16 16 1\n22 22 7\n20 20 5\n15 15 0\n15 15 0\n151643 151643 <|endoftext|>\n========================================\n151643 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<|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n16 16 1\n20 20 5\n151643 151643 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n========================================\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 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-100 bought\n220 -100 \n19 -100 4\n20830 -100 concert\n14403 -100 tickets\n448 -100 with\n279 -100 the\n3220 -100 money\n13 -100 .\n8886 -100 Each\n11727 -100 ticket\n2783 -100 cost\n400 -100 $\n16 -100 1\n16 -100 1\n323 -100 and\n8224 -100 Sam\n7171 -100 paid\n369 -100 for\n678 -100 all\n3040 -100 four\n14403 -100 tickets\n13 -100 .\n8224 -100 Sam\n6551 -100 gave\n279 -100 the\n2732 -100 rest\n315 -100 of\n806 -100 his\n3220 -100 money\n311 -100 to\n36273 -100 Pete\n13 -100 .\n1416 -100 If\n36273 -100 Pete\n2669 -100 already\n1030 -100 had\n400 -100 $\n19 -100 4\n15 -100 0\n11 -100 ,\n1246 -100 how\n1753 -100 much\n3220 -100 money\n1558 -100 does\n36273 -100 Pete\n614 -100 have\n1431 -100 now\n5267 -100 ?\n\n151669 -100 <|start-latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n20 20 5\n21 21 6\n151643 151643 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n========================================\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n47681 -100 Kevin\n374 -100 is\n2326 -100 three\n3039 -100 times\n96357 -100 Lola\n594 -100 's\n4231 -100 age\n13 -100 .\n13294 -100 Pen\n81565 -100 elope\n374 -100 is\n220 -100 \n20 -100 5\n1635 -100 years\n9014 -100 older\n1091 -100 than\n16364 -100 Kevin\n13 -100 .\n1416 -100 If\n96357 -100 Lola\n374 -100 is\n220 -100 \n23 -100 8\n11 -100 ,\n1246 -100 how\n1657 -100 many\n1635 -100 years\n2310 -100 old\n374 -100 is\n13294 -100 Pen\n81565 -100 elope\n5267 -100 ?\n\n151669 -100 <|start-latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151671 -100 <|latent|>\n151670 -100 <|end-latent|>\n14374 14374 ###\n220 220 \n17 17 2\n24 24 9\n151643 151643 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n151643 -100 <|endoftext|>\n========================================\n"]]}
wandb/run-20260413_074459-5pydxgbu/files/media/table/data_table_56382_cad700199a89c404ab81.table.json ADDED
The diff for this file is too large to render. See raw diff
 
wandb/run-20260413_074459-5pydxgbu/files/media/table/data_table_59396_619e8e2047bd45dc12c0.table.json ADDED
The diff for this file is too large to render. See raw diff
 
wandb/run-20260413_074459-5pydxgbu/files/media/table/data_table_62410_2ba9e5a2461fe4987a6d.table.json ADDED
The diff for this file is too large to render. See raw diff
 
wandb/run-20260413_074459-5pydxgbu/files/media/table/data_table_8561_d1082655dfb898cefe09.table.json ADDED
The diff for this file is too large to render. See raw diff
 
wandb/run-20260413_074459-5pydxgbu/files/output.log ADDED
@@ -0,0 +1,1420 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ============================================================
3
+ EPOCH 18/50 (thought_stage=3, data_stage=3)
4
+ ============================================================
5
+ n_latent_tokens=6, max_difficulty=3
6
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2424.69 examples/s]
7
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:05<00:00, 74489.96 examples/s]
8
+ Map (num_proc=4): 100%|██████████| 358844/358844 [00:20<00:00, 17848.58 examples/s]
9
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2355.57 examples/s]
10
+ Map (num_proc=4): 100%|██████████| 412/412 [00:00<00:00, 1514.29 examples/s]
11
+ Training Epoch: 19/50, batch 2803/2804 completed (loss: 0.0262: 100%|██████████| 2804/2804 [1:06:37<00:00, 1.43s/it]
12
+ logging training data
13
+ rank 0 | gpu 0 | alloc=4.85 GB | reserved=24.83 GB | max=18.80 GB
14
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
15
+ return func(*args, **kwargs)
16
+ saving model.
17
+ eval loss 0.8143050968647003
18
+ Test accuracy: 0.16: 100%|██████████| 8/8 [00:34<00:00, 4.26s/it]
19
+
20
+ [WRONG] Example 0 (diff=3)
21
+ Expected : '300'
22
+ Extracted: '200'
23
+ Generated: '### 200<|endoftext|><|endoftext|>'
24
+
25
+ [WRONG] Example 1 (diff=3)
26
+ Expected : '25'
27
+ Extracted: '23.5'
28
+ Generated: '### 23.5<|endoftext|>'
29
+
30
+ [WRONG] Example 2 (diff=2)
31
+ Expected : '45'
32
+ Extracted: '35'
33
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|>'
34
+
35
+ [CORRECT] Example 3 (diff=2)
36
+ Expected : '12000'
37
+ Extracted: '12000'
38
+ Generated: '### 12000'
39
+
40
+ [WRONG] Example 4 (diff=2)
41
+ Expected : '55'
42
+ Extracted: '95'
43
+ Generated: '### 95<|endoftext|><|endoftext|><|endoftext|>'
44
+ Device 0: Cor=10, CoT=0, Total=62
45
+
46
+ ============================================================
47
+ EVALUATION RESULTS (Epoch 18)
48
+ ============================================================
49
+ Config: data_curriculum=ON, thought_curriculum=ON
50
+ thought_stage=3, data_stage=3, n_latent_tokens=6
51
+ Training on difficulties: 0-3
52
+
53
+ Overall accuracy: 144 / 496 = 0.2903
54
+
55
+ Per-difficulty accuracy:
56
+ diff=0: 10/26 = 0.3846 (trained)
57
+ diff=1: 83/155 = 0.5355 (trained)
58
+ diff=2: 36/141 = 0.2553 (trained)
59
+ diff=3: 10/92 = 0.1087 (trained)
60
+ diff=4: 2/53 = 0.0377 (untrained)
61
+ diff=5: 3/18 = 0.1667 (untrained)
62
+ diff=6: 0/8 = 0.0000 (untrained)
63
+ diff=7: 0/3 = 0.0000 (untrained)
64
+
65
+ Trained (diff 0-3): 139/414 = 0.3357
66
+ ============================================================
67
+
68
+
69
+ ============================================================
70
+ EPOCH 19/50 (thought_stage=3, data_stage=3)
71
+ ============================================================
72
+ n_latent_tokens=6, max_difficulty=3
73
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2278.86 examples/s]
74
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:05<00:00, 74866.91 examples/s]
75
+ Map (num_proc=4): 100%|██████████| 358844/358844 [00:20<00:00, 17333.20 examples/s]
76
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2201.26 examples/s]
77
+ Map (num_proc=4): 100%|██████████| 412/412 [00:00<00:00, 1424.76 examples/s]
78
+ Training Epoch: 20/50, batch 2803/2804 completed (loss: 0.1343: 100%|██████████| 2804/2804 [1:06:36<00:00, 1.43s/it]
79
+ logging training data
80
+ rank 0 | gpu 0 | alloc=5.48 GB | reserved=26.19 GB | max=27.38 GB
81
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
82
+ return func(*args, **kwargs)
83
+ saving model.
84
+ eval loss 0.9176397323608398
85
+ Test accuracy: 0.23: 100%|██████████| 8/8 [00:34<00:00, 4.26s/it]
86
+
87
+ [WRONG] Example 0 (diff=3)
88
+ Expected : '300'
89
+ Extracted: '200'
90
+ Generated: '### 200<|endoftext|>'
91
+
92
+ [WRONG] Example 1 (diff=3)
93
+ Expected : '25'
94
+ Extracted: '21.5'
95
+ Generated: '### 21.5'
96
+
97
+ [CORRECT] Example 2 (diff=2)
98
+ Expected : '45'
99
+ Extracted: '45'
100
+ Generated: '### 45<|endoftext|><|endoftext|>'
101
+
102
+ [WRONG] Example 3 (diff=2)
103
+ Expected : '12000'
104
+ Extracted: '6000'
105
+ Generated: '### 6000'
106
+
107
+ [CORRECT] Example 4 (diff=2)
108
+ Expected : '55'
109
+ Extracted: '55'
110
+ Generated: '### 55<|endoftext|><|endoftext|>'
111
+ Device 0: Cor=14, CoT=0, Total=62
112
+
113
+ ============================================================
114
+ EVALUATION RESULTS (Epoch 19)
115
+ ============================================================
116
+ Config: data_curriculum=ON, thought_curriculum=ON
117
+ thought_stage=3, data_stage=3, n_latent_tokens=6
118
+ Training on difficulties: 0-3
119
+
120
+ Overall accuracy: 143 / 496 = 0.2883
121
+
122
+ Per-difficulty accuracy:
123
+ diff=0: 10/26 = 0.3846 (trained)
124
+ diff=1: 81/155 = 0.5226 (trained)
125
+ diff=2: 33/141 = 0.2340 (trained)
126
+ diff=3: 14/92 = 0.1522 (trained)
127
+ diff=4: 3/53 = 0.0566 (untrained)
128
+ diff=5: 1/18 = 0.0556 (untrained)
129
+ diff=6: 1/8 = 0.1250 (untrained)
130
+ diff=7: 0/3 = 0.0000 (untrained)
131
+
132
+ Trained (diff 0-3): 138/414 = 0.3333
133
+ ============================================================
134
+
135
+
136
+ ============================================================
137
+ EPOCH 20/50 (thought_stage=4, data_stage=4)
138
+ ============================================================
139
+ n_latent_tokens=8, max_difficulty=4
140
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2878.86 examples/s]
141
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61897.85 examples/s]
142
+ Map (num_proc=4): 100%|██████████| 376750/376750 [00:22<00:00, 16385.22 examples/s]
143
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2235.05 examples/s]
144
+ Map (num_proc=4): 100%|██████████| 465/465 [00:00<00:00, 1599.04 examples/s]
145
+ Training Epoch: 21/50, batch 2943/2944 completed (loss: 0.0284: 100%|██████████| 2944/2944 [1:32:51<00:00, 1.89s/it]
146
+ logging training data
147
+ rank 0 | gpu 0 | alloc=5.27 GB | reserved=22.87 GB | max=29.93 GB
148
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
149
+ return func(*args, **kwargs)
150
+ saving model.
