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=== full run start Mon Sep 14 16:44:32 UTC 2026 ===
[2026-09-14 16:44:37,556] [WARNING] [runner.py:232:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2026-09-14 16:44:37,556] [INFO] [runner.py:630:main] cmd = /opt/conda/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMV19 --master_addr=127.0.0.1 --master_port=29500 --enable_each_rank_log=None --log_level=info scripts/training/train.py --model_config configs/model/model_300m.yaml --tokenizer_path tokenizer --train_data_dir /workspace/data/pretrain/train --val_data_dir /workspace/data/pretrain/val_small --output_dir outputs/devops-300m-4096-bf16-vast --max_steps 4000 --warmup_steps 40 --learning_rate 6e-4 --batch_size 1 --grad_accum 16 --logging_steps 10 --save_steps 2000 --eval_steps 2000 --deepspeed_config configs/training/deepspeed_zero2_bf16.json --bf16 --seq_length 4096 --run_name devops-300m-4096-bf16-vast
[2026-09-14 16:44:42,440] [INFO] [launch.py:155:main] 0 NCCL_DEBUG=INFO
[2026-09-14 16:44:42,440] [INFO] [launch.py:162:main] WORLD INFO DICT: {'localhost': [0, 1]}
[2026-09-14 16:44:42,440] [INFO] [launch.py:168:main] nnodes=1, num_local_procs=2, node_rank=0
[2026-09-14 16:44:42,440] [INFO] [launch.py:179:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1]})
[2026-09-14 16:44:42,440] [INFO] [launch.py:180:main] dist_world_size=2
[2026-09-14 16:44:42,440] [INFO] [launch.py:184:main] Setting CUDA_VISIBLE_DEVICES=0,1
[2026-09-14 16:44:42,441] [INFO] [launch.py:272:main] process 10692 spawned with command: ['/opt/conda/bin/python', '-u', 'scripts/training/train.py', '--local_rank=0', '--model_config', 'configs/model/model_300m.yaml', '--tokenizer_path', 'tokenizer', '--train_data_dir', '/workspace/data/pretrain/train', '--val_data_dir', '/workspace/data/pretrain/val_small', '--output_dir', 'outputs/devops-300m-4096-bf16-vast', '--max_steps', '4000', '--warmup_steps', '40', '--learning_rate', '6e-4', '--batch_size', '1', '--grad_accum', '16', '--logging_steps', '10', '--save_steps', '2000', '--eval_steps', '2000', '--deepspeed_config', 'configs/training/deepspeed_zero2_bf16.json', '--bf16', '--seq_length', '4096', '--run_name', 'devops-300m-4096-bf16-vast']
[2026-09-14 16:44:42,441] [INFO] [launch.py:272:main] process 10693 spawned with command: ['/opt/conda/bin/python', '-u', 'scripts/training/train.py', '--local_rank=1', '--model_config', 'configs/model/model_300m.yaml', '--tokenizer_path', 'tokenizer', '--train_data_dir', '/workspace/data/pretrain/train', '--val_data_dir', '/workspace/data/pretrain/val_small', '--output_dir', 'outputs/devops-300m-4096-bf16-vast', '--max_steps', '4000', '--warmup_steps', '40', '--learning_rate', '6e-4', '--batch_size', '1', '--grad_accum', '16', '--logging_steps', '10', '--save_steps', '2000', '--eval_steps', '2000', '--deepspeed_config', 'configs/training/deepspeed_zero2_bf16.json', '--bf16', '--seq_length', '4096', '--run_name', 'devops-300m-4096-bf16-vast']
[Tokenizer] vocab_size=65536
[Tokenizer] vocab_size=65536
[Dataset] 1509 files, 132,068 non-overlapping samples, seq_len=4096
[Dataset] 10 files, 22 non-overlapping samples, seq_len=4096
[Dataset] 1509 files, 132,068 non-overlapping samples, seq_len=4096
[Dataset] 10 files, 22 non-overlapping samples, seq_len=4096
[transformers] LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
 - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
 - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
 - If you are not the owner of the model architecture class, please contact the model code owner to update it.
[transformers] LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
 - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
 - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
 - If you are not the owner of the model architecture class, please contact the model code owner to update it.
[Model] 287.3M parameters
[Env] 2 GPUs detected, using DeepSpeed (launch with: deepspeed --master_port 29500 train.py ...)
[Model] 287.3M parameters
[Env] 2 GPUs detected, using DeepSpeed (launch with: deepspeed --master_port 29500 train.py ...)
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[Train] Starting: 4000 steps, batch=1×16×world_size, lr=0.0006, seq_len=4096
[Train] Starting: 4000 steps, batch=1×16×world_size, lr=0.0006, seq_len=4096
[RANK 0] Gradient accumulation steps mismatch: GradientAccumulationPlugin has 1, DeepSpeed config has 16. Using DeepSpeed's value.
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81d666a3c511:10692:11110 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
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[2026-09-14 16:44:56,871] [WARNING] [lr_schedules.py:693:get_lr] Attempting to get learning rate from scheduler before it has started
[2026-09-14 16:44:57,091] [WARNING] [lr_schedules.py:693:get_lr] Attempting to get learning rate from scheduler before it has started
 0%| | 0/4000 [00:00<?, ?it/s][transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
 0%| | 1/4000 [00:05<5:36:20, 5.05s/it] 0%| | 2/4000 [00:09<5:01:22, 4.52s/it] 0%| | 3/4000 [00:13<4:49:48, 4.35s/it] 0%| | 4/4000 [00:17<4:43:51, 4.26s/it] 0%| | 5/4000 [00:21<4:40:02, 4.21s/it] 0%| | 6/4000 [00:25<4:37:30, 4.17s/it] 0%| | 7/4000 [00:30<4:45:38, 4.29s/it] 0%| | 8/4000 [00:34<4:41:01, 4.22s/it] 0%| | 9/4000 [00:38<4:37:14, 4.17s/it] 0%| | 10/4000 [00:42<4:34:56, 4.13s/it] {'loss': '9.602', 'grad_norm': '2.286', 'learning_rate': '0.0003745', 'epoch': '0.002423'}
 0%| | 10/4000 [00:42<4:34:56, 4.13s/it] 0%| | 11/4000 [00:46<4:33:45, 4.12s/it] 0%| | 12/4000 [00:50<4:33:06, 4.11s/it] 0%| | 13/4000 [00:54<4:36:32, 4.16s/it] 0%| | 14/4000 [00:59<4:38:38, 4.19s/it] 0%| | 15/4000 [01:03<4:36:01, 4.16s/it] 0%| | 16/4000 [01:07<4:34:29, 4.13s/it] 0%| | 17/4000 [01:11<4:33:13, 4.12s/it] 0%| | 18/4000 [01:15<4:32:44, 4.11s/it] 0%| | 19/4000 [01:19<4:32:15, 4.10s/it] 0%| | 20/4000 [01:24<4:39:36, 4.22s/it] {'loss': '6.495', 'grad_norm': '0.9675', 'learning_rate': '0.0004873', 'epoch': '0.004846'}
