slurm submission log: 2024-05-11 17:54:08.580940
created following sbatch script: 

###############################

#!/bin/bash

#SBATCH --account=nlp
#SBATCH --cpus-per-task=16
#SBATCH --dependency=afterok:
#SBATCH --gres=gpu:2
#SBATCH --job-name=tthrush-job-4732981
#SBATCH --mem=400G
#SBATCH --nodelist=sphinx2
#SBATCH --open-mode=append
#SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq/train_job_output.txt
#SBATCH --partition=sphinx
#SBATCH --time=14-0

# activate your desired anaconda environment
. /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection

# cd to working directory
cd .

# launch commands
srun --unbuffered run_as_child_processes 'torchrun --master_port 29504 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq --output_hub_id pythia-70m_sciq --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'

###############################

submission to slurm complete!


###############################
slurm submission output



sbatch: error: Batch job submission failed: Job dependency problem

###############################

slurm submission log: 2024-05-11 17:55:06.870798
created following sbatch script: 

###############################

#!/bin/bash

#SBATCH --account=nlp
#SBATCH --cpus-per-task=16
#SBATCH --dependency=afterok:7598872
#SBATCH --gres=gpu:2
#SBATCH --job-name=tthrush-job-4842885
#SBATCH --mem=400G
#SBATCH --nodelist=sphinx2
#SBATCH --open-mode=append
#SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq/train_job_output.txt
#SBATCH --partition=sphinx
#SBATCH --time=14-0

# activate your desired anaconda environment
. /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection

# cd to working directory
cd .

# launch commands
srun --unbuffered run_as_child_processes 'torchrun --master_port 29504 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq --output_hub_id pythia-70m_sciq --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'

###############################

submission to slurm complete!


###############################
slurm submission output

Submitted batch job 7598873



###############################

###############################
start time: 2024-05-11 17:57:32.246511
machine: sphinx2
conda env: pretraining-coreset-selection
###############################
running following processes

	torchrun --master_port 29504 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq --output_hub_id pythia-70m_sciq --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2


###############################
command outputs: 


[2024-05-11 17:57:34,935] torch.distributed.run: [WARNING] 
[2024-05-11 17:57:34,935] torch.distributed.run: [WARNING] *****************************************
[2024-05-11 17:57:34,935] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
[2024-05-11 17:57:34,935] torch.distributed.run: [WARNING] *****************************************
05/11/2024 17:57:45 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq', output_hub_id='pythia-70m_sciq', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)
05/11/2024 17:57:45 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq', output_hub_id='pythia-70m_sciq', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)
Traceback (most recent call last):
  File "/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_llm.py", line 202, in <module>
    train_model()
  File "/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_llm.py", line 162, in train_model
    train_dataset = load_from_disk(script_args.dataset_id)
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/datasets/load.py", line 2638, in load_from_disk
    raise FileNotFoundError(
FileNotFoundError: Directory /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq is neither a `Dataset` directory nor a `DatasetDict` directory.
Traceback (most recent call last):
  File "/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_llm.py", line 202, in <module>
    train_model()
  File "/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_llm.py", line 162, in train_model
    train_dataset = load_from_disk(script_args.dataset_id)
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/datasets/load.py", line 2638, in load_from_disk
    raise FileNotFoundError(
FileNotFoundError: Directory /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq is neither a `Dataset` directory nor a `DatasetDict` directory.
[2024-05-11 17:57:49,960] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 3600439) of binary: /nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/bin/python
Traceback (most recent call last):
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/torch/distributed/run.py", line 812, in main
    run(args)
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/nlp/scr/tthrush/miniconda3/envs/pretraining-coreset-selection/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 268, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
train_llm.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-05-11_17:57:49
  host      : sphinx2.stanford.edu
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 3600440)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-05-11_17:57:49
  host      : sphinx2.stanford.edu
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 3600439)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
###############################
end time: 2024-05-11 17:57:52.272138
elapsed time: 0:00:20.025627
slurm submission log: 2024-05-11 18:01:39.581982
created following sbatch script: 