151
+ eval loss 0.7872725427150726
152
+ Test accuracy: 0.18: 100%|██████████| 8/8 [00:34<00:00, 4.26s/it]
153
+
154
+ [WRONG] Example 0 (diff=3)
155
+ Expected : '300'
156
+ Extracted: '180'
157
+ Generated: '### 180<|endoftext|><|endoftext|>'
158
+
159
+ [WRONG] Example 1 (diff=3)
160
+ Expected : '25'
161
+ Extracted: '21.50'
162
+ Generated: '### 21.50'
163
+
164
+ [CORRECT] Example 2 (diff=2)
165
+ Expected : '45'
166
+ Extracted: '45'
167
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
168
+
169
+ [WRONG] Example 3 (diff=2)
170
+ Expected : '12000'
171
+ Extracted: '5000'
172
+ Generated: '### 5000<|endoftext|>'
173
+
174
+ [WRONG] Example 4 (diff=2)
175
+ Expected : '55'
176
+ Extracted: '5'
177
+ Generated: '### 5<|endoftext|><|endoftext|><|endoftext|><|endoftext|>'
178
+ Device 0: Cor=11, CoT=0, Total=62
179
+
180
+ ============================================================
181
+ EVALUATION RESULTS (Epoch 20)
182
+ ============================================================
183
+ Config: data_curriculum=ON, thought_curriculum=ON
184
+ thought_stage=4, data_stage=4, n_latent_tokens=8
185
+ Training on difficulties: 0-4
186
+
187
+ Overall accuracy: 154 / 496 = 0.3105
188
+
189
+ Per-difficulty accuracy:
190
+ diff=0: 11/26 = 0.4231 (trained)
191
+ diff=1: 83/155 = 0.5355 (trained)
192
+ diff=2: 42/141 = 0.2979 (trained)
193
+ diff=3: 12/92 = 0.1304 (trained)
194
+ diff=4: 4/53 = 0.0755 (trained)
195
+ diff=5: 2/18 = 0.1111 (untrained)
196
+ diff=6: 0/8 = 0.0000 (untrained)
197
+ diff=7: 0/3 = 0.0000 (untrained)
198
+
199
+ Trained (diff 0-4): 152/467 = 0.3255
200
+ ============================================================
201
+
202
+
203
+ ============================================================
204
+ EPOCH 21/50 (thought_stage=4, data_stage=4)
205
+ ============================================================
206
+ n_latent_tokens=8, max_difficulty=4
207
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2760.79 examples/s]
208
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 62910.66 examples/s]
209
+ Map (num_proc=4): 100%|██████████| 376750/376750 [00:22<00:00, 16849.28 examples/s]
210
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2008.32 examples/s]
211
+ Map (num_proc=4): 100%|██████████| 465/465 [00:00<00:00, 1605.76 examples/s]
212
+ Training Epoch: 22/50, batch 2943/2944 completed (loss: 0.0855: 100%|██████████| 2944/2944 [1:33:03<00:00, 1.90s/it]
213
+ logging training data
214
+ rank 0 | gpu 0 | alloc=5.39 GB | reserved=25.72 GB | max=29.93 GB
215
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
216
+ return func(*args, **kwargs)
217
+ saving model.
218
+ eval loss 0.9247933626174927
219
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.25s/it]
220
+
221
+ [WRONG] Example 0 (diff=3)
222
+ Expected : '300'
223
+ Extracted: '180'
224
+ Generated: '### 180<|endoftext|><|endoftext|>'
225
+
226
+ [WRONG] Example 1 (diff=3)
227
+ Expected : '25'
228
+ Extracted: '24.5'
229
+ Generated: '### 24.5<|endoftext|>'
230
+
231
+ [CORRECT] Example 2 (diff=2)
232
+ Expected : '45'
233
+ Extracted: '45'
234
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
235
+
236
+ [WRONG] Example 3 (diff=2)
237
+ Expected : '12000'
238
+ Extracted: '14000'
239
+ Generated: '### 14000'
240
+
241
+ [WRONG] Example 4 (diff=2)
242
+ Expected : '55'
243
+ Extracted: '15'
244
+ Generated: '### 15<|endoftext|><|endoftext|><|endoftext|>'
245
+ Device 0: Cor=12, CoT=0, Total=62
246
+
247
+ ============================================================
248
+ EVALUATION RESULTS (Epoch 21)
249
+ ============================================================
250
+ Config: data_curriculum=ON, thought_curriculum=ON
251
+ thought_stage=4, data_stage=4, n_latent_tokens=8
252
+ Training on difficulties: 0-4
253
+
254
+ Overall accuracy: 154 / 496 = 0.3105
255
+
256
+ Per-difficulty accuracy:
257
+ diff=0: 12/26 = 0.4615 (trained)
258
+ diff=1: 84/155 = 0.5419 (trained)
259
+ diff=2: 37/141 = 0.2624 (trained)
260
+ diff=3: 15/92 = 0.1630 (trained)
261
+ diff=4: 4/53 = 0.0755 (trained)
262
+ diff=5: 2/18 = 0.1111 (untrained)
263
+ diff=6: 0/8 = 0.0000 (untrained)
264
+ diff=7: 0/3 = 0.0000 (untrained)
265
+
266
+ Trained (diff 0-4): 152/467 = 0.3255
267
+ ============================================================
268
+
269
+
270
+ ============================================================
271
+ EPOCH 22/50 (thought_stage=4, data_stage=4)
272
+ ============================================================
273
+ n_latent_tokens=8, max_difficulty=4
274
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2839.12 examples/s]
275
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 62523.06 examples/s]
276
+ Map (num_proc=4): 100%|██████████| 376750/376750 [00:22<00:00, 16609.97 examples/s]
277
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2009.34 examples/s]
278
+ Map (num_proc=4): 100%|██████████| 465/465 [00:00<00:00, 1513.68 examples/s]
279
+ Training Epoch: 23/50, batch 2943/2944 completed (loss: 0.254: 100%|██████████| 2944/2944 [1:33:01<00:00, 1.90s/it]
280
+ logging training data
281
+ rank 0 | gpu 0 | alloc=5.26 GB | reserved=23.23 GB | max=31.83 GB
282
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
283
+ return func(*args, **kwargs)
284
+ saving model.
285
+ eval loss 0.9715301245450974
286
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.27s/it]
287
+
288
+ [WRONG] Example 0 (diff=3)
289
+ Expected : '300'
290
+ Extracted: '200'
291
+ Generated: '### 200<|endoftext|>'
292
+
293
+ [WRONG] Example 1 (diff=3)
294
+ Expected : '25'
295
+ Extracted: '21'
296
+ Generated: '### 21<|endoftext|><|endoftext|>'
297
+
298
+ [CORRECT] Example 2 (diff=2)
299
+ Expected : '45'
300
+ Extracted: '45'
301
+ Generated: '### 45<|endoftext|><|endoftext|>'
302
+
303
+ [WRONG] Example 3 (diff=2)
304
+ Expected : '12000'
305
+ Extracted: '6000'
306
+ Generated: '### 6000'
307
+
308
+ [WRONG] Example 4 (diff=2)
309
+ Expected : '55'
310
+ Extracted: '35'
311
+ Generated: '### 35<|endoftext|><|endoftext|>'
312
+ Device 0: Cor=12, CoT=0, Total=62
313
+
314
+ ============================================================
315
+ EVALUATION RESULTS (Epoch 22)
316
+ ============================================================
317
+ Config: data_curriculum=ON, thought_curriculum=ON
318
+ thought_stage=4, data_stage=4, n_latent_tokens=8
319
+ Training on difficulties: 0-4
320
+
321
+ Overall accuracy: 151 / 496 = 0.3044
322
+
323
+ Per-difficulty accuracy:
324
+ diff=0: 7/26 = 0.2692 (trained)
325
+ diff=1: 87/155 = 0.5613 (trained)
326
+ diff=2: 39/141 = 0.2766 (trained)
327
+ diff=3: 13/92 = 0.1413 (trained)
328
+ diff=4: 3/53 = 0.0566 (trained)
329
+ diff=5: 2/18 = 0.1111 (untrained)
330
+ diff=6: 0/8 = 0.0000 (untrained)
331
+ diff=7: 0/3 = 0.0000 (untrained)
332
+
333
+ Trained (diff 0-4): 149/467 = 0.3191
334
+ ============================================================
335
+
336
+
337
+ ============================================================
338
+ EPOCH 23/50 (thought_stage=4, data_stage=4)
339
+ ============================================================
340
+ n_latent_tokens=8, max_difficulty=4
341
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2673.21 examples/s]
342
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61740.96 examples/s]
343
+ Map (num_proc=4): 100%|██████████| 376750/376750 [00:22<00:00, 16434.39 examples/s]
344
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1686.87 examples/s]
345
+ Map (num_proc=4): 100%|██████████| 465/465 [00:00<00:00, 1407.84 examples/s]
346
+ Training Epoch: 24/50, batch 2943/2944 completed (loss: 0.0192: 100%|██████████| 2944/2944 [1:33:18<00:00, 1.90s/it]
347
+ logging training data
348
+ rank 0 | gpu 0 | alloc=5.65 GB | reserved=31.52 GB | max=38.24 GB
349
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
350
+ return func(*args, **kwargs)
351
+ saving model.