 0%| | 20/4000 [01:24<4:39:36, 4.22s/it] 1%| | 21/4000 [01:28<4:36:45, 4.17s/it] 1%| | 22/4000 [01:32<4:34:35, 4.14s/it] 1%| | 23/4000 [01:36<4:33:19, 4.12s/it] 1%| | 24/4000 [01:40<4:32:11, 4.11s/it] 1%| | 25/4000 [01:44<4:31:40, 4.10s/it] 1%| | 26/4000 [01:48<4:31:10, 4.09s/it] 1%| | 27/4000 [01:52<4:38:34, 4.21s/it] 1%| | 28/4000 [01:57<4:35:46, 4.17s/it] 1%| | 29/4000 [02:01<4:34:07, 4.14s/it] 1%| | 30/4000 [02:05<4:33:20, 4.13s/it] {'loss': '5.982', 'grad_norm': '1.023', 'learning_rate': '0.0005532', 'epoch': '0.007269'}
 1%| | 30/4000 [02:05<4:33:20, 4.13s/it] 1%| | 31/4000 [02:09<4:33:04, 4.13s/it] 1%| | 32/4000 [02:13<4:33:06, 4.13s/it] 1%| | 33/4000 [02:17<4:32:41, 4.12s/it] 1%| | 34/4000 [02:22<4:40:00, 4.24s/it] 1%| | 35/4000 [02:26<4:36:57, 4.19s/it] 1%| | 36/4000 [02:30<4:34:41, 4.16s/it] 1%| | 37/4000 [02:34<4:33:18, 4.14s/it] 1%| | 38/4000 [02:38<4:32:43, 4.13s/it] 1%| | 39/4000 [02:42<4:32:33, 4.13s/it] 1%| | 40/4000 [02:46<4:35:49, 4.18s/it] {'loss': '5.683', 'grad_norm': '1.414', 'learning_rate': '0.0006', 'epoch': '0.009692'}
 1%| | 40/4000 [02:46<4:35:49, 4.18s/it] 1%| | 41/4000 [02:51<4:38:33, 4.22s/it] 1%| | 42/4000 [02:55<4:37:00, 4.20s/it] 1%| | 43/4000 [02:59<4:35:33, 4.18s/it] 1%| | 44/4000 [03:03<4:33:54, 4.15s/it] 1%| | 45/4000 [03:07<4:32:41, 4.14s/it] 1%| | 46/4000 [03:11<4:32:04, 4.13s/it] 1%| | 47/4000 [03:16<4:39:07, 4.24s/it] 1%| | 48/4000 [03:20<4:36:54, 4.20s/it] 1%| | 49/4000 [03:24<4:35:00, 4.18s/it] 1%|▏ | 50/4000 [03:28<4:33:25, 4.15s/it] {'loss': '5.258', 'grad_norm': '2.436', 'learning_rate': '0.0005986', 'epoch': '0.01211'}
 1%|▏ | 50/4000 [03:28<4:33:25, 4.15s/it] 1%|▏ | 51/4000 [03:32<4:32:41, 4.14s/it] 1%|▏ | 52/4000 [03:36<4:31:52, 4.13s/it] 1%|▏ | 53/4000 [03:40<4:30:50, 4.12s/it] 1%|▏ | 54/4000 [03:45<4:37:37, 4.22s/it] 1%|▏ | 55/4000 [03:49<4:34:50, 4.18s/it] 1%|▏ | 56/4000 [03:53<4:32:55, 4.15s/it] 1%|▏ | 57/4000 [03:57<4:31:40, 4.13s/it] 1%|▏ | 58/4000 [04:01<4:30:43, 4.12s/it] 1%|▏ | 59/4000 [04:05<4:30:07, 4.11s/it] 2%|▏ | 60/4000 [04:09<4:29:39, 4.11s/it] {'loss': '4.63', 'grad_norm': '1.147', 'learning_rate': '0.0005971', 'epoch': '0.01454'}
 2%|▏ | 60/4000 [04:09<4:29:39, 4.11s/it] 2%|▏ | 61/4000 [04:14<4:36:15, 4.21s/it] 2%|▏ | 62/4000 [04:18<4:34:06, 4.18s/it] 2%|▏ | 63/4000 [04:22<4:32:23, 4.15s/it] 2%|▏ | 64/4000 [04:26<4:31:10, 4.13s/it] 2%|▏ | 65/4000 [04:30<4:30:13, 4.12s/it] 2%|▏ | 66/4000 [04:34<4:29:33, 4.11s/it] 2%|▏ | 67/4000 [04:39<4:32:41, 4.16s/it] 2%|▏ | 68/4000 [04:43<4:34:43, 4.19s/it] 2%|▏ | 69/4000 [04:47<4:32:46, 4.16s/it] 2%|▏ | 70/4000 [04:51<4:31:28, 4.14s/it] {'loss': '3.956', 'grad_norm': '1.276', 'learning_rate': '0.0005956', 'epoch': '0.01696'}
 2%|▏ | 70/4000 [04:51<4:31:28, 4.14s/it] 2%|▏ | 71/4000 [04:55<4:30:15, 4.13s/it] 2%|▏ | 72/4000 [04:59<4:29:14, 4.11s/it] 2%|▏ | 73/4000 [05:03<4:28:36, 4.10s/it] 2%|▏ | 74/4000 [05:08<4:35:56, 4.22s/it] 2%|▏ | 75/4000 [05:12<4:33:28, 4.18s/it] 2%|▏ | 76/4000 [05:16<4:31:42, 4.15s/it] 2%|▏ | 77/4000 [05:20<4:30:22, 4.14s/it] 2%|▏ | 78/4000 [05:24<4:29:19, 4.12s/it] 2%|▏ | 79/4000 [05:28<4:28:46, 4.11s/it] 2%|▏ | 80/4000 [05:32<4:28:17, 4.11s/it] {'loss': '3.471', 'grad_norm': '0.7973', 'learning_rate': '0.0005941', 'epoch': '0.01938'}
 2%|▏ | 80/4000 [05:32<4:28:17, 4.11s/it] 2%|▏ | 81/4000 [05:37<4:33:40, 4.19s/it] 2%|▏ | 82/4000 [05:41<4:31:34, 4.16s/it] 2%|▏ | 83/4000 [05:45<4:30:06, 4.14s/it] 2%|▏ | 84/4000 [05:49<4:29:04, 4.12s/it] 2%|▏ | 85/4000 [05:53<4:28:28, 4.11s/it] 2%|▏ | 86/4000 [05:57<4:28:15, 4.11s/it] 2%|▏ | 87/4000 [06:01<4:28:05, 4.11s/it] 2%|▏ | 88/4000 [06:06<4:35:19, 4.22s/it] 2%|▏ | 89/4000 [06:10<4:32:36, 4.18s/it] 2%|▏ | 90/4000 [06:14<4:31:02, 4.16s/it] {'loss': '3.021', 'grad_norm': '0.7119', 'learning_rate': '0.0005926', 'epoch': '0.02181'}
 2%|▏ | 90/4000 [06:14<4:31:02, 4.16s/it] 2%|▏ | 91/4000 [06:18<4:29:50, 4.14s/it] 2%|▏ | 92/4000 [06:22<4:28:49, 4.13s/it] 2%|▏ | 93/4000 [06:26<4:28:11, 4.12s/it] 2%|▏ | 94/4000 [06:31<4:33:26, 4.20s/it] 2%|▏ | 95/4000 [06:35<4:31:19, 4.17s/it] 2%|▏ | 96/4000 [06:39<4:29:39, 4.14s/it] 2%|▏ | 97/4000 [06:43<4:28:49, 4.13s/it] 2%|▏ | 98/4000 [06:47<4:27:56, 4.12s/it] 2%|▏ | 99/4000 [06:51<4:27:28, 4.11s/it] 2%|▎ | 100/4000 [06:55<4:26:55, 4.11s/it] {'loss': '2.637', 'grad_norm': '0.6688', 'learning_rate': '0.0005911', 'epoch': '0.02423'}
 2%|▎ | 100/4000 [06:55<4:26:55, 4.11s/it] 3%|▎ | 101/4000 [07:00<4:34:09, 4.22s/it] 3%|▎ | 102/4000 [07:04<4:31:45, 4.18s/it] 3%|▎ | 103/4000 [07:08<4:29:58, 4.16s/it] 3%|▎ | 104/4000 [07:12<4:28:31, 4.14s/it] 3%|▎ | 105/4000 [07:16<4:27:33, 4.12s/it] 3%|▎ | 106/4000 [07:20<4:26:52, 4.11s/it] 3%|▎ | 107/4000 [07:24<4:26:21, 4.11s/it] 3%|▎ | 108/4000 [07:29<4:32:45, 4.20s/it] 3%|▎ | 109/4000 [07:33<4:30:28, 4.17s/it] 3%|▎ | 110/4000 [07:37<4:28:50, 4.15s/it] {'loss': '2.353', 'grad_norm': '0.5264', 'learning_rate': '0.0005895', 'epoch': '0.02665'}
 3%|▎ | 110/4000 [07:37<4:28:50, 4.15s/it] 3%|▎ | 111/4000 [07:41<4:27:40, 4.13s/it] 3%|▎ | 112/4000 [07:45<4:26:59, 4.12s/it] 3%|▎ | 113/4000 [07:49<4:26:34, 4.11s/it] 3%|▎ | 114/4000 [07:53<4:26:06, 4.11s/it] 3%|▎ | 115/4000 [07:58<4:32:41, 4.21s/it] 3%|▎ | 116/4000 [08:02<4:30:24, 4.18s/it] 3%|▎ | 117/4000 [08:06<4:28:57, 4.16s/it] 3%|▎ | 118/4000 [08:10<4:27:45, 4.14s/it] 3%|▎ | 119/4000 [08:14<4:27:05, 4.13s/it] 3%|▎ | 120/4000 [08:18<4:26:31, 4.12s/it] {'loss': '2.108', 'grad_norm': '0.4656', 'learning_rate': '0.000588', 'epoch': '0.02908'}
 3%|▎ | 120/4000 [08:18<4:26:31, 4.12s/it] 3%|▎ | 121/4000 [08:23<4:33:00, 4.22s/it] 3%|▎ | 122/4000 [08:27<4:30:33, 4.19s/it] 3%|▎ | 123/4000 [08:31<4:28:35, 4.16s/it] 3%|▎ | 124/4000 [08:35<4:27:29, 4.14s/it] 3%|▎ | 125/4000 [08:39<4:26:33, 4.13s/it] 3%|▎ | 126/4000 [08:43<4:26:02, 4.12s/it] 3%|▎ | 127/4000 [08:47<4:25:25, 4.11s/it] 3%|▎ | 128/4000 [08:52<4:30:59, 4.20s/it] 3%|▎ | 129/4000 [08:56<4:28:58, 4.17s/it] 3%|▎ | 130/4000 [09:00<4:27:26, 4.15s/it] {'loss': '1.974', 'grad_norm': '0.4384', 'learning_rate': '0.0005865', 'epoch': '0.0315'}