###############################

#!/bin/bash

#SBATCH --account=nlp
#SBATCH --cpus-per-task=16
#SBATCH --dependency=afterok:7598911
#SBATCH --gres=gpu:2
#SBATCH --job-name=tthrush-job-4293909
#SBATCH --mem=400G
#SBATCH --nodelist=sphinx2
#SBATCH --open-mode=append
#SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq/train_job_output.txt
#SBATCH --partition=sphinx
#SBATCH --time=14-0

# activate your desired anaconda environment
. /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection

# cd to working directory
cd .

# launch commands
srun --unbuffered run_as_child_processes 'torchrun --master_port 29504 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq --output_hub_id pythia-70m_sciq --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'

###############################

submission to slurm complete!


###############################
slurm submission output

Submitted batch job 7598912



###############################

###############################
start time: 2024-05-11 20:42:46.833506
machine: sphinx2
conda env: pretraining-coreset-selection
###############################
running following processes

	torchrun --master_port 29504 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq --output_hub_id pythia-70m_sciq --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2


###############################
command outputs: 


[2024-05-11 20:42:48,810] torch.distributed.run: [WARNING] 
[2024-05-11 20:42:48,810] torch.distributed.run: [WARNING] *****************************************
[2024-05-11 20:42:48,810] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
[2024-05-11 20:42:48,810] torch.distributed.run: [WARNING] *****************************************
05/11/2024 20:42:55 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq', output_hub_id='pythia-70m_sciq', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)
05/11/2024 20:42:55 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/sciq', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_sciq', output_hub_id='pythia-70m_sciq', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)

  0%|          | 0/10682 [00:00<?, ?it/s][rank1]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[rank0]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())

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{'loss': 10.6527, 'grad_norm': 1.4306793212890625, 'learning_rate': 2.3386342376052384e-05, 'epoch': 0.03}

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  0%|          | 50/10682 [00:26<1:27:34,  2.02it/s]{'loss': 9.9332, 'grad_norm': 1.3259479999542236, 'learning_rate': 4.677268475210477e-05, 'epoch': 0.07}
                                                    

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  1%|          | 75/10682 [00:38<1:27:18,  2.02it/s]{'loss': 9.2234, 'grad_norm': 1.1572329998016357, 'learning_rate': 7.015902712815715e-05, 'epoch': 0.1}
                                                    

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{'loss': 7.7787, 'grad_norm': 0.4531400799751282, 'learning_rate': 0.00011693171188026193, 'epoch': 0.16}

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{'loss': 7.3082, 'grad_norm': 0.4752507507801056, 'learning_rate': 0.0001403180542563143, 'epoch': 0.2}

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{'loss': 6.9137, 'grad_norm': 0.5296725034713745, 'learning_rate': 0.00016370439663236668, 'epoch': 0.23}

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{'loss': 6.3124, 'grad_norm': 0.5124927759170532, 'learning_rate': 0.00021047708138447147, 'epoch': 0.29}

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                                                     {'loss': 5.9284, 'grad_norm': 1.1340378522872925, 'learning_rate': 0.00025724976613657625, 'epoch': 0.36}

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                                                     {'loss': 5.7957, 'grad_norm': 0.8028761744499207, 'learning_rate': 0.0002806361085126286, 'epoch': 0.39}

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                                                     {'loss': 5.6629, 'grad_norm': 0.5284114480018616, 'learning_rate': 0.00030402245088868103, 'epoch': 0.43}

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{'loss': 5.3617, 'grad_norm': 0.6408572793006897, 'learning_rate': 0.00037418147801683815, 'epoch': 0.52}

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                                                     {'loss': 5.2839, 'grad_norm': 0.6423079371452332, 'learning_rate': 0.0003975678203928906, 'epoch': 0.56}