352
+ eval loss 1.107031226158142
353
+ Test accuracy: 0.15: 100%|██████████| 8/8 [00:34<00:00, 4.26s/it]
354
+
355
+ [WRONG] Example 0 (diff=3)
356
+ Expected : '300'
357
+ Extracted: '180'
358
+ Generated: '### 180<|endoftext|><|endoftext|>'
359
+
360
+ [WRONG] Example 1 (diff=3)
361
+ Expected : '25'
362
+ Extracted: '20'
363
+ Generated: '### 20<|endoftext|><|endoftext|><|endoftext|>'
364
+
365
+ [CORRECT] Example 2 (diff=2)
366
+ Expected : '45'
367
+ Extracted: '45'
368
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
369
+
370
+ [CORRECT] Example 3 (diff=2)
371
+ Expected : '12000'
372
+ Extracted: '12000'
373
+ Generated: '### 12000'
374
+
375
+ [WRONG] Example 4 (diff=2)
376
+ Expected : '55'
377
+ Extracted: '35'
378
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|>'
379
+ Device 0: Cor=9, CoT=0, Total=62
380
+
381
+ ============================================================
382
+ EVALUATION RESULTS (Epoch 23)
383
+ ============================================================
384
+ Config: data_curriculum=ON, thought_curriculum=ON
385
+ thought_stage=4, data_stage=4, n_latent_tokens=8
386
+ Training on difficulties: 0-4
387
+
388
+ Overall accuracy: 151 / 496 = 0.3044
389
+
390
+ Per-difficulty accuracy:
391
+ diff=0: 10/26 = 0.3846 (trained)
392
+ diff=1: 79/155 = 0.5097 (trained)
393
+ diff=2: 40/141 = 0.2837 (trained)
394
+ diff=3: 10/92 = 0.1087 (trained)
395
+ diff=4: 7/53 = 0.1321 (trained)
396
+ diff=5: 3/18 = 0.1667 (untrained)
397
+ diff=6: 2/8 = 0.2500 (untrained)
398
+ diff=7: 0/3 = 0.0000 (untrained)
399
+
400
+ Trained (diff 0-4): 146/467 = 0.3126
401
+ ============================================================
402
+
403
+
404
+ ============================================================
405
+ EPOCH 24/50 (thought_stage=4, data_stage=4)
406
+ ============================================================
407
+ n_latent_tokens=8, max_difficulty=4
408
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2780.54 examples/s]
409
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 62489.04 examples/s]
410
+ Map (num_proc=4): 100%|██████████| 376750/376750 [00:22<00:00, 16403.02 examples/s]
411
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1928.16 examples/s]
412
+ Map (num_proc=4): 100%|██████████| 465/465 [00:00<00:00, 1284.73 examples/s]
413
+ Training Epoch: 25/50, batch 2943/2944 completed (loss: 0.007: 100%|██████████| 2944/2944 [1:33:15<00:00, 1.90s/it]
414
+ logging training data
415
+ rank 0 | gpu 0 | alloc=5.40 GB | reserved=26.62 GB | max=38.24 GB
416
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
417
+ return func(*args, **kwargs)
418
+ saving model.
419
+ eval loss 0.9769439399242401
420
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.28s/it]
421
+
422
+ [CORRECT] Example 0 (diff=3)
423
+ Expected : '300'
424
+ Extracted: '300'
425
+ Generated: '### 300<|endoftext|><|endoftext|>'
426
+
427
+ [WRONG] Example 1 (diff=3)
428
+ Expected : '25'
429
+ Extracted: '23.50'
430
+ Generated: '### 23.50'
431
+
432
+ [CORRECT] Example 2 (diff=2)
433
+ Expected : '45'
434
+ Extracted: '45'
435
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
436
+
437
+ [CORRECT] Example 3 (diff=2)
438
+ Expected : '12000'
439
+ Extracted: '12000'
440
+ Generated: '### 12000'
441
+
442
+ [WRONG] Example 4 (diff=2)
443
+ Expected : '55'
444
+ Extracted: '35'
445
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|>'
446
+ Device 0: Cor=12, CoT=0, Total=62
447
+
448
+ ============================================================
449
+ EVALUATION RESULTS (Epoch 24)
450
+ ============================================================
451
+ Config: data_curriculum=ON, thought_curriculum=ON
452
+ thought_stage=4, data_stage=4, n_latent_tokens=8
453
+ Training on difficulties: 0-4
454
+
455
+ Overall accuracy: 144 / 496 = 0.2903
456
+
457
+ Per-difficulty accuracy:
458
+ diff=0: 11/26 = 0.4231 (trained)
459
+ diff=1: 78/155 = 0.5032 (trained)
460
+ diff=2: 39/141 = 0.2766 (trained)
461
+ diff=3: 12/92 = 0.1304 (trained)
462
+ diff=4: 2/53 = 0.0377 (trained)
463
+ diff=5: 2/18 = 0.1111 (untrained)
464
+ diff=6: 0/8 = 0.0000 (untrained)
465
+ diff=7: 0/3 = 0.0000 (untrained)
466
+
467
+ Trained (diff 0-4): 142/467 = 0.3041
468
+ ============================================================
469
+
470
+
471
+ ============================================================
472
+ EPOCH 25/50 (thought_stage=5, data_stage=5)
473
+ ============================================================
474
+ n_latent_tokens=10, max_difficulty=5
475
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2727.84 examples/s]
476
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61239.32 examples/s]
477
+ Map (num_proc=4): 100%|██████████| 382416/382416 [00:23<00:00, 16031.31 examples/s]
478
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1783.86 examples/s]
479
+ Map (num_proc=4): 100%|██████████| 483/483 [00:00<00:00, 1333.28 examples/s]
480
+ Training Epoch: 26/50, batch 2987/2988 completed (loss: 0.0003: 100%|██████████| 2988/2988 [1:57:48<00:00, 2.37s/it]
481
+ logging training data
482
+ rank 0 | gpu 0 | alloc=5.32 GB | reserved=24.68 GB | max=38.24 GB
483
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
484
+ return func(*args, **kwargs)
485
+ saving model.
486
+ eval loss 0.9224649369716644
487
+ Test accuracy: 0.16: 100%|██████████| 8/8 [00:34<00:00, 4.29s/it]
488
+
489
+ [WRONG] Example 0 (diff=3)
490
+ Expected : '300'
491
+ Extracted: '200'
492
+ Generated: '### 200<|endoftext|><|endoftext|>'
493
+
494
+ [WRONG] Example 1 (diff=3)
495
+ Expected : '25'
496
+ Extracted: '18'
497
+ Generated: '### 18<|endoftext|><|endoftext|><|endoftext|>'
498
+
499
+ [CORRECT] Example 2 (diff=2)
500
+ Expected : '45'
501
+ Extracted: '45'
502
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
503
+
504
+ [CORRECT] Example 3 (diff=2)
505
+ Expected : '12000'
506
+ Extracted: '12000'
507
+ Generated: '### 12000'
508
+
509
+ [WRONG] Example 4 (diff=2)
510
+ Expected : '55'
511
+ Extracted: '35'
512
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|>'
513
+ Device 0: Cor=10, CoT=0, Total=62
514
+
515
+ ============================================================
516
+ EVALUATION RESULTS (Epoch 25)
517
+ ============================================================
518
+ Config: data_curriculum=ON, thought_curriculum=ON
519
+ thought_stage=5, data_stage=5, n_latent_tokens=10
520
+ Training on difficulties: 0-5
521
+
522
+ Overall accuracy: 155 / 496 = 0.3125
523
+
524
+ Per-difficulty accuracy:
525
+ diff=0: 12/26 = 0.4615 (trained)
526
+ diff=1: 85/155 = 0.5484 (trained)
527
+ diff=2: 37/141 = 0.2624 (trained)
528
+ diff=3: 12/92 = 0.1304 (trained)
529
+ diff=4: 4/53 = 0.0755 (trained)
530
+ diff=5: 3/18 = 0.1667 (trained)
531
+ diff=6: 2/8 = 0.2500 (untrained)
532
+ diff=7: 0/3 = 0.0000 (untrained)
533
+
534
+ Trained (diff 0-5): 153/485 = 0.3155
535
+ ============================================================
536
+
537
+
538
+ ============================================================
539
+ EPOCH 26/50 (thought_stage=5, data_stage=5)
540
+ ============================================================
541
+ n_latent_tokens=10, max_difficulty=5
542
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2713.57 examples/s]
543
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61182.57 examples/s]
544
+ Map (num_proc=4): 100%|██████████| 382416/382416 [00:22<00:00, 16760.20 examples/s]
545
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1891.51 examples/s]
546
+ Map (num_proc=4): 100%|██████████| 483/483 [00:00<00:00, 1470.42 examples/s]
547
+ Training Epoch: 27/50, batch 2987/2988 completed (loss: 0.001: 100%|██████████| 2988/2988 [1:59:01<00:00, 2.39s/it]
548
+ logging training data
549
+ rank 0 | gpu 0 | alloc=5.32 GB | reserved=24.88 GB | max=38.24 GB
550
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
551
+ return func(*args, **kwargs)
552
+ saving model.