 3%|▎ | 130/4000 [09:00<4:27:26, 4.15s/it] 3%|▎ | 131/4000 [09:04<4:26:40, 4.14s/it] 3%|▎ | 132/4000 [09:08<4:25:50, 4.12s/it] 3%|▎ | 133/4000 [09:12<4:25:21, 4.12s/it] 3%|▎ | 134/4000 [09:16<4:24:53, 4.11s/it] 3%|▎ | 135/4000 [09:21<4:31:25, 4.21s/it] 3%|▎ | 136/4000 [09:25<4:29:01, 4.18s/it] 3%|▎ | 137/4000 [09:29<4:27:19, 4.15s/it] 3%|▎ | 138/4000 [09:33<4:26:11, 4.14s/it] 3%|▎ | 139/4000 [09:37<4:25:21, 4.12s/it] 4%|▎ | 140/4000 [09:41<4:24:34, 4.11s/it] {'loss': '1.871', 'grad_norm': '0.443', 'learning_rate': '0.000585', 'epoch': '0.03392'}
 4%|▎ | 140/4000 [09:41<4:24:34, 4.11s/it] 4%|▎ | 141/4000 [09:45<4:27:53, 4.17s/it] 4%|▎ | 142/4000 [09:50<4:29:39, 4.19s/it] 4%|▎ | 143/4000 [09:54<4:27:37, 4.16s/it] 4%|▎ | 144/4000 [09:58<4:26:16, 4.14s/it] 4%|▎ | 145/4000 [10:02<4:25:30, 4.13s/it] 4%|▎ | 146/4000 [10:06<4:24:50, 4.12s/it] 4%|▎ | 147/4000 [10:10<4:24:09, 4.11s/it] 4%|▎ | 148/4000 [10:15<4:30:09, 4.21s/it] 4%|▎ | 149/4000 [10:19<4:27:54, 4.17s/it] 4%|▍ | 150/4000 [10:23<4:26:15, 4.15s/it] {'loss': '1.745', 'grad_norm': '0.3832', 'learning_rate': '0.0005835', 'epoch': '0.03634'}
 4%|▍ | 150/4000 [10:23<4:26:15, 4.15s/it] 4%|▍ | 151/4000 [10:27<4:25:09, 4.13s/it] 4%|▍ | 152/4000 [10:31<4:24:16, 4.12s/it] 4%|▍ | 153/4000 [10:35<4:23:44, 4.11s/it] 4%|▍ | 154/4000 [10:39<4:23:24, 4.11s/it] 4%|▍ | 155/4000 [10:44<4:29:38, 4.21s/it] 4%|▍ | 156/4000 [10:48<4:27:29, 4.18s/it] 4%|▍ | 157/4000 [10:52<4:26:01, 4.15s/it] 4%|▍ | 158/4000 [10:56<4:24:47, 4.14s/it] 4%|▍ | 159/4000 [11:00<4:24:03, 4.12s/it] 4%|▍ | 160/4000 [11:04<4:23:40, 4.12s/it] {'loss': '1.724', 'grad_norm': '0.3886', 'learning_rate': '0.000582', 'epoch': '0.03877'}
 4%|▍ | 160/4000 [11:04<4:23:40, 4.12s/it] 4%|▍ | 161/4000 [11:08<4:23:12, 4.11s/it] 4%|▍ | 162/4000 [11:13<4:28:48, 4.20s/it] 4%|▍ | 163/4000 [11:17<4:26:48, 4.17s/it] 4%|▍ | 164/4000 [11:21<4:25:17, 4.15s/it] 4%|▍ | 165/4000 [11:25<4:24:09, 4.13s/it] 4%|▍ | 166/4000 [11:29<4:23:26, 4.12s/it] 4%|▍ | 167/4000 [11:33<4:22:49, 4.11s/it] 4%|▍ | 168/4000 [11:37<4:25:55, 4.16s/it] 4%|▍ | 169/4000 [11:42<4:28:16, 4.20s/it] 4%|▍ | 170/4000 [11:46<4:26:00, 4.17s/it] {'loss': '1.625', 'grad_norm': '0.5112', 'learning_rate': '0.0005805', 'epoch': '0.04119'}
 4%|▍ | 170/4000 [11:46<4:26:00, 4.17s/it] 4%|▍ | 171/4000 [11:50<4:24:38, 4.15s/it] 4%|▍ | 172/4000 [11:54<4:23:38, 4.13s/it] 4%|▍ | 173/4000 [11:58<4:22:43, 4.12s/it] 4%|▍ | 174/4000 [12:02<4:22:21, 4.11s/it] 4%|▍ | 175/4000 [12:07<4:28:10, 4.21s/it] 4%|▍ | 176/4000 [12:11<4:26:01, 4.17s/it] 4%|▍ | 177/4000 [12:15<4:24:22, 4.15s/it] 4%|▍ | 178/4000 [12:19<4:23:30, 4.14s/it] 4%|▍ | 179/4000 [12:23<4:22:46, 4.13s/it] 4%|▍ | 180/4000 [12:27<4:22:07, 4.12s/it] {'loss': '1.559', 'grad_norm': '0.3595', 'learning_rate': '0.0005789', 'epoch': '0.04361'}
 4%|▍ | 180/4000 [12:27<4:22:07, 4.12s/it] 5%|▍ | 181/4000 [12:31<4:21:39, 4.11s/it] 5%|▍ | 182/4000 [12:36<4:27:47, 4.21s/it] 5%|▍ | 183/4000 [12:40<4:25:34, 4.17s/it] 5%|▍ | 184/4000 [12:44<4:24:02, 4.15s/it] 5%|▍ | 185/4000 [12:48<4:22:48, 4.13s/it] 5%|▍ | 186/4000 [12:52<4:22:00, 4.12s/it] 5%|▍ | 187/4000 [12:56<4:21:24, 4.11s/it] 5%|▍ | 188/4000 [13:00<4:21:00, 4.11s/it] 5%|▍ | 189/4000 [13:05<4:27:27, 4.21s/it] 5%|▍ | 190/4000 [13:09<4:25:15, 4.18s/it] {'loss': '1.55', 'grad_norm': '0.2786', 'learning_rate': '0.0005774', 'epoch': '0.04604'}
 5%|▍ | 190/4000 [13:09<4:25:15, 4.18s/it] 5%|▍ | 191/4000 [13:13<4:23:40, 4.15s/it] 5%|▍ | 192/4000 [13:17<4:22:23, 4.13s/it] 5%|▍ | 193/4000 [13:21<4:21:38, 4.12s/it] 5%|▍ | 194/4000 [13:25<4:21:07, 4.12s/it] 5%|▍ | 195/4000 [13:29<4:23:39, 4.16s/it] 5%|▍ | 196/4000 [13:34<4:25:30, 4.19s/it] 5%|▍ | 197/4000 [13:38<4:23:39, 4.16s/it] 5%|▍ | 198/4000 [13:42<4:22:29, 4.14s/it] 5%|▍ | 199/4000 [13:46<4:21:33, 4.13s/it] 5%|▌ | 200/4000 [13:50<4:21:04, 4.12s/it] {'loss': '1.418', 'grad_norm': '0.3144', 'learning_rate': '0.0005759', 'epoch': '0.04846'}
 5%|▌ | 200/4000 [13:50<4:21:04, 4.12s/it] 5%|▌ | 201/4000 [13:54<4:20:37, 4.12s/it] 5%|▌ | 202/4000 [13:59<4:27:28, 4.23s/it] 5%|▌ | 203/4000 [14:03<4:25:03, 4.19s/it] 5%|▌ | 204/4000 [14:07<4:23:04, 4.16s/it] 5%|▌ | 205/4000 [14:11<4:21:50, 4.14s/it] 5%|▌ | 206/4000 [14:15<4:20:49, 4.12s/it] 5%|▌ | 207/4000 [14:19<4:20:10, 4.12s/it] 5%|▌ | 208/4000 [14:23<4:19:47, 4.11s/it] 5%|▌ | 209/4000 [14:28<4:26:08, 4.21s/it] 5%|▌ | 210/4000 [14:32<4:23:52, 4.18s/it] {'loss': '1.449', 'grad_norm': '0.3036', 'learning_rate': '0.0005744', 'epoch': '0.05088'}
 5%|▌ | 210/4000 [14:32<4:23:52, 4.18s/it] 5%|▌ | 211/4000 [14:36<4:22:19, 4.15s/it] 5%|▌ | 212/4000 [14:40<4:21:20, 4.14s/it] 5%|▌ | 213/4000 [14:44<4:20:31, 4.13s/it] 5%|▌ | 214/4000 [14:48<4:19:59, 4.12s/it] 5%|▌ | 215/4000 [14:52<4:19:29, 4.11s/it] 5%|▌ | 216/4000 [14:57<4:25:25, 4.21s/it] 5%|▌ | 217/4000 [15:01<4:23:12, 4.17s/it] 5%|▌ | 218/4000 [15:05<4:21:32, 4.15s/it] 5%|▌ | 219/4000 [15:09<4:20:36, 4.14s/it] 6%|▌ | 220/4000 [15:13<4:19:40, 4.12s/it] {'loss': '1.435', 'grad_norm': '0.4483', 'learning_rate': '0.0005729', 'epoch': '0.05331'}
 6%|▌ | 220/4000 [15:13<4:19:40, 4.12s/it] 6%|▌ | 221/4000 [15:17<4:19:14, 4.12s/it] 6%|▌ | 222/4000 [15:22<4:25:10, 4.21s/it] 6%|▌ | 223/4000 [15:26<4:22:49, 4.18s/it] 6%|▌ | 224/4000 [15:30<4:21:28, 4.15s/it] 6%|▌ | 225/4000 [15:34<4:20:13, 4.14s/it] 6%|▌ | 226/4000 [15:38<4:19:24, 4.12s/it] 6%|▌ | 227/4000 [15:42<4:18:53, 4.12s/it] 6%|▌ | 228/4000 [15:46<4:18:26, 4.11s/it] 6%|▌ | 229/4000 [15:51<4:23:41, 4.20s/it] 6%|▌ | 230/4000 [15:55<4:21:44, 4.17s/it] {'loss': '1.39', 'grad_norm': '0.251', 'learning_rate': '0.0005714', 'epoch': '0.05573'}
 6%|▌ | 230/4000 [15:55<4:21:44, 4.17s/it] 6%|▌ | 231/4000 [15:59<4:20:26, 4.15s/it] 6%|▌ | 232/4000 [16:03<4:19:24, 4.13s/it] 6%|▌ | 233/4000 [16:07<4:18:37, 4.12s/it] 6%|▌ | 234/4000 [16:11<4:18:08, 4.11s/it] 6%|▌ | 235/4000 [16:15<4:17:46, 4.11s/it] 6%|▌ | 236/4000 [16:20<4:24:34, 4.22s/it] 6%|▌ | 237/4000 [16:24<4:22:22, 4.18s/it] 6%|▌ | 238/4000 [16:28<4:20:31, 4.16s/it] 6%|▌ | 239/4000 [16:32<4:19:13, 4.14s/it] 6%|▌ | 240/4000 [16:36<4:18:23, 4.12s/it] {'loss': '1.308', 'grad_norm': '0.2457', 'learning_rate': '0.0005698', 'epoch': '0.05815'}