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                                                     {'loss': 5.1951, 'grad_norm': 0.9615857005119324, 'learning_rate': 0.00042095416276894293, 'epoch': 0.59}

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                                                     {'loss': 5.1186, 'grad_norm': 0.9014081954956055, 'learning_rate': 0.0004443405051449954, 'epoch': 0.62}

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{'loss': 4.9916, 'grad_norm': 0.581803023815155, 'learning_rate': 0.0004911131898971, 'epoch': 0.69}

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                                                     {'loss': 4.9264, 'grad_norm': 0.9201842546463013, 'learning_rate': 0.0005144995322731525, 'epoch': 0.72}

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{'loss': 4.7288, 'grad_norm': 0.49271953105926514, 'learning_rate': 0.0006080449017773621, 'epoch': 0.85}

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{'loss': 4.4397, 'grad_norm': 0.4103018641471863, 'learning_rate': 0.0007717492984097287, 'epoch': 1.08}

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  8%|▊         | 850/10682 [07:13<1:20:44,  2.03it/s]{'loss': 4.426, 'grad_norm': 0.45403552055358887, 'learning_rate': 0.0007951356407857812, 'epoch': 1.11}

                                                     

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  8%|▊         | 875/10682 [07:26<1:20:30,  2.03it/s]{'loss': 4.3986, 'grad_norm': 0.37543046474456787, 'learning_rate': 0.0008185219831618334, 'epoch': 1.15}

                                                     

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                                                     {'loss': 4.3828, 'grad_norm': 0.4127384424209595, 'learning_rate': 0.0008419083255378859, 'epoch': 1.18}

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                                                     {'loss': 4.3254, 'grad_norm': 0.39499613642692566, 'learning_rate': 0.0008886810102899908, 'epoch': 1.25}

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  9%|▉         | 975/10682 [08:15<1:19:46,  2.03it/s]{'loss': 4.3115, 'grad_norm': 0.36431053280830383, 'learning_rate': 0.000912067352666043, 'epoch': 1.28}

                                                     

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{'loss': 4.2558, 'grad_norm': 0.3449212312698364, 'learning_rate': 0.0009822263797942, 'epoch': 1.38}

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                                                      {'loss': 4.1496, 'grad_norm': 0.33627545833587646, 'learning_rate': 0.0009997000214725905, 'epoch': 1.54}


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{'loss': 4.1235, 'grad_norm': 0.3130369186401367, 'learning_rate': 0.000999541860089067, 'epoch': 1.57}

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{'loss': 4.0804, 'grad_norm': 0.3049085736274719, 'learning_rate': 0.0009988673590077238, 'epoch': 1.67}

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{'loss': 4.0429, 'grad_norm': 0.2962116003036499, 'learning_rate': 0.0009985759030302366, 'epoch': 1.7}

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                                                      {'loss': 4.0154, 'grad_norm': 0.2766225039958954, 'learning_rate': 0.0009978931710439229, 'epoch': 1.77}


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                                                      {'loss': 3.9625, 'grad_norm': 0.2846989631652832, 'learning_rate': 0.0009961291115580116, 'epoch': 1.9}

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                                                      {'loss': 3.9289, 'grad_norm': 0.25892743468284607, 'learning_rate': 0.0009950482550633215, 'epoch': 1.97}

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                                                      {'loss': 3.7656, 'grad_norm': 0.2522944211959839, 'learning_rate': 0.0009867503683768772, 'epoch': 2.33}

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                                                      {'loss': 3.7684, 'grad_norm': 0.2673543095588684, 'learning_rate': 0.0009857999404263656, 'epoch': 2.36}

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{'loss': 3.7589, 'grad_norm': 0.25726452469825745, 'learning_rate': 0.0009848170847181403, 'epoch': 2.39}

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                                                      {'loss': 3.7408, 'grad_norm': 0.26593708992004395, 'learning_rate': 0.000982754354616187, 'epoch': 2.46}