553
+ eval loss 1.0395403653383255
554
+ Test accuracy: 0.16: 100%|██████████| 8/8 [00:34<00:00, 4.27s/it]
555
+
556
+ [WRONG] Example 0 (diff=3)
557
+ Expected : '300'
558
+ Extracted: '200'
559
+ Generated: '### 200<|endoftext|><|endoftext|>'
560
+
561
+ [WRONG] Example 1 (diff=3)
562
+ Expected : '25'
563
+ Extracted: '24'
564
+ Generated: '### 24<|endoftext|><|endoftext|><|endoftext|>'
565
+
566
+ [CORRECT] Example 2 (diff=2)
567
+ Expected : '45'
568
+ Extracted: '45'
569
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
570
+
571
+ [CORRECT] Example 3 (diff=2)
572
+ Expected : '12000'
573
+ Extracted: '12000'
574
+ Generated: '### 12000'
575
+
576
+ [WRONG] Example 4 (diff=2)
577
+ Expected : '55'
578
+ Extracted: '30'
579
+ Generated: '### 30<|endoftext|><|endoftext|><|endoftext|>'
580
+ Device 0: Cor=10, CoT=0, Total=62
581
+
582
+ ============================================================
583
+ EVALUATION RESULTS (Epoch 26)
584
+ ============================================================
585
+ Config: data_curriculum=ON, thought_curriculum=ON
586
+ thought_stage=5, data_stage=5, n_latent_tokens=10
587
+ Training on difficulties: 0-5
588
+
589
+ Overall accuracy: 149 / 496 = 0.3004
590
+
591
+ Per-difficulty accuracy:
592
+ diff=0: 11/26 = 0.4231 (trained)
593
+ diff=1: 81/155 = 0.5226 (trained)
594
+ diff=2: 36/141 = 0.2553 (trained)
595
+ diff=3: 16/92 = 0.1739 (trained)
596
+ diff=4: 5/53 = 0.0943 (trained)
597
+ diff=5: 0/18 = 0.0000 (trained)
598
+ diff=6: 0/8 = 0.0000 (untrained)
599
+ diff=7: 0/3 = 0.0000 (untrained)
600
+
601
+ Trained (diff 0-5): 149/485 = 0.3072
602
+ ============================================================
603
+
604
+
605
+ ============================================================
606
+ EPOCH 27/50 (thought_stage=5, data_stage=5)
607
+ ============================================================
608
+ n_latent_tokens=10, max_difficulty=5
609
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2776.27 examples/s]
610
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 60791.08 examples/s]
611
+ Map (num_proc=4): 100%|██████████| 382416/382416 [00:23<00:00, 16459.89 examples/s]
612
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1950.68 examples/s]
613
+ Map (num_proc=4): 100%|██████████| 483/483 [00:00<00:00, 1484.05 examples/s]
614
+ Training Epoch: 28/50, batch 2987/2988 completed (loss: 0.1364: 100%|██████████| 2988/2988 [1:58:28<00:00, 2.38s/it]
615
+ logging training data
616
+ rank 0 | gpu 0 | alloc=5.22 GB | reserved=22.49 GB | max=38.24 GB
617
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
618
+ return func(*args, **kwargs)
619
+ saving model.
620
+ eval loss 1.0927677154541016
621
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.28s/it]
622
+
623
+ [WRONG] Example 0 (diff=3)
624
+ Expected : '300'
625
+ Extracted: '100'
626
+ Generated: '### 100<|endoftext|><|endoftext|>'
627
+
628
+ [WRONG] Example 1 (diff=3)
629
+ Expected : '25'
630
+ Extracted: '18'
631
+ Generated: '### 18<|endoftext|><|endoftext|><|endoftext|>'
632
+
633
+ [CORRECT] Example 2 (diff=2)
634
+ Expected : '45'
635
+ Extracted: '45'
636
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
637
+
638
+ [CORRECT] Example 3 (diff=2)
639
+ Expected : '12000'
640
+ Extracted: '12000'
641
+ Generated: '### 12000'
642
+
643
+ [WRONG] Example 4 (diff=2)
644
+ Expected : '55'
645
+ Extracted: '75'
646
+ Generated: '### 75<|endoftext|><|endoftext|><|endoftext|>'
647
+ Device 0: Cor=12, CoT=0, Total=62
648
+
649
+ ============================================================
650
+ EVALUATION RESULTS (Epoch 27)
651
+ ============================================================
652
+ Config: data_curriculum=ON, thought_curriculum=ON
653
+ thought_stage=5, data_stage=5, n_latent_tokens=10
654
+ Training on difficulties: 0-5
655
+
656
+ Overall accuracy: 164 / 496 = 0.3306
657
+
658
+ Per-difficulty accuracy:
659
+ diff=0: 12/26 = 0.4615 (trained)
660
+ diff=1: 87/155 = 0.5613 (trained)
661
+ diff=2: 47/141 = 0.3333 (trained)
662
+ diff=3: 13/92 = 0.1413 (trained)
663
+ diff=4: 2/53 = 0.0377 (trained)
664
+ diff=5: 3/18 = 0.1667 (trained)
665
+ diff=6: 0/8 = 0.0000 (untrained)
666
+ diff=7: 0/3 = 0.0000 (untrained)
667
+
668
+ Trained (diff 0-5): 164/485 = 0.3381
669
+ ============================================================
670
+
671
+
672
+ ============================================================
673
+ EPOCH 28/50 (thought_stage=5, data_stage=5)
674
+ ============================================================
675
+ n_latent_tokens=10, max_difficulty=5
676
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2768.94 examples/s]
677
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61235.26 examples/s]
678
+ Map (num_proc=4): 100%|██████████| 382416/382416 [00:23<00:00, 16487.79 examples/s]
679
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1986.61 examples/s]
680
+ Map (num_proc=4): 100%|██████████| 483/483 [00:00<00:00, 1517.88 examples/s]
681
+ Training Epoch: 29/50, batch 2987/2988 completed (loss: 0.0022: 100%|██████████| 2988/2988 [1:59:13<00:00, 2.39s/it]
682
+ logging training data
683
+ rank 0 | gpu 0 | alloc=5.96 GB | reserved=39.70 GB | max=38.24 GB
684
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
685
+ return func(*args, **kwargs)
686
+ saving model.
687
+ eval loss 1.1008103489875793
688
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.27s/it]
689
+
690
+ [WRONG] Example 0 (diff=3)
691
+ Expected : '300'
692
+ Extracted: '100'
693
+ Generated: '### 100<|endoftext|><|endoftext|>'
694
+
695
+ [WRONG] Example 1 (diff=3)
696
+ Expected : '25'
697
+ Extracted: '18'
698
+ Generated: '### 18<|endoftext|><|endoftext|><|endoftext|>'
699
+
700
+ [CORRECT] Example 2 (diff=2)
701
+ Expected : '45'
702
+ Extracted: '45'
703
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
704
+
705
+ [WRONG] Example 3 (diff=2)
706
+ Expected : '12000'
707
+ Extracted: '14000'
708
+ Generated: '### 14000'
709
+
710
+ [WRONG] Example 4 (diff=2)
711
+ Expected : '55'
712
+ Extracted: '140'
713
+ Generated: '### 140<|endoftext|><|endoftext|>'
714
+ Device 0: Cor=12, CoT=0, Total=62
715
+
716
+ ============================================================
717
+ EVALUATION RESULTS (Epoch 28)
718
+ ============================================================
719
+ Config: data_curriculum=ON, thought_curriculum=ON
720
+ thought_stage=5, data_stage=5, n_latent_tokens=10
721
+ Training on difficulties: 0-5
722
+
723
+ Overall accuracy: 157 / 496 = 0.3165
724
+
725
+ Per-difficulty accuracy:
726
+ diff=0: 9/26 = 0.3462 (trained)
727
+ diff=1: 86/155 = 0.5548 (trained)
728
+ diff=2: 41/141 = 0.2908 (trained)
729
+ diff=3: 13/92 = 0.1413 (trained)
730
+ diff=4: 5/53 = 0.0943 (trained)
731
+ diff=5: 2/18 = 0.1111 (trained)
732
+ diff=6: 1/8 = 0.1250 (untrained)
733
+ diff=7: 0/3 = 0.0000 (untrained)
734
+
735
+ Trained (diff 0-5): 156/485 = 0.3216
736
+ ============================================================
737
+
738
+
739
+ ============================================================
740
+ EPOCH 29/50 (thought_stage=5, data_stage=5)
741
+ ============================================================
742
+ n_latent_tokens=10, max_difficulty=5
743
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2820.21 examples/s]
744
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 60627.46 examples/s]
745
+ Map (num_proc=4): 100%|██���███████| 382416/382416 [00:23<00:00, 16525.13 examples/s]
746
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2017.61 examples/s]
747
+ Map (num_proc=4): 100%|██████████| 483/483 [00:00<00:00, 1520.35 examples/s]
748
+ Training Epoch: 30/50, batch 2987/2988 completed (loss: 0.0079: 100%|██████████| 2988/2988 [1:58:47<00:00, 2.39s/it]
749
+ logging training data
750
+ rank 0 | gpu 0 | alloc=5.51 GB | reserved=29.43 GB | max=38.24 GB
751
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
752
+ return func(*args, **kwargs)
753
+ saving model.