 6%|▌ | 240/4000 [16:36<4:18:23, 4.12s/it] 6%|▌ | 241/4000 [16:40<4:17:50, 4.12s/it] 6%|▌ | 242/4000 [16:44<4:17:18, 4.11s/it] 6%|▌ | 243/4000 [16:49<4:23:12, 4.20s/it] 6%|▌ | 244/4000 [16:53<4:21:07, 4.17s/it] 6%|▌ | 245/4000 [16:57<4:19:47, 4.15s/it] 6%|▌ | 246/4000 [17:01<4:18:42, 4.13s/it] 6%|▌ | 247/4000 [17:05<4:18:06, 4.13s/it] 6%|▌ | 248/4000 [17:09<4:17:43, 4.12s/it] 6%|▌ | 249/4000 [17:14<4:23:47, 4.22s/it] 6%|▋ | 250/4000 [17:18<4:21:24, 4.18s/it] {'loss': '1.299', 'grad_norm': '0.2576', 'learning_rate': '0.0005683', 'epoch': '0.06057'}
 6%|▋ | 250/4000 [17:18<4:21:24, 4.18s/it] 6%|▋ | 251/4000 [17:22<4:19:46, 4.16s/it] 6%|▋ | 252/4000 [17:26<4:18:36, 4.14s/it] 6%|▋ | 253/4000 [17:30<4:17:57, 4.13s/it] 6%|▋ | 254/4000 [17:34<4:17:16, 4.12s/it] 6%|▋ | 255/4000 [17:38<4:16:44, 4.11s/it] 6%|▋ | 256/4000 [17:43<4:22:49, 4.21s/it] 6%|▋ | 257/4000 [17:47<4:20:36, 4.18s/it] 6%|▋ | 258/4000 [17:51<4:19:00, 4.15s/it] 6%|▋ | 259/4000 [17:55<4:17:49, 4.14s/it] 6%|▋ | 260/4000 [17:59<4:17:05, 4.12s/it] {'loss': '1.242', 'grad_norm': '0.2659', 'learning_rate': '0.0005668', 'epoch': '0.063'}
 6%|▋ | 260/4000 [17:59<4:17:05, 4.12s/it] 7%|▋ | 261/4000 [18:03<4:16:37, 4.12s/it] 7%|▋ | 262/4000 [18:07<4:16:13, 4.11s/it] 7%|▋ | 263/4000 [18:12<4:21:17, 4.20s/it] 7%|▋ | 264/4000 [18:16<4:19:31, 4.17s/it] 7%|▋ | 265/4000 [18:20<4:18:02, 4.15s/it] 7%|▋ | 266/4000 [18:24<4:17:04, 4.13s/it] 7%|▋ | 267/4000 [18:28<4:16:23, 4.12s/it] 7%|▋ | 268/4000 [18:32<4:15:58, 4.12s/it] 7%|▋ | 269/4000 [18:36<4:19:12, 4.17s/it] 7%|▋ | 270/4000 [18:41<4:21:23, 4.20s/it] {'loss': '1.182', 'grad_norm': '0.286', 'learning_rate': '0.0005653', 'epoch': '0.06542'}
 7%|▋ | 270/4000 [18:41<4:21:23, 4.20s/it] 7%|▋ | 271/4000 [18:45<4:19:14, 4.17s/it] 7%|▋ | 272/4000 [18:49<4:17:45, 4.15s/it] 7%|▋ | 273/4000 [18:53<4:16:32, 4.13s/it] 7%|▋ | 274/4000 [18:57<4:15:53, 4.12s/it] 7%|▋ | 275/4000 [19:01<4:15:24, 4.11s/it] 7%|▋ | 276/4000 [19:06<4:21:28, 4.21s/it] 7%|▋ | 277/4000 [19:10<4:19:08, 4.18s/it] 7%|▋ | 278/4000 [19:14<4:17:37, 4.15s/it] 7%|▋ | 279/4000 [19:18<4:16:21, 4.13s/it] 7%|▋ | 280/4000 [19:22<4:15:38, 4.12s/it] {'loss': '1.113', 'grad_norm': '0.2803', 'learning_rate': '0.0005638', 'epoch': '0.06784'}
 7%|▋ | 280/4000 [19:22<4:15:38, 4.12s/it] 7%|▋ | 281/4000 [19:26<4:14:59, 4.11s/it] 7%|▋ | 282/4000 [19:30<4:14:38, 4.11s/it] 7%|▋ | 283/4000 [19:35<4:19:35, 4.19s/it] 7%|▋ | 284/4000 [19:39<4:17:50, 4.16s/it] 7%|▋ | 285/4000 [19:43<4:16:32, 4.14s/it] 7%|▋ | 286/4000 [19:47<4:15:42, 4.13s/it] 7%|▋ | 287/4000 [19:51<4:15:06, 4.12s/it] 7%|▋ | 288/4000 [19:55<4:14:33, 4.11s/it] 7%|▋ | 289/4000 [19:59<4:14:12, 4.11s/it] 7%|▋ | 290/4000 [20:04<4:20:31, 4.21s/it] {'loss': '1.162', 'grad_norm': '0.3629', 'learning_rate': '0.0005623', 'epoch': '0.07027'}
 7%|▋ | 290/4000 [20:04<4:20:31, 4.21s/it] 7%|▋ | 291/4000 [20:08<4:18:26, 4.18s/it] 7%|▋ | 292/4000 [20:12<4:16:52, 4.16s/it] 7%|▋ | 293/4000 [20:16<4:15:44, 4.14s/it] 7%|▋ | 294/4000 [20:20<4:14:48, 4.13s/it] 7%|▋ | 295/4000 [20:24<4:14:18, 4.12s/it] 7%|▋ | 296/4000 [20:28<4:17:19, 4.17s/it] 7%|▋ | 297/4000 [20:33<4:18:51, 4.19s/it] 7%|▋ | 298/4000 [20:37<4:17:01, 4.17s/it] 7%|▋ | 299/4000 [20:41<4:15:36, 4.14s/it] 8%|▊ | 300/4000 [20:45<4:14:30, 4.13s/it] {'loss': '1.117', 'grad_norm': '0.2806', 'learning_rate': '0.0005608', 'epoch': '0.07269'}
 8%|▊ | 300/4000 [20:45<4:14:30, 4.13s/it] 8%|▊ | 301/4000 [20:49<4:13:55, 4.12s/it] 8%|▊ | 302/4000 [20:53<4:13:33, 4.11s/it] 8%|▊ | 303/4000 [20:58<4:19:20, 4.21s/it] 8%|▊ | 304/4000 [21:02<4:17:13, 4.18s/it] 8%|▊ | 305/4000 [21:06<4:15:38, 4.15s/it] 8%|▊ | 306/4000 [21:10<4:14:41, 4.14s/it] 8%|▊ | 307/4000 [21:14<4:13:49, 4.12s/it] 8%|▊ | 308/4000 [21:18<4:13:20, 4.12s/it] 8%|▊ | 309/4000 [21:22<4:12:46, 4.11s/it] 8%|▊ | 310/4000 [21:27<4:19:34, 4.22s/it] {'loss': '1.126', 'grad_norm': '0.2639', 'learning_rate': '0.0005592', 'epoch': '0.07511'}
 8%|▊ | 310/4000 [21:27<4:19:34, 4.22s/it] 8%|▊ | 311/4000 [21:31<4:17:11, 4.18s/it] 8%|▊ | 312/4000 [21:35<4:15:25, 4.16s/it] 8%|▊ | 313/4000 [21:39<4:14:03, 4.13s/it] 8%|▊ | 314/4000 [21:43<4:13:10, 4.12s/it] 8%|▊ | 315/4000 [21:47<4:12:26, 4.11s/it] 8%|▊ | 316/4000 [21:51<4:12:01, 4.10s/it] 8%|▊ | 317/4000 [21:56<4:17:13, 4.19s/it] 8%|▊ | 318/4000 [22:00<4:15:12, 4.16s/it] 8%|▊ | 319/4000 [22:04<4:13:41, 4.14s/it] 8%|▊ | 320/4000 [22:08<4:12:47, 4.12s/it] {'loss': '1.049', 'grad_norm': '0.2683', 'learning_rate': '0.0005577', 'epoch': '0.07754'}
 8%|▊ | 320/4000 [22:08<4:12:47, 4.12s/it] 8%|▊ | 321/4000 [22:12<4:12:10, 4.11s/it] 8%|▊ | 322/4000 [22:16<4:11:41, 4.11s/it] 8%|▊ | 323/4000 [22:20<4:14:10, 4.15s/it] 8%|▊ | 324/4000 [22:24<4:16:21, 4.18s/it] 8%|▊ | 325/4000 [22:29<4:14:33, 4.16s/it] 8%|▊ | 326/4000 [22:33<4:13:19, 4.14s/it] 8%|▊ | 327/4000 [22:37<4:12:25, 4.12s/it] 8%|▊ | 328/4000 [22:41<4:11:44, 4.11s/it] 8%|▊ | 329/4000 [22:45<4:11:12, 4.11s/it] 8%|▊ | 330/4000 [22:49<4:16:56, 4.20s/it] {'loss': '1.116', 'grad_norm': '0.2458', 'learning_rate': '0.0005562', 'epoch': '0.07996'}
 8%|▊ | 330/4000 [22:49<4:16:56, 4.20s/it] 8%|▊ | 331/4000 [22:53<4:15:01, 4.17s/it] 8%|▊ | 332/4000 [22:58<4:13:29, 4.15s/it] 8%|▊ | 333/4000 [23:02<4:12:41, 4.13s/it] 8%|▊ | 334/4000 [23:06<4:12:04, 4.13s/it] 8%|▊ | 335/4000 [23:10<4:11:33, 4.12s/it] 8%|▊ | 336/4000 [23:14<4:11:08, 4.11s/it] 8%|▊ | 337/4000 [23:18<4:17:13, 4.21s/it] 8%|▊ | 338/4000 [23:23<4:15:01, 4.18s/it] 8%|▊ | 339/4000 [23:27<4:13:30, 4.15s/it] 8%|▊ | 340/4000 [23:31<4:12:36, 4.14s/it] {'loss': '1.064', 'grad_norm': '0.2021', 'learning_rate': '0.0005547', 'epoch': '0.08238'}