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 18%|█▊        | 1900/10682 [16:03<1:13:09,  2.00it/s]{'loss': 3.745, 'grad_norm': 0.2467692494392395, 'learning_rate': 0.0009816746179122904, 'epoch': 2.49}
                                                      

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                                                      {'loss': 3.7387, 'grad_norm': 0.2575371265411377, 'learning_rate': 0.0009805627288211524, 'epoch': 2.52}

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                                                      {'loss': 3.7219, 'grad_norm': 0.25070905685424805, 'learning_rate': 0.0009782427924983968, 'epoch': 2.59}

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{'loss': 3.7094, 'grad_norm': 0.2467462122440338, 'learning_rate': 0.0009732205000607044, 'epoch': 2.72}

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                                                      {'loss': 3.7, 'grad_norm': 0.24760499596595764, 'learning_rate': 0.000971885741964458, 'epoch': 2.75}


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{'loss': 3.7026, 'grad_norm': 0.25650879740715027, 'learning_rate': 0.0009705194849007652, 'epoch': 2.79}

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{'loss': 3.6851, 'grad_norm': 0.24378468096256256, 'learning_rate': 0.0009676928407650656, 'epoch': 2.85}

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                                                      {'loss': 3.6705, 'grad_norm': 0.23903612792491913, 'learning_rate': 0.0009616657177703876, 'epoch': 2.98}

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                                                      {'loss': 3.5971, 'grad_norm': 0.25288769602775574, 'learning_rate': 0.0009600816384779067, 'epoch': 3.01}

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{'loss': 3.5114, 'grad_norm': 0.2353508025407791, 'learning_rate': 0.00095846684815675, 'epoch': 3.05}

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{'loss': 3.5301, 'grad_norm': 0.2549166679382324, 'learning_rate': 0.0009568214545962046, 'epoch': 3.08}

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                                                      {'loss': 3.5437, 'grad_norm': 0.2482254058122635, 'learning_rate': 0.0009551455676283627, 'epoch': 3.11}

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                                                      {'loss': 3.5442, 'grad_norm': 0.26593077182769775, 'learning_rate': 0.0009517027629690559, 'epoch': 3.18}

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{'loss': 3.5353, 'grad_norm': 0.25152483582496643, 'learning_rate': 0.0009499360750891371, 'epoch': 3.21}

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{'loss': 3.5261, 'grad_norm': 0.25539252161979675, 'learning_rate': 0.0009387095054051274, 'epoch': 3.41}

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                                                      {'loss': 3.5358, 'grad_norm': 0.2848701775074005, 'learning_rate': 0.0009367351691304569, 'epoch': 3.44}

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{'loss': 3.5381, 'grad_norm': 0.23514452576637268, 'learning_rate': 0.0009306377814504165, 'epoch': 3.54}

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                                                      {'loss': 3.5138, 'grad_norm': 0.23322314023971558, 'learning_rate': 0.0009037001869885992, 'epoch': 3.93}


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{'loss': 3.5014, 'grad_norm': 0.24653689563274384, 'learning_rate': 0.0009012765247103541, 'epoch': 3.96}

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{'loss': 3.3606, 'grad_norm': 0.260498046875, 'learning_rate': 0.0008938454796549082, 'epoch': 4.06}

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{'loss': 3.4046, 'grad_norm': 0.23265957832336426, 'learning_rate': 0.0008755942581046657, 'epoch': 4.29}

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{'loss': 3.3986, 'grad_norm': 0.24089010059833527, 'learning_rate': 0.0008673927112327234, 'epoch': 4.39}

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{'loss': 3.4085, 'grad_norm': 0.23608741164207458, 'learning_rate': 0.0008589704588480251, 'epoch': 4.49}

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                                                    {'loss': 3.3989, 'grad_norm': 0.24647799134254456, 'learning_rate': 0.0008561148664436948, 'epoch': 4.52}


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                                                    {'loss': 3.3925, 'grad_norm': 0.23784807324409485, 'learning_rate': 0.0008532355029236307, 'epoch': 4.55}