754
+ eval loss 1.0997992157936096
755
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.28s/it]
756
+
757
+ [WRONG] Example 0 (diff=3)
758
+ Expected : '300'
759
+ Extracted: '100'
760
+ Generated: '### 100<|endoftext|>'
761
+
762
+ [WRONG] Example 1 (diff=3)
763
+ Expected : '25'
764
+ Extracted: '24.5'
765
+ Generated: '### 24.5'
766
+
767
+ [CORRECT] Example 2 (diff=2)
768
+ Expected : '45'
769
+ Extracted: '45'
770
+ Generated: '### 45<|endoftext|><|endoftext|>'
771
+
772
+ [WRONG] Example 3 (diff=2)
773
+ Expected : '12000'
774
+ Extracted: '6000'
775
+ Generated: '### 6000'
776
+
777
+ [WRONG] Example 4 (diff=2)
778
+ Expected : '55'
779
+ Extracted: '5'
780
+ Generated: '### 5<|endoftext|><|endoftext|><|endoftext|>'
781
+ Device 0: Cor=12, CoT=0, Total=62
782
+
783
+ ============================================================
784
+ EVALUATION RESULTS (Epoch 29)
785
+ ============================================================
786
+ Config: data_curriculum=ON, thought_curriculum=ON
787
+ thought_stage=5, data_stage=5, n_latent_tokens=10
788
+ Training on difficulties: 0-5
789
+
790
+ Overall accuracy: 151 / 496 = 0.3044
791
+
792
+ Per-difficulty accuracy:
793
+ diff=0: 10/26 = 0.3846 (trained)
794
+ diff=1: 83/155 = 0.5355 (trained)
795
+ diff=2: 37/141 = 0.2624 (trained)
796
+ diff=3: 12/92 = 0.1304 (trained)
797
+ diff=4: 8/53 = 0.1509 (trained)
798
+ diff=5: 1/18 = 0.0556 (trained)
799
+ diff=6: 0/8 = 0.0000 (untrained)
800
+ diff=7: 0/3 = 0.0000 (untrained)
801
+
802
+ Trained (diff 0-5): 151/485 = 0.3113
803
+ ============================================================
804
+
805
+
806
+ ============================================================
807
+ EPOCH 30/50 (thought_stage=6, data_stage=6)
808
+ ============================================================
809
+ n_latent_tokens=12, max_difficulty=6
810
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2786.12 examples/s]
811
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 62982.13 examples/s]
812
+ Map (num_proc=4): 100%|██████████| 384775/384775 [00:23<00:00, 16589.15 examples/s]
813
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1930.06 examples/s]
814
+ Map (num_proc=4): 100%|██████████| 491/491 [00:00<00:00, 1582.40 examples/s]
815
+ Training Epoch: 31/50, batch 3006/3007 completed (loss: 0.002: 100%|██████████| 3007/3007 [2:23:56<00:00, 2.87s/it]
816
+ logging training data
817
+ rank 0 | gpu 0 | alloc=5.52 GB | reserved=32.06 GB | max=38.24 GB
818
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
819
+ return func(*args, **kwargs)
820
+ saving model.
821
+ eval loss 1.0377523005008698
822
+ Test accuracy: 0.26: 100%|██████████| 8/8 [00:34<00:00, 4.30s/it]
823
+
824
+ [WRONG] Example 0 (diff=3)
825
+ Expected : '300'
826
+ Extracted: '333.33'
827
+ Generated: '### 333.33'
828
+
829
+ [WRONG] Example 1 (diff=3)
830
+ Expected : '25'
831
+ Extracted: '21'
832
+ Generated: '### 21<|endoftext|><|endoftext|><|endoftext|><|endoftext|>'
833
+
834
+ [CORRECT] Example 2 (diff=2)
835
+ Expected : '45'
836
+ Extracted: '45'
837
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|><|endoftext|>'
838
+
839
+ [CORRECT] Example 3 (diff=2)
840
+ Expected : '12000'
841
+ Extracted: '12000'
842
+ Generated: '### 12000<|endoftext|>'
843
+
844
+ [WRONG] Example 4 (diff=2)
845
+ Expected : '55'
846
+ Extracted: '35'
847
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|><|endoftext|>'
848
+ Device 0: Cor=16, CoT=0, Total=62
849
+
850
+ ============================================================
851
+ EVALUATION RESULTS (Epoch 30)
852
+ ============================================================
853
+ Config: data_curriculum=ON, thought_curriculum=ON
854
+ thought_stage=6, data_stage=6, n_latent_tokens=12
855
+ Training on difficulties: 0-6
856
+
857
+ Overall accuracy: 162 / 496 = 0.3266
858
+
859
+ Per-difficulty accuracy:
860
+ diff=0: 11/26 = 0.4231 (trained)
861
+ diff=1: 89/155 = 0.5742 (trained)
862
+ diff=2: 42/141 = 0.2979 (trained)
863
+ diff=3: 15/92 = 0.1630 (trained)
864
+ diff=4: 3/53 = 0.0566 (trained)
865
+ diff=5: 2/18 = 0.1111 (trained)
866
+ diff=6: 0/8 = 0.0000 (trained)
867
+ diff=7: 0/3 = 0.0000 (untrained)
868
+
869
+ Trained (diff 0-6): 162/493 = 0.3286
870
+ ============================================================
871
+
872
+
873
+ ============================================================
874
+ EPOCH 31/50 (thought_stage=6, data_stage=6)
875
+ ============================================================
876
+ n_latent_tokens=12, max_difficulty=6
877
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2737.30 examples/s]
878
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:07<00:00, 54836.05 examples/s]
879
+ Map (num_proc=4): 100%|██████████| 384775/384775 [00:24<00:00, 15867.74 examples/s]
880
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1667.90 examples/s]
881
+ Map (num_proc=4): 100%|██████████| 491/491 [00:00<00:00, 1486.91 examples/s]
882
+ Training Epoch: 32/50, batch 3006/3007 completed (loss: 0.0001: 100%|██████████| 3007/3007 [2:22:58<00:00, 2.85s/it]
883
+ logging training data
884
+ rank 0 | gpu 0 | alloc=5.38 GB | reserved=27.60 GB | max=38.24 GB
885
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
886
+ return func(*args, **kwargs)
887
+ saving model.
888
+ eval loss 1.0501955896615982
889
+ Test accuracy: 0.16: 100%|██████████| 8/8 [00:34<00:00, 4.30s/it]
890
+
891
+ [WRONG] Example 0 (diff=3)
892
+ Expected : '300'
893
+ Extracted: '100'
894
+ Generated: '### 100<|endoftext|>'
895
+
896
+ [WRONG] Example 1 (diff=3)
897
+ Expected : '25'
898
+ Extracted: '23'
899
+ Generated: '### 23<|endoftext|><|endoftext|>'
900
+
901
+ [CORRECT] Example 2 (diff=2)
902
+ Expected : '45'
903
+ Extracted: '45'
904
+ Generated: '### 45<|endoftext|><|endoftext|>'
905
+
906
+ [WRONG] Example 3 (diff=2)
907
+ Expected : '12000'
908
+ Extracted: '6000'
909
+ Generated: '### 6000'
910
+
911
+ [WRONG] Example 4 (diff=2)
912
+ Expected : '55'
913
+ Extracted: '15'
914
+ Generated: '### 15<|endoftext|><|endoftext|>'
915
+ Device 0: Cor=10, CoT=0, Total=62
916
+
917
+ ============================================================
918
+ EVALUATION RESULTS (Epoch 31)
919
+ ============================================================
920
+ Config: data_curriculum=ON, thought_curriculum=ON
921
+ thought_stage=6, data_stage=6, n_latent_tokens=12
922
+ Training on difficulties: 0-6
923
+
924
+ Overall accuracy: 146 / 496 = 0.2944
925
+
926
+ Per-difficulty accuracy:
927
+ diff=0: 11/26 = 0.4231 (trained)
928
+ diff=1: 86/155 = 0.5548 (trained)
929
+ diff=2: 30/141 = 0.2128 (trained)
930
+ diff=3: 11/92 = 0.1196 (trained)
931
+ diff=4: 6/53 = 0.1132 (trained)
932
+ diff=5: 1/18 = 0.0556 (trained)
933
+ diff=6: 1/8 = 0.1250 (trained)
934
+ diff=7: 0/3 = 0.0000 (untrained)
935
+
936
+ Trained (diff 0-6): 146/493 = 0.2961
937
+ ============================================================
938
+
939
+
940
+ ============================================================
941
+ EPOCH 32/50 (thought_stage=6, data_stage=6)
942
+ ============================================================
943
+ n_latent_tokens=12, max_difficulty=6
944
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2697.68 examples/s]
945
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 60370.07 examples/s]
946
+ Map (num_proc=4): 100%|██████████| 384775/384775 [00:23<00:00, 16436.44 examples/s]
947
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1883.12 examples/s]
948
+ Map (num_proc=4): 100%|██████████| 491/491 [00:00<00:00, 1425.83 examples/s]
949
+ Training Epoch: 33/50, batch 3006/3007 completed (loss: 0.0046: 100%|██████████| 3007/3007 [2:24:05<00:00, 2.88s/it]
950
+ logging training data
951
+ rank 0 | gpu 0 | alloc=5.31 GB | reserved=26.34 GB | max=44.97 GB
952
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
953
+ return func(*args, **kwargs)
954
+ saving model.