 8%|▊ | 340/4000 [23:31<4:12:36, 4.14s/it] 9%|▊ | 341/4000 [23:35<4:11:48, 4.13s/it] 9%|▊ | 342/4000 [23:39<4:11:19, 4.12s/it] 9%|▊ | 343/4000 [23:43<4:10:43, 4.11s/it] 9%|▊ | 344/4000 [23:47<4:16:51, 4.22s/it] 9%|▊ | 345/4000 [23:52<4:14:38, 4.18s/it] 9%|▊ | 346/4000 [23:56<4:13:07, 4.16s/it] 9%|▊ | 347/4000 [24:00<4:12:03, 4.14s/it] 9%|▊ | 348/4000 [24:04<4:11:15, 4.13s/it] 9%|▊ | 349/4000 [24:08<4:10:32, 4.12s/it] 9%|▉ | 350/4000 [24:12<4:15:49, 4.21s/it] {'loss': '1.056', 'grad_norm': '0.2233', 'learning_rate': '0.0005532', 'epoch': '0.0848'}
 9%|▉ | 350/4000 [24:12<4:15:49, 4.21s/it] 9%|▉ | 351/4000 [24:16<4:13:43, 4.17s/it] 9%|▉ | 352/4000 [24:21<4:12:09, 4.15s/it] 9%|▉ | 353/4000 [24:25<4:11:05, 4.13s/it] 9%|▉ | 354/4000 [24:29<4:10:23, 4.12s/it] 9%|▉ | 355/4000 [24:33<4:09:44, 4.11s/it] 9%|▉ | 356/4000 [24:37<4:09:24, 4.11s/it] 9%|▉ | 357/4000 [24:41<4:15:41, 4.21s/it] 9%|▉ | 358/4000 [24:45<4:13:29, 4.18s/it] 9%|▉ | 359/4000 [24:50<4:11:42, 4.15s/it] 9%|▉ | 360/4000 [24:54<4:10:44, 4.13s/it] {'loss': '1.067', 'grad_norm': '0.2488', 'learning_rate': '0.0005517', 'epoch': '0.08723'}
 9%|▉ | 360/4000 [24:54<4:10:44, 4.13s/it] 9%|▉ | 361/4000 [24:58<4:09:55, 4.12s/it] 9%|▉ | 362/4000 [25:02<4:09:18, 4.11s/it] 9%|▉ | 363/4000 [25:06<4:09:08, 4.11s/it] 9%|▉ | 364/4000 [25:10<4:14:42, 4.20s/it] 9%|▉ | 365/4000 [25:14<4:12:35, 4.17s/it] 9%|▉ | 366/4000 [25:19<4:11:01, 4.14s/it] 9%|▉ | 367/4000 [25:23<4:09:58, 4.13s/it] 9%|▉ | 368/4000 [25:27<4:09:10, 4.12s/it] 9%|▉ | 369/4000 [25:31<4:08:32, 4.11s/it] 9%|▉ | 370/4000 [25:35<4:08:18, 4.10s/it] {'loss': '1.039', 'grad_norm': '0.2644', 'learning_rate': '0.0005502', 'epoch': '0.08965'}
 9%|▉ | 370/4000 [25:35<4:08:18, 4.10s/it] 9%|▉ | 371/4000 [25:39<4:14:43, 4.21s/it] 9%|▉ | 372/4000 [25:43<4:12:28, 4.18s/it] 9%|▉ | 373/4000 [25:48<4:10:47, 4.15s/it] 9%|▉ | 374/4000 [25:52<4:09:34, 4.13s/it] 9%|▉ | 375/4000 [25:56<4:08:52, 4.12s/it] 9%|▉ | 376/4000 [26:00<4:08:07, 4.11s/it] 9%|▉ | 377/4000 [26:04<4:13:59, 4.21s/it] 9%|▉ | 378/4000 [26:08<4:11:47, 4.17s/it] 9%|▉ | 379/4000 [26:12<4:10:30, 4.15s/it] 10%|▉ | 380/4000 [26:17<4:09:22, 4.13s/it] {'loss': '1.02', 'grad_norm': '0.2621', 'learning_rate': '0.0005486', 'epoch': '0.09207'}
 10%|▉ | 380/4000 [26:17<4:09:22, 4.13s/it] 10%|▉ | 381/4000 [26:21<4:08:34, 4.12s/it] 10%|▉ | 382/4000 [26:25<4:07:59, 4.11s/it] 10%|▉ | 383/4000 [26:29<4:07:43, 4.11s/it] 10%|▉ | 384/4000 [26:33<4:13:46, 4.21s/it] 10%|▉ | 385/4000 [26:37<4:11:30, 4.17s/it] 10%|▉ | 386/4000 [26:41<4:09:58, 4.15s/it] 10%|▉ | 387/4000 [26:46<4:08:59, 4.13s/it] 10%|▉ | 388/4000 [26:50<4:08:02, 4.12s/it] 10%|▉ | 389/4000 [26:54<4:07:20, 4.11s/it] 10%|▉ | 390/4000 [26:58<4:06:52, 4.10s/it] {'loss': '0.9707', 'grad_norm': '0.2696', 'learning_rate': '0.0005471', 'epoch': '0.0945'}
 10%|▉ | 390/4000 [26:58<4:06:52, 4.10s/it] 10%|▉ | 391/4000 [27:02<4:12:38, 4.20s/it] 10%|▉ | 392/4000 [27:06<4:10:38, 4.17s/it] 10%|▉ | 393/4000 [27:10<4:09:06, 4.14s/it] 10%|▉ | 394/4000 [27:14<4:07:53, 4.12s/it] 10%|▉ | 395/4000 [27:19<4:07:10, 4.11s/it] 10%|▉ | 396/4000 [27:23<4:06:34, 4.11s/it] 10%|▉ | 397/4000 [27:27<4:09:34, 4.16s/it] 10%|▉ | 398/4000 [27:31<4:11:09, 4.18s/it] 10%|▉ | 399/4000 [27:35<4:09:32, 4.16s/it] 10%|█ | 400/4000 [27:39<4:08:22, 4.14s/it] {'loss': '0.9642', 'grad_norm': '0.2032', 'learning_rate': '0.0005456', 'epoch': '0.09692'}
 10%|█ | 400/4000 [27:39<4:08:22, 4.14s/it] 10%|█ | 401/4000 [27:43<4:07:21, 4.12s/it] 10%|█ | 402/4000 [27:48<4:06:41, 4.11s/it] 10%|█ | 403/4000 [27:52<4:06:12, 4.11s/it] 10%|█ | 404/4000 [27:56<4:12:35, 4.21s/it] 10%|█ | 405/4000 [28:00<4:10:15, 4.18s/it] 10%|█ | 406/4000 [28:04<4:08:28, 4.15s/it] 10%|█ | 407/4000 [28:08<4:07:28, 4.13s/it] 10%|█ | 408/4000 [28:12<4:06:42, 4.12s/it] 10%|█ | 409/4000 [28:17<4:06:11, 4.11s/it] 10%|█ | 410/4000 [28:21<4:05:39, 4.11s/it] {'loss': '1.005', 'grad_norm': '0.2873', 'learning_rate': '0.0005441', 'epoch': '0.09934'}
 10%|█ | 410/4000 [28:21<4:05:39, 4.11s/it] 10%|█ | 411/4000 [28:25<4:11:33, 4.21s/it] 10%|█ | 412/4000 [28:29<4:09:37, 4.17s/it] 10%|█ | 413/4000 [28:33<4:07:59, 4.15s/it] 10%|█ | 414/4000 [28:37<4:06:43, 4.13s/it] 10%|█ | 415/4000 [28:41<4:05:57, 4.12s/it] 10%|█ | 416/4000 [28:46<4:05:18, 4.11s/it] 10%|█ | 417/4000 [28:50<4:04:50, 4.10s/it] 10%|█ | 418/4000 [28:54<4:10:06, 4.19s/it] 10%|█ | 419/4000 [28:58<4:08:11, 4.16s/it] 10%|█ | 420/4000 [29:02<4:06:57, 4.14s/it] {'loss': '0.9969', 'grad_norm': '0.1983', 'learning_rate': '0.0005426', 'epoch': '0.1018'}
 10%|█ | 420/4000 [29:02<4:06:57, 4.14s/it] 11%|█ | 421/4000 [29:06<4:06:02, 4.12s/it] 11%|█ | 422/4000 [29:10<4:05:27, 4.12s/it] 11%|█ | 423/4000 [29:15<4:05:16, 4.11s/it] 11%|█ | 424/4000 [29:19<4:05:00, 4.11s/it] 11%|█ | 425/4000 [29:23<4:11:23, 4.22s/it] 11%|█ | 426/4000 [29:27<4:09:24, 4.19s/it] 11%|█ | 427/4000 [29:31<4:08:09, 4.17s/it] 11%|█ | 428/4000 [29:35<4:07:06, 4.15s/it] 11%|█ | 429/4000 [29:40<4:06:24, 4.14s/it] 11%|█ | 430/4000 [29:44<4:05:55, 4.13s/it] {'loss': '0.9302', 'grad_norm': '0.1899', 'learning_rate': '0.0005411', 'epoch': '0.1042'}
 11%|█ | 430/4000 [29:44<4:05:55, 4.13s/it] 11%|█ | 431/4000 [29:48<4:10:57, 4.22s/it] 11%|█ | 432/4000 [29:52<4:08:59, 4.19s/it] 11%|█ | 433/4000 [29:56<4:07:31, 4.16s/it] 11%|█ | 434/4000 [30:00<4:06:39, 4.15s/it] 11%|█ | 435/4000 [30:05<4:06:05, 4.14s/it] 11%|█ | 436/4000 [30:09<4:05:08, 4.13s/it] 11%|█ | 437/4000 [30:13<4:04:31, 4.12s/it] 11%|█ | 438/4000 [30:17<4:12:09, 4.25s/it] 11%|█ | 439/4000 [30:21<4:09:36, 4.21s/it] 11%|█ | 440/4000 [30:26<4:08:02, 4.18s/it] {'loss': '0.9665', 'grad_norm': '0.2194', 'learning_rate': '0.0005395', 'epoch': '0.1066'}
 11%|█ | 440/4000 [30:26<4:08:02, 4.18s/it] 11%|█ | 441/4000 [30:30<4:06:38, 4.16s/it] 11%|█ | 442/4000 [30:34<4:05:51, 4.15s/it] 11%|█ | 443/4000 [30:38<4:05:01, 4.13s/it] 11%|█ | 444/4000 [30:42<4:04:26, 4.12s/it] 11%|█ | 445/4000 [30:46<4:10:01, 4.22s/it] 11%|█ | 446/4000 [30:50<4:07:55, 4.19s/it] 11%|█ | 447/4000 [30:55<4:06:13, 4.16s/it] 11%|█ | 448/4000 [30:59<4:05:14, 4.14s/it] 11%|█ | 449/4000 [31:03<4:06:00, 4.16s/it] 11%|█▏ | 450/4000 [31:07<4:05:34, 4.15s/it] {'loss': '0.9361', 'grad_norm': '0.232', 'learning_rate': '0.000538', 'epoch': '0.109'}