955
+ eval loss 1.0964151918888092
956
+ Test accuracy: 0.21: 100%|██████████| 8/8 [00:34<00:00, 4.30s/it]
957
+
958
+ [WRONG] Example 0 (diff=3)
959
+ Expected : '300'
960
+ Extracted: '100'
961
+ Generated: '### 100<|endoftext|><|endoftext|>'
962
+
963
+ [WRONG] Example 1 (diff=3)
964
+ Expected : '25'
965
+ Extracted: '23'
966
+ Generated: '### 23<|endoftext|><|endoftext|><|endoftext|>'
967
+
968
+ [CORRECT] Example 2 (diff=2)
969
+ Expected : '45'
970
+ Extracted: '45'
971
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
972
+
973
+ [WRONG] Example 3 (diff=2)
974
+ Expected : '12000'
975
+ Extracted: '14000'
976
+ Generated: '### 14000'
977
+
978
+ [WRONG] Example 4 (diff=2)
979
+ Expected : '55'
980
+ Extracted: '30'
981
+ Generated: '### 30<|endoftext|><|endoftext|><|endoftext|>'
982
+ Device 0: Cor=13, CoT=0, Total=62
983
+
984
+ ============================================================
985
+ EVALUATION RESULTS (Epoch 32)
986
+ ============================================================
987
+ Config: data_curriculum=ON, thought_curriculum=ON
988
+ thought_stage=6, data_stage=6, n_latent_tokens=12
989
+ Training on difficulties: 0-6
990
+
991
+ Overall accuracy: 155 / 496 = 0.3125
992
+
993
+ Per-difficulty accuracy:
994
+ diff=0: 11/26 = 0.4231 (trained)
995
+ diff=1: 83/155 = 0.5355 (trained)
996
+ diff=2: 43/141 = 0.3050 (trained)
997
+ diff=3: 12/92 = 0.1304 (trained)
998
+ diff=4: 5/53 = 0.0943 (trained)
999
+ diff=5: 0/18 = 0.0000 (trained)
1000
+ diff=6: 1/8 = 0.1250 (trained)
1001
+ diff=7: 0/3 = 0.0000 (untrained)
1002
+
1003
+ Trained (diff 0-6): 155/493 = 0.3144
1004
+ ============================================================
1005
+
1006
+
1007
+ ============================================================
1008
+ EPOCH 33/50 (thought_stage=6, data_stage=6)
1009
+ ============================================================
1010
+ n_latent_tokens=12, max_difficulty=6
1011
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2724.15 examples/s]
1012
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 57154.04 examples/s]
1013
+ Map (num_proc=4): 100%|██████████| 384775/384775 [00:23<00:00, 16147.32 examples/s]
1014
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1854.52 examples/s]
1015
+ Map (num_proc=4): 100%|██████████| 491/491 [00:00<00:00, 1587.53 examples/s]
1016
+ Training Epoch: 34/50, batch 3006/3007 completed (loss: 0.0001: 100%|██████████| 3007/3007 [2:23:57<00:00, 2.87s/it]
1017
+ logging training data
1018
+ rank 0 | gpu 0 | alloc=5.33 GB | reserved=26.50 GB | max=44.97 GB
1019
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1020
+ return func(*args, **kwargs)
1021
+ saving model.
1022
+ eval loss 1.1013361811637878
1023
+ Test accuracy: 0.19: 100%|██████████| 8/8 [00:34<00:00, 4.36s/it]
1024
+
1025
+ [WRONG] Example 0 (diff=3)
1026
+ Expected : '300'
1027
+ Extracted: '100'
1028
+ Generated: '### 100<|endoftext|>'
1029
+
1030
+ [WRONG] Example 1 (diff=3)
1031
+ Expected : '25'
1032
+ Extracted: '23.5'
1033
+ Generated: '### 23.5'
1034
+
1035
+ [CORRECT] Example 2 (diff=2)
1036
+ Expected : '45'
1037
+ Extracted: '45'
1038
+ Generated: '### 45<|endoftext|><|endoftext|>'
1039
+
1040
+ [WRONG] Example 3 (diff=2)
1041
+ Expected : '12000'
1042
+ Extracted: '8000'
1043
+ Generated: '### 8000'
1044
+
1045
+ [WRONG] Example 4 (diff=2)
1046
+ Expected : '55'
1047
+ Extracted: '115'
1048
+ Generated: '### 115<|endoftext|>'
1049
+ Device 0: Cor=12, CoT=0, Total=62
1050
+
1051
+ ============================================================
1052
+ EVALUATION RESULTS (Epoch 33)
1053
+ ============================================================
1054
+ Config: data_curriculum=ON, thought_curriculum=ON
1055
+ thought_stage=6, data_stage=6, n_latent_tokens=12
1056
+ Training on difficulties: 0-6
1057
+
1058
+ Overall accuracy: 151 / 496 = 0.3044
1059
+
1060
+ Per-difficulty accuracy:
1061
+ diff=0: 9/26 = 0.3462 (trained)
1062
+ diff=1: 83/155 = 0.5355 (trained)
1063
+ diff=2: 40/141 = 0.2837 (trained)
1064
+ diff=3: 14/92 = 0.1522 (trained)
1065
+ diff=4: 4/53 = 0.0755 (trained)
1066
+ diff=5: 1/18 = 0.0556 (trained)
1067
+ diff=6: 0/8 = 0.0000 (trained)
1068
+ diff=7: 0/3 = 0.0000 (untrained)
1069
+
1070
+ Trained (diff 0-6): 151/493 = 0.3063
1071
+ ============================================================
1072
+
1073
+
1074
+ ============================================================
1075
+ EPOCH 34/50 (thought_stage=6, data_stage=6)
1076
+ ============================================================
1077
+ n_latent_tokens=12, max_difficulty=6
1078
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2808.01 examples/s]
1079
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 57443.85 examples/s]
1080
+ Map (num_proc=4): 100%|██████████| 384775/384775 [00:23<00:00, 16190.56 examples/s]
1081
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1924.32 examples/s]
1082
+ Map (num_proc=4): 100%|██████████| 491/491 [00:00<00:00, 1504.08 examples/s]
1083
+ Training Epoch: 35/50, batch 3006/3007 completed (loss: 0.0015: 100%|██████████| 3007/3007 [2:24:32<00:00, 2.88s/it]
1084
+ logging training data
1085
+ rank 0 | gpu 0 | alloc=5.67 GB | reserved=36.07 GB | max=44.97 GB
1086
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1087
+ return func(*args, **kwargs)
1088
+ saving model.
1089
+ eval loss 1.1001246571540833
1090
+ Test accuracy: 0.18: 100%|██████████| 8/8 [00:34<00:00, 4.31s/it]
1091
+
1092
+ [WRONG] Example 0 (diff=3)
1093
+ Expected : '300'
1094
+ Extracted: '100'
1095
+ Generated: '### 100<|endoftext|><|endoftext|>'
1096
+
1097
+ [WRONG] Example 1 (diff=3)
1098
+ Expected : '25'
1099
+ Extracted: '21.5'
1100
+ Generated: '### 21.5<|endoftext|>'
1101
+
1102
+ [CORRECT] Example 2 (diff=2)
1103
+ Expected : '45'
1104
+ Extracted: '45'
1105
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
1106
+
1107
+ [WRONG] Example 3 (diff=2)
1108
+ Expected : '12000'
1109
+ Extracted: '6000'
1110
+ Generated: '### 6000<|endoftext|>'
1111
+
1112
+ [WRONG] Example 4 (diff=2)
1113
+ Expected : '55'
1114
+ Extracted: '60'
1115
+ Generated: '### 60<|endoftext|><|endoftext|><|endoftext|>'
1116
+ Device 0: Cor=11, CoT=0, Total=62
1117
+
1118
+ ============================================================
1119
+ EVALUATION RESULTS (Epoch 34)
1120
+ ============================================================
1121
+ Config: data_curriculum=ON, thought_curriculum=ON
1122
+ thought_stage=6, data_stage=6, n_latent_tokens=12
1123
+ Training on difficulties: 0-6
1124
+
1125
+ Overall accuracy: 155 / 496 = 0.3125
1126
+
1127
+ Per-difficulty accuracy:
1128
+ diff=0: 13/26 = 0.5000 (trained)
1129
+ diff=1: 83/155 = 0.5355 (trained)
1130
+ diff=2: 36/141 = 0.2553 (trained)
1131
+ diff=3: 16/92 = 0.1739 (trained)
1132
+ diff=4: 5/53 = 0.0943 (trained)
1133
+ diff=5: 2/18 = 0.1111 (trained)
1134
+ diff=6: 0/8 = 0.0000 (trained)
1135
+ diff=7: 0/3 = 0.0000 (untrained)
1136
+
1137
+ Trained (diff 0-6): 155/493 = 0.3144
1138
+ ============================================================
1139
+
1140
+
1141
+ ============================================================
1142
+ EPOCH 35/50 (thought_stage=7, data_stage=7)
1143
+ ============================================================
1144
+ n_latent_tokens=14, max_difficulty=7
1145
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2795.56 examples/s]
1146
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 55137.08 examples/s]
1147
+ Map (num_proc=4): 100%|██████████| 385352/385352 [00:24<00:00, 15857.47 examples/s]
1148
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1833.42 examples/s]
1149
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1605.56 examples/s]
1150
+ Training Epoch: 36/50, batch 3010/3011 completed (loss: 0.0031: 100%|██████████| 3011/3011 [2:48:25<00:00, 3.36s/it]
1151
+ logging training data
1152
+ rank 0 | gpu 0 | alloc=5.32 GB | reserved=27.52 GB | max=44.97 GB
1153
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1154
+ return func(*args, **kwargs)
1155
+ saving model.
1156
+ eval loss 1.12626314163208
1157
+ Test accuracy: 0.23: 100%|██████████| 8/8 [00:34<00:00, 4.32s/it]
1158
+
1159
+ [WRONG] Example 0 (diff=3)
1160
+ Expected : '300'
1161
+ Extracted: '200'
1162
+ Generated: '### 200<|endoftext|><|endoftext|>'
1163
+
1164
+ [WRONG] Example 1 (diff=3)
1165
+ Expected : '25'
1166
+ Extracted: '18'
1167
+ Generated: '### 18<|endoftext|><|endoftext|><|endoftext|>'
1168
+
1169
+ [CORRECT] Example 2 (diff=2)
1170
+ Expected : '45'
1171
+ Extracted: '45'
1172
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
1173
+
1174
+ [CORRECT] Example 3 (diff=2)
1175
+ Expected : '12000'
1176
+ Extracted: '12000'
1177
+ Generated: '### 12000'
1178
+
1179
+ [WRONG] Example 4 (diff=2)
1180
+ Expected : '55'
1181
+ Extracted: '40'
1182
+ Generated: '### 40<|endoftext|><|endoftext|><|endoftext|>'
1183
+ Device 0: Cor=14, CoT=0, Total=62
1184
+
1185
+ ============================================================
1186
+ EVALUATION RESULTS (Epoch 35)
1187
+ ============================================================
1188
+ Config: data_curriculum=ON, thought_curriculum=ON
1189
+ thought_stage=7, data_stage=7, n_latent_tokens=14
1190
+ Training on difficulties: 0-7
1191
+
1192
+ Overall accuracy: 153 / 496 = 0.3085
1193
+
1194
+ Per-difficulty accuracy:
1195
+ diff=0: 12/26 = 0.4615 (trained)
1196
+ diff=1: 83/155 = 0.5355 (trained)
1197
+ diff=2: 34/141 = 0.2411 (trained)
1198
+ diff=3: 16/92 = 0.1739 (trained)
1199
+ diff=4: 6/53 = 0.1132 (trained)
1200
+ diff=5: 1/18 = 0.0556 (trained)
1201
+ diff=6: 1/8 = 0.1250 (trained)
1202
+ diff=7: 0/3 = 0.0000 (trained)
1203
+
1204
+ Trained (diff 0-7): 153/496 = 0.3085
1205
+ ============================================================
1206
+
1207
+
1208
+ ============================================================
1209
+ EPOCH 36/50 (thought_stage=7, data_stage=7)
1210
+ ============================================================
1211
+ n_latent_tokens=14, max_difficulty=7
1212
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2850.38 examples/s]
1213
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 62039.41 examples/s]
1214
+ Map (num_proc=4): 100%|██████████| 385352/385352 [00:23<00:00, 16504.64 examples/s]
1215
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1885.78 examples/s]
1216
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1641.73 examples/s]
1217
+ Training Epoch: 37/50, batch 3010/3011 completed (loss: 0.0101: 100%|██████████| 3011/3011 [2:48:37<00:00, 3.36s/it]
1218
+ logging training data
1219
+ rank 0 | gpu 0 | alloc=5.44 GB | reserved=31.29 GB | max=44.97 GB
1220
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1221
+ return func(*args, **kwargs)
1222
+ saving model.
1223
+ eval loss 1.1475436836481094
1224
+ Test accuracy: 0.26: 100%|██████████| 8/8 [00:34<00:00, 4.29s/it]
1225
+
1226
+ [WRONG] Example 0 (diff=3)
1227
+ Expected : '300'
1228
+ Extracted: '100'
1229
+ Generated: '### 100<|endoftext|><|endoftext|>'
1230
+
1231
+ [WRONG] Example 1 (diff=3)
1232
+ Expected : '25'
1233
+ Extracted: '14.5'
1234
+ Generated: '### 14.5<|endoftext|>'
1235
+
1236
+ [CORRECT] Example 2 (diff=2)
1237
+ Expected : '45'
1238
+ Extracted: '45'
1239
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
1240
+
1241
+ [CORRECT] Example 3 (diff=2)
1242
+ Expected : '12000'
1243
+ Extracted: '12000'
1244
+ Generated: '### 12000'
1245
+
1246
+ [WRONG] Example 4 (diff=2)
1247
+ Expected : '55'
1248
+ Extracted: '60'
1249
+ Generated: '### 60<|endoftext|><|endoftext|><|endoftext|>'
1250
+ Device 0: Cor=16, CoT=0, Total=62
1251
+
1252
+ ============================================================
1253
+ EVALUATION RESULTS (Epoch 36)
1254
+ ============================================================
1255
+ Config: data_curriculum=ON, thought_curriculum=ON
1256
+ thought_stage=7, data_stage=7, n_latent_tokens=14
1257
+ Training on difficulties: 0-7
1258
+
1259
+ Overall accuracy: 167 / 496 = 0.3367
1260
+
1261
+ Per-difficulty accuracy:
1262
+ diff=0: 9/26 = 0.3462 (trained)
1263
+ diff=1: 89/155 = 0.5742 (trained)
1264
+ diff=2: 42/141 = 0.2979 (trained)
1265
+ diff=3: 18/92 = 0.1957 (trained)
1266
+ diff=4: 5/53 = 0.0943 (trained)
1267
+ diff=5: 3/18 = 0.1667 (trained)
1268
+ diff=6: 1/8 = 0.1250 (trained)
1269
+ diff=7: 0/3 = 0.0000 (trained)
1270
+
1271
+ Trained (diff 0-7): 167/496 = 0.3367
1272
+ ============================================================
1273
+
1274
+
1275
+ ============================================================
1276
+ EPOCH 37/50 (thought_stage=7, data_stage=7)
1277
+ ============================================================
1278
+ n_latent_tokens=14, max_difficulty=7
1279
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2720.13 examples/s]
1280
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 58716.32 examples/s]
1281
+ Map (num_proc=4): 100%|██████████| 385352/385352 [00:24<00:00, 15897.37 examples/s]
1282
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1938.91 examples/s]
1283
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1507.11 examples/s]
1284
+ Training Epoch: 38/50, batch 3010/3011 completed (loss: 0.0055: 100%|██████████| 3011/3011 [2:48:38<00:00, 3.36s/it]
1285
+ logging training data
1286
+ rank 0 | gpu 0 | alloc=5.43 GB | reserved=30.73 GB | max=48.43 GB
1287
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1288
+ return func(*args, **kwargs)
1289
+ saving model.
1290
+ eval loss 1.2535533905029297
1291
+ Test accuracy: 0.24: 100%|██████████| 8/8 [00:34<00:00, 4.27s/it]
1292
+
1293
+ [WRONG] Example 0 (diff=3)
1294
+ Expected : '300'
1295
+ Extracted: '100'
1296
+ Generated: '### 100<|endoftext|><|endoftext|>'
1297
+
1298
+ [WRONG] Example 1 (diff=3)
1299
+ Expected : '25'
1300
+ Extracted: '21'
1301
+ Generated: '### 21<|endoftext|><|endoftext|><|endoftext|>'
1302
+
1303
+ [CORRECT] Example 2 (diff=2)
1304
+ Expected : '45'
1305
+ Extracted: '45'
1306
+ Generated: '### 45<|endoftext|><|endoftext|><|endoftext|>'
1307
+
1308
+ [CORRECT] Example 3 (diff=2)
1309
+ Expected : '12000'
1310
+ Extracted: '12000'
1311
+ Generated: '### 12000'
1312
+
1313
+ [WRONG] Example 4 (diff=2)
1314
+ Expected : '55'
1315
+ Extracted: '35'
1316
+ Generated: '### 35<|endoftext|><|endoftext|><|endoftext|>'
1317
+ Device 0: Cor=15, CoT=0, Total=62
1318
+
1319
+ ============================================================
1320
+ EVALUATION RESULTS (Epoch 37)
1321
+ ============================================================
1322
+ Config: data_curriculum=ON, thought_curriculum=ON
1323
+ thought_stage=7, data_stage=7, n_latent_tokens=14
1324
+ Training on difficulties: 0-7
1325
+
1326
+ Overall accuracy: 141 / 496 = 0.2843
1327
+
1328
+ Per-difficulty accuracy:
1329
+ diff=0: 9/26 = 0.3462 (trained)
1330
+ diff=1: 76/155 = 0.4903 (trained)
1331
+ diff=2: 40/141 = 0.2837 (trained)
1332
+ diff=3: 9/92 = 0.0978 (trained)
1333
+ diff=4: 7/53 = 0.1321 (trained)
1334
+ diff=5: 0/18 = 0.0000 (trained)
1335
+ diff=6: 0/8 = 0.0000 (trained)
1336
+ diff=7: 0/3 = 0.0000 (trained)
1337
+
1338
+ Trained (diff 0-7): 141/496 = 0.2843
1339
+ ============================================================
1340
+
1341
+
1342
+ ============================================================
1343
+ EPOCH 38/50 (thought_stage=7, data_stage=7)
1344
+ ============================================================
1345
+ n_latent_tokens=14, max_difficulty=7
1346
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2729.76 examples/s]
1347
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 58280.73 examples/s]
1348
+ Map (num_proc=4): 100%|██████████| 385352/385352 [00:23<00:00, 16062.14 examples/s]
1349
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1960.84 examples/s]
1350
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1515.96 examples/s]
1351
+ Training Epoch: 39/50, batch 3010/3011 completed (loss: 0.0005: 100%|██████████| 3011/3011 [2:48:46<00:00, 3.36s/it]
1352
+ logging training data
1353
+ rank 0 | gpu 0 | alloc=5.40 GB | reserved=30.27 GB | max=48.43 GB
1354
+ /home/ubuntu/thoughtformer/.venv/lib/python3.12/site-packages/torch/distributed/c10d_logger.py:83: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning.
1355
+ return func(*args, **kwargs)
1356
+ saving model.