 11%|█▏ | 450/4000 [31:07<4:05:34, 4.15s/it] 11%|█▏ | 451/4000 [31:11<4:07:10, 4.18s/it] 11%|█▏ | 452/4000 [31:16<4:09:29, 4.22s/it] 11%|█▏ | 453/4000 [31:20<4:07:40, 4.19s/it] 11%|█▏ | 454/4000 [31:24<4:06:09, 4.17s/it] 11%|█▏ | 455/4000 [31:28<4:05:25, 4.15s/it] 11%|█▏ | 456/4000 [31:32<4:04:42, 4.14s/it] 11%|█▏ | 457/4000 [31:36<4:04:31, 4.14s/it] 11%|█▏ | 458/4000 [31:41<4:11:03, 4.25s/it] 11%|█▏ | 459/4000 [31:45<4:08:55, 4.22s/it] 12%|█▏ | 460/4000 [31:49<4:07:20, 4.19s/it] {'loss': '0.9602', 'grad_norm': '0.2184', 'learning_rate': '0.0005365', 'epoch': '0.1115'}
 12%|█▏ | 460/4000 [31:49<4:07:20, 4.19s/it] 12%|█▏ | 461/4000 [31:53<4:06:08, 4.17s/it] 12%|█▏ | 462/4000 [31:57<4:05:09, 4.16s/it] 12%|█▏ | 463/4000 [32:01<4:04:41, 4.15s/it] 12%|█▏ | 464/4000 [32:05<4:04:11, 4.14s/it] 12%|█▏ | 465/4000 [32:10<4:09:12, 4.23s/it] 12%|█▏ | 466/4000 [32:14<4:07:08, 4.20s/it] 12%|█▏ | 467/4000 [32:18<4:05:40, 4.17s/it] 12%|█▏ | 468/4000 [32:22<4:05:05, 4.16s/it] 12%|█▏ | 469/4000 [32:26<4:04:18, 4.15s/it] 12%|█▏ | 470/4000 [32:31<4:03:56, 4.15s/it] {'loss': '0.8988', 'grad_norm': '0.2376', 'learning_rate': '0.000535', 'epoch': '0.1139'}
 12%|█▏ | 470/4000 [32:31<4:03:56, 4.15s/it] 12%|█▏ | 471/4000 [32:35<4:03:56, 4.15s/it] 12%|█▏ | 472/4000 [32:39<4:10:09, 4.25s/it] 12%|█▏ | 473/4000 [32:43<4:07:55, 4.22s/it] 12%|█▏ | 474/4000 [32:47<4:06:14, 4.19s/it] 12%|█▏ | 475/4000 [32:52<4:05:14, 4.17s/it] 12%|█▏ | 476/4000 [32:56<4:04:08, 4.16s/it] 12%|█▏ | 477/4000 [33:00<4:03:26, 4.15s/it] 12%|█▏ | 478/4000 [33:04<4:09:43, 4.25s/it] 12%|█▏ | 479/4000 [33:08<4:07:33, 4.22s/it] 12%|█▏ | 480/4000 [33:13<4:05:45, 4.19s/it] {'loss': '0.8198', 'grad_norm': '0.2114', 'learning_rate': '0.0005335', 'epoch': '0.1163'}
 12%|█▏ | 480/4000 [33:13<4:05:45, 4.19s/it] 12%|█▏ | 481/4000 [33:17<4:04:21, 4.17s/it] 12%|█▏ | 482/4000 [33:21<4:03:23, 4.15s/it] 12%|█▏ | 483/4000 [33:25<4:02:42, 4.14s/it] 12%|█▏ | 484/4000 [33:29<4:02:29, 4.14s/it] 12%|█▏ | 485/4000 [33:33<4:07:35, 4.23s/it] 12%|█▏ | 486/4000 [33:38<4:05:56, 4.20s/it] 12%|█▏ | 487/4000 [33:42<4:04:26, 4.17s/it] 12%|█▏ | 488/4000 [33:46<4:03:21, 4.16s/it] 12%|█▏ | 489/4000 [33:50<4:02:54, 4.15s/it] 12%|█▏ | 490/4000 [33:54<4:02:22, 4.14s/it] {'loss': '0.8315', 'grad_norm': '0.1903', 'learning_rate': '0.000532', 'epoch': '0.1187'}
 12%|█▏ | 490/4000 [33:54<4:02:22, 4.14s/it] 12%|█▏ | 491/4000 [33:58<4:02:09, 4.14s/it] 12%|█▏ | 492/4000 [34:03<4:08:39, 4.25s/it] 12%|█▏ | 493/4000 [34:07<4:06:13, 4.21s/it] 12%|█▏ | 494/4000 [34:11<4:04:42, 4.19s/it] 12%|█▏ | 495/4000 [34:15<4:03:32, 4.17s/it] 12%|█▏ | 496/4000 [34:19<4:02:49, 4.16s/it] 12%|█▏ | 497/4000 [34:23<4:02:04, 4.15s/it] 12%|█▏ | 498/4000 [34:28<4:01:56, 4.15s/it] 12%|█▏ | 499/4000 [34:32<4:07:15, 4.24s/it] 12%|█▎ | 500/4000 [34:36<4:05:27, 4.21s/it] {'loss': '0.8515', 'grad_norm': '0.2477', 'learning_rate': '0.0005305', 'epoch': '0.1211'}
 12%|█▎ | 500/4000 [34:36<4:05:27, 4.21s/it] 13%|█▎ | 501/4000 [34:40<4:04:12, 4.19s/it] 13%|█▎ | 502/4000 [34:44<4:03:22, 4.17s/it] 13%|█▎ | 503/4000 [34:49<4:02:14, 4.16s/it] 13%|█▎ | 504/4000 [34:53<4:01:50, 4.15s/it] 13%|█▎ | 505/4000 [34:57<4:04:23, 4.20s/it] 13%|█▎ | 506/4000 [35:01<4:06:37, 4.24s/it] 13%|█▎ | 507/4000 [35:05<4:04:32, 4.20s/it] 13%|█▎ | 508/4000 [35:10<4:03:22, 4.18s/it] 13%|█▎ | 509/4000 [35:14<4:02:11, 4.16s/it] 13%|█▎ | 510/4000 [35:18<4:01:42, 4.16s/it] {'loss': '0.8253', 'grad_norm': '0.1977', 'learning_rate': '0.0005289', 'epoch': '0.1236'}
 13%|█▎ | 510/4000 [35:18<4:01:42, 4.16s/it] 13%|█▎ | 511/4000 [35:22<4:00:59, 4.14s/it] 13%|█▎ | 512/4000 [35:26<4:06:49, 4.25s/it] 13%|█▎ | 513/4000 [35:31<4:04:45, 4.21s/it] 13%|█▎ | 514/4000 [35:35<4:03:11, 4.19s/it] 13%|█▎ | 515/4000 [35:39<4:01:53, 4.16s/it] 13%|█▎ | 516/4000 [35:43<4:01:20, 4.16s/it] 13%|█▎ | 517/4000 [35:47<4:00:38, 4.15s/it] 13%|█▎ | 518/4000 [35:51<4:00:28, 4.14s/it] 13%|█▎ | 519/4000 [35:56<4:05:44, 4.24s/it] 13%|█▎ | 520/4000 [36:00<4:03:55, 4.21s/it] {'loss': '0.8197', 'grad_norm': '0.2504', 'learning_rate': '0.0005274', 'epoch': '0.126'}
 13%|█▎ | 520/4000 [36:00<4:03:55, 4.21s/it] 13%|█▎ | 521/4000 [36:04<4:02:24, 4.18s/it] 13%|█▎ | 522/4000 [36:08<4:01:08, 4.16s/it] 13%|█▎ | 523/4000 [36:12<4:00:48, 4.16s/it] 13%|█▎ | 524/4000 [36:16<4:00:30, 4.15s/it] 13%|█▎ | 525/4000 [36:20<3:59:49, 4.14s/it] 13%|█▎ | 526/4000 [36:25<4:06:01, 4.25s/it] 13%|█▎ | 527/4000 [36:29<4:04:08, 4.22s/it] 13%|█▎ | 528/4000 [36:33<4:02:19, 4.19s/it] 13%|█▎ | 529/4000 [36:37<4:01:01, 4.17s/it] 13%|█▎ | 530/4000 [36:41<4:00:23, 4.16s/it] {'loss': '0.8134', 'grad_norm': '0.1738', 'learning_rate': '0.0005259', 'epoch': '0.1284'}
 13%|█▎ | 530/4000 [36:41<4:00:23, 4.16s/it] 13%|█▎ | 531/4000 [36:46<3:59:42, 4.15s/it] 13%|█▎ | 532/4000 [36:50<4:04:52, 4.24s/it] 13%|█▎ | 533/4000 [36:54<4:02:46, 4.20s/it] 13%|█▎ | 534/4000 [36:58<4:01:35, 4.18s/it] 13%|█▎ | 535/4000 [37:02<4:00:26, 4.16s/it] 13%|█▎ | 536/4000 [37:07<4:00:07, 4.16s/it] 13%|█▎ | 537/4000 [37:11<4:01:48, 4.19s/it] 13%|█▎ | 538/4000 [37:15<4:01:56, 4.19s/it] 13%|█▎ | 539/4000 [37:20<4:08:53, 4.31s/it] 14%|█▎ | 540/4000 [37:24<4:07:56, 4.30s/it] {'loss': '0.7794', 'grad_norm': '0.2064', 'learning_rate': '0.0005244', 'epoch': '0.1308'}
 14%|█▎ | 540/4000 [37:24<4:07:56, 4.30s/it] 14%|█▎ | 541/4000 [37:28<4:07:28, 4.29s/it] 14%|█▎ | 542/4000 [37:32<4:04:54, 4.25s/it] 14%|█▎ | 543/4000 [37:36<4:03:24, 4.22s/it] 14%|█▎ | 544/4000 [37:41<4:02:25, 4.21s/it] 14%|█▎ | 545/4000 [37:45<4:01:26, 4.19s/it] 14%|█▎ | 546/4000 [37:49<4:07:07, 4.29s/it] 14%|█▎ | 547/4000 [37:53<4:04:39, 4.25s/it] 14%|█▎ | 548/4000 [37:58<4:02:56, 4.22s/it] 14%|█▎ | 549/4000 [38:02<4:01:33, 4.20s/it] 14%|█▍ | 550/4000 [38:06<4:00:33, 4.18s/it] {'loss': '0.8368', 'grad_norm': '0.2211', 'learning_rate': '0.0005229', 'epoch': '0.1333'}
 14%|█▍ | 550/4000 [38:06<4:00:33, 4.18s/it] 14%|█▍ | 551/4000 [38:10<4:00:12, 4.18s/it] 14%|█▍ | 552/4000 [38:14<4:02:41, 4.22s/it] 14%|█▍ | 553/4000 [38:19<4:05:13, 4.27s/it] 14%|█▍ | 554/4000 [38:23<4:03:03, 4.23s/it] 14%|█▍ | 555/4000 [38:27<4:01:23, 4.20s/it] 14%|█▍ | 556/4000 [38:31<4:00:14, 4.19s/it] 14%|█▍ | 557/4000 [38:35<3:59:36, 4.18s/it] 14%|█▍ | 558/4000 [38:39<3:59:04, 4.17s/it] 14%|█▍ | 559/4000 [38:44<4:05:51, 4.29s/it] 14%|█▍ | 560/4000 [38:48<4:03:14, 4.24s/it] {'loss': '0.7846', 'grad_norm': '0.1814', 'learning_rate': '0.0005214', 'epoch': '0.1357'}
 14%|█▍ | 560/4000 [38:48<4:03:14, 4.24s/it] 14%|█▍ | 561/4000 [38:52<4:01:12, 4.21s/it] 14%|█▍ | 562/4000 [38:56<3:59:57, 4.19s/it] 14%|█▍ | 563/4000 [39:01<3:59:12, 4.18s/it] 14%|█▍ | 564/4000 [39:05<3:58:20, 4.16s/it] 14%|█▍ | 565/4000 [39:09<3:57:38, 4.15s/it] 14%|█▍ | 566/4000 [39:13<4:03:40, 4.26s/it] 14%|█▍ | 567/4000 [39:18<4:01:43, 4.22s/it] 14%|█▍ | 568/4000 [39:22<4:00:10, 4.20s/it] 14%|█▍ | 569/4000 [39:26<3:59:04, 4.18s/it] 14%|█▍ | 570/4000 [39:30<3:58:16, 4.17s/it] {'loss': '0.8119', 'grad_norm': '0.3592', 'learning_rate': '0.0005198', 'epoch': '0.1381'}
 14%|█▍ | 570/4000 [39:30<3:58:16, 4.17s/it] 14%|█▍ | 571/4000 [39:34<3:57:28, 4.16s/it] 14%|█▍ | 572/4000 [39:38<3:57:07, 4.15s/it] 14%|█▍ | 573/4000 [39:43<4:03:24, 4.26s/it] 14%|█▍ | 574/4000 [39:47<4:01:06, 4.22s/it] 14%|█▍ | 575/4000 [39:51<3:59:09, 4.19s/it] 14%|█▍ | 576/4000 [39:55<3:58:21, 4.18s/it] 14%|█▍ | 577/4000 [39:59<3:57:39, 4.17s/it] 14%|█▍ | 578/4000 [40:03<3:57:13, 4.16s/it] 14%|█▍ | 579/4000 [40:08<4:01:30, 4.24s/it] 14%|█▍ | 580/4000 [40:12<4:04:47, 4.29s/it] {'loss': '0.8114', 'grad_norm': '0.176', 'learning_rate': '0.0005183', 'epoch': '0.1405'}
 14%|█▍ | 580/4000 [40:12<4:04:47, 4.29s/it] 15%|█▍ | 581/4000 [40:16<4:03:06, 4.27s/it] 15%|█▍ | 582/4000 [40:21<4:01:40, 4.24s/it] 15%|█▍ | 583/4000 [40:25<4:02:36, 4.26s/it] 15%|█▍ | 584/4000 [40:29<4:02:05, 4.25s/it] 15%|█▍ | 585/4000 [40:33<3:59:48, 4.21s/it] 15%|█▍ | 586/4000 [40:38<4:04:40, 4.30s/it] 15%|█▍ | 587/4000 [40:42<4:01:28, 4.25s/it] 15%|█▍ | 588/4000 [40:46<3:59:44, 4.22s/it] 15%|█▍ | 589/4000 [40:50<3:58:20, 4.19s/it] 15%|█▍ | 590/4000 [40:54<3:57:02, 4.17s/it] {'loss': '0.7472', 'grad_norm': '0.1836', 'learning_rate': '0.0005168', 'epoch': '0.143'}
 15%|█▍ | 590/4000 [40:54<3:57:02, 4.17s/it] 15%|█▍ | 591/4000 [40:58<3:56:13, 4.16s/it] 15%|█▍ | 592/4000 [41:03<3:55:38, 4.15s/it] 15%|█▍ | 593/4000 [41:07<4:01:42, 4.26s/it] 15%|█▍ | 594/4000 [41:11<3:59:28, 4.22s/it] 15%|█▍ | 595/4000 [41:15<3:57:42, 4.19s/it] 15%|█▍ | 596/4000 [41:19<3:56:27, 4.17s/it] 15%|█▍ | 597/4000 [41:24<3:55:45, 4.16s/it] 15%|█▍ | 598/4000 [41:28<3:55:19, 4.15s/it] 15%|█▍ | 599/4000 [41:32<3:54:58, 4.15s/it] 15%|█▌ | 600/4000 [41:36<4:00:47, 4.25s/it] {'loss': '0.7377', 'grad_norm': '0.1711', 'learning_rate': '0.0005153', 'epoch': '0.1454'}
 15%|█▌ | 600/4000 [41:36<4:00:47, 4.25s/it] 15%|█▌ | 601/4000 [41:40<3:58:49, 4.22s/it] 15%|█▌ | 602/4000 [41:45<3:57:23, 4.19s/it] 15%|█▌ | 603/4000 [41:49<3:56:10, 4.17s/it] 15%|█▌ | 604/4000 [41:53<3:55:28, 4.16s/it] 15%|█▌ | 605/4000 [41:57<3:54:50, 4.15s/it] 15%|█▌ | 606/4000 [42:01<3:57:24, 4.20s/it] 15%|█▌ | 607/4000 [42:06<3:59:43, 4.24s/it] 15%|█▌ | 608/4000 [42:10<3:57:55, 4.21s/it] 15%|█▌ | 609/4000 [42:14<3:56:23, 4.18s/it] 15%|█▌ | 610/4000 [42:18<3:55:32, 4.17s/it] {'loss': '0.7716', 'grad_norm': '0.2212', 'learning_rate': '0.0005138', 'epoch': '0.1478'}
 15%|█▌ | 610/4000 [42:18<3:55:32, 4.17s/it] 15%|█▌ | 611/4000 [42:22<3:54:46, 4.16s/it] 15%|█▌ | 612/4000 [42:26<3:54:12, 4.15s/it] 15%|█▌ | 613/4000 [42:31<4:00:03, 4.25s/it] 15%|█▌ | 614/4000 [42:35<3:57:45, 4.21s/it] 15%|█▌ | 615/4000 [42:39<3:56:20, 4.19s/it] 15%|█▌ | 616/4000 [42:43<3:55:56, 4.18s/it] 15%|█▌ | 617/4000 [42:47<3:55:57, 4.19s/it] 15%|█▌ | 618/4000 [42:52<3:55:24, 4.18s/it] 15%|█▌ | 619/4000 [42:56<3:55:12, 4.17s/it] 16%|█▌ | 620/4000 [43:00<4:02:11, 4.30s/it] {'loss': '0.7474', 'grad_norm': '0.1852', 'learning_rate': '0.0005123', 'epoch': '0.1502'}
 16%|█▌ | 620/4000 [43:00<4:02:11, 4.30s/it] 16%|█▌ | 621/4000 [43:05<4:00:32, 4.27s/it] 16%|█▌ | 622/4000 [43:09<3:58:00, 4.23s/it] 16%|█▌ | 623/4000 [43:13<3:56:20, 4.20s/it] 16%|█▌ | 624/4000 [43:17<3:55:32, 4.19s/it] 16%|█▌ | 625/4000 [43:21<3:54:36, 4.17s/it] 16%|█▌ | 626/4000 [43:25<3:53:57, 4.16s/it] 16%|█▌ | 627/4000 [43:30<3:59:30, 4.26s/it] 16%|█▌ | 628/4000 [43:34<3:57:18, 4.22s/it] 16%|█▌ | 629/4000 [43:38<3:55:49, 4.20s/it] 16%|█▌ | 630/4000 [43:42<3:54:42, 4.18s/it] {'loss': '0.7208', 'grad_norm': '0.1889', 'learning_rate': '0.0005108', 'epoch': '0.1526'}
 16%|█▌ | 630/4000 [43:42<3:54:42, 4.18s/it] 16%|█▌ | 631/4000 [43:46<3:53:49, 4.16s/it] 16%|█▌ | 632/4000 [43:50<3:53:17, 4.16s/it] 16%|█▌ | 633/4000 [43:55<3:58:31, 4.25s/it] 16%|█▌ | 634/4000 [43:59<3:56:38, 4.22s/it] 16%|█▌ | 635/4000 [44:03<3:55:25, 4.20s/it] 16%|█▌ | 636/4000 [44:07<3:54:30, 4.18s/it] 16%|█▌ | 637/4000 [44:11<3:53:39, 4.17s/it] 16%|█▌ | 638/4000 [44:16<3:53:14, 4.16s/it] 16%|█▌ | 639/4000 [44:20<3:54:44, 4.19s/it] 16%|█▌ | 640/4000 [44:24<4:01:01, 4.30s/it] {'loss': '0.7037', 'grad_norm': '0.1678', 'learning_rate': '0.0005092', 'epoch': '0.1551'}
 16%|█▌ | 640/4000 [44:24<4:01:01, 4.30s/it] 16%|█▌ | 641/4000 [44:29<3:59:05, 4.27s/it] 16%|█▌ | 642/4000 [44:33<3:57:50, 4.25s/it] 16%|█▌ | 643/4000 [44:37<3:57:47, 4.25s/it] 16%|█▌ | 644/4000 [44:41<3:55:53, 4.22s/it] 16%|█▌ | 645/4000 [44:45<3:54:35, 4.20s/it] 16%|█▌ | 646/4000 [44:49<3:53:34, 4.18s/it] 16%|█▌ | 647/4000 [44:54<3:59:05, 4.28s/it] 16%|█▌ | 648/4000 [44:58<3:56:55, 4.24s/it] 16%|█▌ | 649/4000 [45:02<3:55:02, 4.21s/it] 16%|█▋ | 650/4000 [45:06<3:53:48, 4.19s/it] {'loss': '0.7125', 'grad_norm': '0.2202', 'learning_rate': '0.0005077', 'epoch': '0.1575'}
 16%|█▋ | 650/4000 [45:06<3:53:48, 4.19s/it] 16%|█▋ | 651/4000 [45:11<3:52:52, 4.17s/it] 16%|█▋ | 652/4000 [45:15<3:52:16, 4.16s/it] 16%|█▋ | 653/4000 [45:19<3:52:24, 4.17s/it] 16%|█▋ | 654/4000 [45:23<3:57:47, 4.26s/it] 16%|█▋ | 655/4000 [45:27<3:55:34, 4.23s/it] 16%|█▋ | 656/4000 [45:32<3:54:18, 4.20s/it] 16%|█▋ | 657/4000 [45:36<3:52:54, 4.18s/it] 16%|█▋ | 658/4000 [45:40<3:51:52, 4.16s/it] 16%|█▋ | 659/4000 [45:44<3:51:32, 4.16s/it] 16%|█▋ | 660/4000 [45:49<3:57:32, 4.27s/it] {'loss': '0.7117', 'grad_norm': '0.1797', 'learning_rate': '0.0005062', 'epoch': '0.1599'}