1357
+ eval loss 1.2187020778656006
1358
+ Test accuracy: 0.13: 100%|█████████���| 8/8 [00:34<00:00, 4.30s/it]
1359
+
1360
+ [WRONG] Example 0 (diff=3)
1361
+ Expected : '300'
1362
+ Extracted: '200'
1363
+ Generated: '### 200<|endoftext|>'
1364
+
1365
+ [WRONG] Example 1 (diff=3)
1366
+ Expected : '25'
1367
+ Extracted: '21'
1368
+ Generated: '### 21<|endoftext|><|endoftext|>'
1369
+
1370
+ [CORRECT] Example 2 (diff=2)
1371
+ Expected : '45'
1372
+ Extracted: '45'
1373
+ Generated: '### 45<|endoftext|><|endoftext|>'
1374
+
1375
+ [WRONG] Example 3 (diff=2)
1376
+ Expected : '12000'
1377
+ Extracted: '6000'
1378
+ Generated: '### 6000'
1379
+
1380
+ [WRONG] Example 4 (diff=2)
1381
+ Expected : '55'
1382
+ Extracted: '40'
1383
+ Generated: '### 40<|endoftext|><|endoftext|>'
1384
+ Device 0: Cor=8, CoT=0, Total=62
1385
+
1386
+ ============================================================
1387
+ EVALUATION RESULTS (Epoch 38)
1388
+ ============================================================
1389
+ Config: data_curriculum=ON, thought_curriculum=ON
1390
+ thought_stage=7, data_stage=7, n_latent_tokens=14
1391
+ Training on difficulties: 0-7
1392
+
1393
+ Overall accuracy: 149 / 496 = 0.3004
1394
+
1395
+ Per-difficulty accuracy:
1396
+ diff=0: 13/26 = 0.5000 (trained)
1397
+ diff=1: 82/155 = 0.5290 (trained)
1398
+ diff=2: 32/141 = 0.2270 (trained)
1399
+ diff=3: 14/92 = 0.1522 (trained)
1400
+ diff=4: 6/53 = 0.1132 (trained)
1401
+ diff=5: 2/18 = 0.1111 (trained)
1402
+ diff=6: 0/8 = 0.0000 (trained)
1403
+ diff=7: 0/3 = 0.0000 (trained)
1404
+
1405
+ Trained (diff 0-7): 149/496 = 0.3004
1406
+ ============================================================
1407
+
1408
+
1409
+ ============================================================
1410
+ EPOCH 39/50 (thought_stage=7, data_stage=7)
1411
+ ============================================================
1412
+ n_latent_tokens=14, max_difficulty=7
1413
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 2639.71 examples/s]
1414
+ Filter (num_proc=4): 100%|██████████| 385352/385352 [00:06<00:00, 61387.18 examples/s]
1415
+ Map (num_proc=4): 100%|██████████| 385352/385352 [00:23<00:00, 16105.64 examples/s]
1416
+ Filter (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1910.34 examples/s]
1417
+ Map (num_proc=4): 100%|██████████| 494/494 [00:00<00:00, 1644.38 examples/s]
1418
+ Training Epoch: 40/50, batch 2796/3011 completed (loss: 0.0694: 93%|█████████▎| 2797/3011 [2:37:03<11:37, 3.26s/it]
1419
+ logging training data
1420
+ rank 0 | gpu 0 | alloc=5.36 GB | reserved=28.17 GB | max=48.43 GB
wandb/run-20260413_074459-5pydxgbu/files/requirements.txt ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ gitdb==4.0.12
2
+ nvidia-nvshmem-cu13==3.4.5
3
+ pydantic_core==2.41.5
4
+ pyarrow==23.0.1
5
+ typing_extensions==4.15.0
6
+ attrs==26.1.0
7
+ nvidia-cufft==12.0.0.61
8
+ huggingface_hub==1.10.1
9
+ aiosignal==1.4.0
10
+ markdown-it-py==4.0.0
11
+ dill==0.3.8
12
+ h11==0.16.0
13
+ nvidia-cusparse==12.6.3.3
14
+ packaging==26.0
15
+ safetensors==0.7.0
16
+ yarl==1.23.0
17
+ idna==3.11
18
+ smmap==5.0.3
19
+ python-dateutil==2.9.0.post0
20
+ mpmath==1.3.0
21
+ regex==2026.4.4
22
+ anyio==4.13.0
23
+ fsspec==2024.9.0
24
+ frozenlist==1.8.0
25
+ MarkupSafe==3.0.3
26
+ httpx==0.28.1
27
+ pandas==3.0.2
28
+ aiohappyeyeballs==2.6.1
29
+ aiohttp==3.13.5
30
+ pip==24.0
31
+ multiprocess==0.70.16
32
+ Jinja2==3.1.6
33
+ sentry-sdk==2.57.0
34
+ cuda-pathfinder==1.5.2
35
+ tokenizers==0.22.2
36
+ transformers==5.4.0
37
+ cuda-bindings==13.2.0
38
+ nvidia-nvtx==13.0.85
39
+ nvidia-cusparselt-cu13==0.8.0
40
+ multidict==6.7.1
41
+ nvidia-nccl-cu13==2.28.9
42
+ annotated-doc==0.0.4
43
+ triton==3.6.0
44
+ click==8.3.2
45
+ nvidia-nvjitlink==13.0.88
46
+ typer==0.24.1
47
+ filelock==3.25.2
48
+ nvidia-cudnn-cu13==9.19.0.56
49
+ charset-normalizer==3.4.7
50
+ nvidia-cuda-runtime==13.0.96
51
+ torch==2.11.0
52
+ nvidia-curand==10.4.0.35
53
+ certifi==2026.2.25
54
+ nvidia-cuda-nvrtc==13.0.88
55
+ httpcore==1.0.9
56
+ hf-xet==1.4.3
57
+ wandb==0.25.1
58
+ cuda-toolkit==13.0.2
59
+ six==1.17.0
60
+ rich==15.0.0
61
+ networkx==3.6.1
62
+ numpy==2.1.3
63
+ nvidia-cublas==13.1.0.3
64
+ PyYAML==6.0.3
65
+ shellingham==1.5.4
66
+ protobuf==6.33.6
67
+ typing-inspection==0.4.2
68
+ platformdirs==4.9.6
69
+ sympy==1.14.0
70
+ urllib3==2.6.3
71
+ GitPython==3.1.46
72
+ nvidia-cufile==1.15.1.6
73
+ setuptools==81.0.0
74
+ annotated-types==0.7.0
75
+ requests==2.33.1
76
+ python-dotenv==1.2.2
77
+ nvidia-cuda-cupti==13.0.85
78
+ tqdm==4.67.0
79
+ propcache==0.4.1
80
+ nvidia-cusolver==12.0.4.66
81
+ pydantic==2.12.5
82
+ xxhash==3.6.0
83
+ Pygments==2.20.0
84
+ datasets==3.1.0
85
+ mdurl==0.1.2
wandb/run-20260413_074459-5pydxgbu/files/wandb-metadata.json ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "os": "Linux-6.17.0-1009-aws-x86_64-with-glibc2.39",
3
+ "python": "CPython 3.12.3",
4
+ "startedAt": "2026-04-13T07:44:59.106992Z",
5
+ "args": [
6
+ "args/gsm_thoughtformer_resume.yaml"
7
+ ],
8
+ "program": "/home/ubuntu/thoughtformer/run.py",
9
+ "codePath": "run.py",
10
+ "codePathLocal": "run.py",
11
+ "git": {
12
+ "remote": "git@github.com:seyedparsa/thoughtformer.git",
13
+ "commit": "48c93425ad7c95c1e6c944297647e1ed4da32d79"
14
+ },
15
+ "email": "seyedparsamirtaheri@gmail.com",
16
+ "root": "/home/ubuntu/thoughtformer",
17
+ "host": "ip-172-31-3-36",
18
+ "executable": "/home/ubuntu/thoughtformer/.venv/bin/python3",
19
+ "cpu_count": 96,
20
+ "cpu_count_logical": 192,
21
+ "gpu": "NVIDIA H200",
22
+ "gpu_count": 8,
23
+ "disk": {
24
+ "/": {
25
+ "total": "265147105280",
26
+ "used": "24694632448"
27
+ }
28
+ },
29
+ "memory": {
30
+ "total": "2147422769152"
31
+ },
32
+ "gpu_nvidia": [
33
+ {
34
+ "name": "NVIDIA H200",
35
+ "memoryTotal": "150754820096",
36
+ "cudaCores": 16896,
37
+ "architecture": "Hopper",
38
+ "uuid": "GPU-0598853e-f265-e180-5304-73ee82dda2e5"
39
+ },
40
+ {
41
+ "name": "NVIDIA H200",
42
+ "memoryTotal": "150754820096",
43
+ "cudaCores": 16896,
44
+ "architecture": "Hopper",
45
+ "uuid": "GPU-9315075f-b748-4b55-ca7f-be7ec6da2aa3"
46
+ },
47
+ {
48
+ "name": "NVIDIA H200",
49
+ "memoryTotal": "150754820096",
50
+ "cudaCores": 16896,
51
+ "architecture": "Hopper",
52
+ "uuid": "GPU-21c73f8d-f9ff-e978-a373-7578afcd54b9"
53
+ },
54
+ {
55
+ "name": "NVIDIA H200",
56
+ "memoryTotal": "150754820096",
57
+ "cudaCores": 16896,
58
+ "architecture": "Hopper",
59
+ "uuid": "GPU-73ade037-2f20-7044-9fbc-5cec13a7eed0"
60
+ },
61
+ {
62
+ "name": "NVIDIA H200",
63
+ "memoryTotal": "150754820096",
64
+ "cudaCores": 16896,
65
+ "architecture": "Hopper",
66
+ "uuid": "GPU-13377733-222f-7c35-2d9d-ea908b40fb80"
67
+ },
68
+ {
69
+ "name": "NVIDIA H200",
70
+ "memoryTotal": "150754820096",
71
+ "cudaCores": 16896,
72
+ "architecture": "Hopper",
73
+ "uuid": "GPU-30407468-2341-aee9-29f1-c933177c8c37"
74
+ },
75
+ {
76
+ "name": "NVIDIA H200",
77
+ "memoryTotal": "150754820096",
78
+ "cudaCores": 16896,
79
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