 16%|█▋ | 660/4000 [45:49<3:57:32, 4.27s/it] 17%|█▋ | 661/4000 [45:53<3:55:14, 4.23s/it] 17%|█▋ | 662/4000 [45:57<3:53:35, 4.20s/it] 17%|█▋ | 663/4000 [46:01<3:52:37, 4.18s/it] 17%|█▋ | 664/4000 [46:05<3:53:21, 4.20s/it] 17%|█▋ | 665/4000 [46:09<3:53:34, 4.20s/it] 17%|█▋ | 666/4000 [46:14<3:53:06, 4.19s/it] 17%|█▋ | 667/4000 [46:18<3:58:25, 4.29s/it] 17%|█▋ | 668/4000 [46:22<3:58:32, 4.30s/it] 17%|█▋ | 669/4000 [46:27<3:57:14, 4.27s/it] 17%|█▋ | 670/4000 [46:31<3:54:46, 4.23s/it] {'loss': '0.7337', 'grad_norm': '0.1529', 'learning_rate': '0.0005047', 'epoch': '0.1623'}
 17%|█▋ | 670/4000 [46:31<3:54:46, 4.23s/it] 17%|█▋ | 671/4000 [46:35<3:52:57, 4.20s/it] 17%|█▋ | 672/4000 [46:39<3:51:51, 4.18s/it] 17%|█▋ | 673/4000 [46:43<3:50:42, 4.16s/it] 17%|█▋ | 674/4000 [46:48<3:56:20, 4.26s/it] 17%|█▋ | 675/4000 [46:52<3:53:51, 4.22s/it] 17%|█▋ | 676/4000 [46:56<3:52:30, 4.20s/it] 17%|█▋ | 677/4000 [47:00<3:51:13, 4.17s/it] 17%|█▋ | 678/4000 [47:04<3:50:25, 4.16s/it] 17%|█▋ | 679/4000 [47:08<3:50:06, 4.16s/it] 17%|█▋ | 680/4000 [47:13<3:52:58, 4.21s/it] {'loss': '0.724', 'grad_norm': '0.169', 'learning_rate': '0.0005032', 'epoch': '0.1648'}
 17%|█▋ | 680/4000 [47:13<3:52:58, 4.21s/it] 17%|█▋ | 681/4000 [47:17<3:55:07, 4.25s/it] 17%|█▋ | 682/4000 [47:21<3:53:55, 4.23s/it] 17%|█▋ | 683/4000 [47:25<3:53:19, 4.22s/it] 17%|█▋ | 684/4000 [47:29<3:51:50, 4.20s/it] 17%|█▋ | 685/4000 [47:34<3:50:30, 4.17s/it] 17%|█▋ | 686/4000 [47:38<3:49:53, 4.16s/it] 17%|█▋ | 687/4000 [47:42<3:54:54, 4.25s/it] 17%|█▋ | 688/4000 [47:46<3:53:15, 4.23s/it] 17%|█▋ | 689/4000 [47:51<3:52:18, 4.21s/it] 17%|█▋ | 690/4000 [47:55<3:51:00, 4.19s/it] {'loss': '0.7363', 'grad_norm': '0.2151', 'learning_rate': '0.0005017', 'epoch': '0.1672'}
 17%|█▋ | 690/4000 [47:55<3:51:00, 4.19s/it] 17%|█▋ | 691/4000 [47:59<3:50:13, 4.17s/it] 17%|█▋ | 692/4000 [48:03<3:49:31, 4.16s/it] 17%|█▋ | 693/4000 [48:07<3:49:16, 4.16s/it] 17%|█▋ | 694/4000 [48:12<3:55:14, 4.27s/it] 17%|█▋ | 695/4000 [48:16<3:53:00, 4.23s/it] 17%|█▋ | 696/4000 [48:20<3:51:10, 4.20s/it] 17%|█▋ | 697/4000 [48:24<3:50:06, 4.18s/it] 17%|█▋ | 698/4000 [48:28<3:49:22, 4.17s/it] 17%|█▋ | 699/4000 [48:32<3:48:59, 4.16s/it] 18%|█▊ | 700/4000 [48:36<3:48:29, 4.15s/it] {'loss': '0.7571', 'grad_norm': '0.1559', 'learning_rate': '0.0005002', 'epoch': '0.1696'}
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 18%|█▊ | 710/4000 [49:19<3:51:35, 4.22s/it] 18%|█▊ | 711/4000 [49:23<3:50:27, 4.20s/it] 18%|█▊ | 712/4000 [49:27<3:49:56, 4.20s/it] 18%|█▊ | 713/4000 [49:31<3:50:28, 4.21s/it] 18%|█▊ | 714/4000 [49:36<3:58:56, 4.36s/it] 18%|█▊ | 715/4000 [49:40<3:57:38, 4.34s/it] 18%|█▊ | 716/4000 [49:45<3:56:43, 4.32s/it] 18%|█▊ | 717/4000 [49:49<3:55:52, 4.31s/it] 18%|█▊ | 718/4000 [49:53<3:56:14, 4.32s/it] 18%|█▊ | 719/4000 [49:58<3:54:39, 4.29s/it] 18%|█▊ | 720/4000 [50:02<3:52:50, 4.26s/it] {'loss': '0.7251', 'grad_norm': '0.2271', 'learning_rate': '0.0004971', 'epoch': '0.1745'}
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{'eval_loss': '2.541', 'eval_runtime': '0.9973', 'eval_samples_per_second': '22.06', 'eval_steps_per_second': '2.005', 'epoch': '0.4846'}
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 [transformers] LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
 - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
 - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
 - If you are not the owner of the model architecture class, please contact the model code owner to update it.
Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]
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{'eval_loss': '2.231', 'eval_runtime': '1.011', 'eval_samples_per_second': '21.76', 'eval_steps_per_second': '1.978', 'epoch': '0.9692'}
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 [transformers] LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
 - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
 - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
 - If you are not the owner of the model architecture class, please contact the model code owner to update it.
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 {'train_runtime': '1.691e+04', 'train_samples_per_second': '7.569', 'train_steps_per_second': '0.237', 'train_loss': '0.6708', 'epoch': '0.9692'}
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[transformers] LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
 - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
 - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
 - If you are not the owner of the model architecture class, please contact the model code owner to update it.
 Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]
[Done] Model saved to outputs/devops-300m-4096-bf16-vast
 Writing model shards: 100%|██████████| 1/1 [00:00<00:00, 3.80it/s] Writing model shards: 100%|██████████| 1/1 [00:00<00:00, 3.80it/s]
[Done] Model saved to outputs/devops-300m-4096-bf16-vast
81d666a3c511:10693:10966 [1] NCCL INFO [Service thread] Connection closed by localRank 1
[rank0]:[W914 21:26:49.450832496 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present, but this warning has only been added since PyTorch 2.4 (function operator())
81d666a3c511:10692:10965 [0] NCCL INFO [Service thread] Connection closed by localRank 0
81d666a3c511:10693:15114 [1] NCCL INFO comm 0x1aa33b30 rank 1 nranks 2 cudaDev 1 busId d6000 - Abort COMPLETE
81d666a3c511:10692:15115 [0] NCCL INFO comm 0x1adc3b90 rank 0 nranks 2 cudaDev 0 busId d2000 - Abort COMPLETE
[2026-09-14 21:26:51,207] [INFO] [launch.py:367:main] Process 10692 exits successfully.
[2026-09-14 21:26:51,208] [INFO] [launch.py:367:main] Process 10693 exits successfully.
=== full run done Mon Sep 14 21:26:53 UTC 2026 exit=0 ===