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{'loss': 3.3965, 'grad_norm': 0.27305951714515686, 'learning_rate': 0.0008503325604889736, 'epoch': 4.59}

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                                                    {'loss': 3.3932, 'grad_norm': 0.2502672076225281, 'learning_rate': 0.0008474062329147856, 'epoch': 4.62}


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{'loss': 3.4028, 'grad_norm': 0.23103992640972137, 'learning_rate': 0.0008444567155371176, 'epoch': 4.65}

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                                                    {'loss': 3.4034, 'grad_norm': 0.24344360828399658, 'learning_rate': 0.0008384889004421422, 'epoch': 4.72}


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                                                    {'loss': 3.4041, 'grad_norm': 0.24148575961589813, 'learning_rate': 0.0008354710010840066, 'epoch': 4.75}

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{'loss': 3.4081, 'grad_norm': 0.24292631447315216, 'learning_rate': 0.0008324307086141438, 'epoch': 4.78}

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{'loss': 3.4003, 'grad_norm': 0.23460906744003296, 'learning_rate': 0.000829368225975904, 'epoch': 4.82}

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{'loss': 3.3972, 'grad_norm': 0.26145219802856445, 'learning_rate': 0.0008231775093601584, 'epoch': 4.88}

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{'loss': 3.3979, 'grad_norm': 0.24352329969406128, 'learning_rate': 0.0008200496886207818, 'epoch': 4.91}

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{'loss': 3.4022, 'grad_norm': 0.2295740842819214, 'learning_rate': 0.0008169005041617021, 'epoch': 4.95}

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                                                      {'loss': 3.3275, 'grad_norm': 0.25923624634742737, 'learning_rate': 0.0008105388863445693, 'epoch': 5.01}

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                                                    {'loss': 3.2405, 'grad_norm': 0.24252888560295105, 'learning_rate': 0.0008040943544644116, 'epoch': 5.08}

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                                                    {'loss': 3.2768, 'grad_norm': 0.2412186861038208, 'learning_rate': 0.0007876316210265521, 'epoch': 5.24}

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{'loss': 3.2889, 'grad_norm': 0.24944435060024261, 'learning_rate': 0.0007842805893115124, 'epoch': 5.28}

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                                                    {'loss': 3.2787, 'grad_norm': 0.24943660199642181, 'learning_rate': 0.0007809105815091867, 'epoch': 5.31}

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{'loss': 3.2907, 'grad_norm': 0.24995286762714386, 'learning_rate': 0.0007775218225718394, 'epoch': 5.34}

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                                                    {'loss': 3.2992, 'grad_norm': 0.24979282915592194, 'learning_rate': 0.0007741145387033967, 'epoch': 5.37}

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                                                    {'loss': 3.2924, 'grad_norm': 0.23800574243068695, 'learning_rate': 0.0007672453071565586, 'epoch': 5.44}

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{'loss': 3.2938, 'grad_norm': 0.2427065223455429, 'learning_rate': 0.0007532946332467235, 'epoch': 5.57}

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{'loss': 3.3031, 'grad_norm': 0.24355722963809967, 'learning_rate': 0.0007462169157217631, 'epoch': 5.64}

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                                                    {'loss': 3.3032, 'grad_norm': 0.2573416531085968, 'learning_rate': 0.0007426532856357045, 'epoch': 5.67}

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                                                    {'loss': 3.3042, 'grad_norm': 0.239610955119133, 'learning_rate': 0.0007390734581372948, 'epoch': 5.7}

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{'loss': 3.2987, 'grad_norm': 0.25175485014915466, 'learning_rate': 0.0007354776721845277, 'epoch': 5.73}

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                                                    {'loss': 3.3055, 'grad_norm': 0.23743318021297455, 'learning_rate': 0.0007318661678006427, 'epoch': 5.77}

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                                                    {'loss': 3.3013, 'grad_norm': 0.2519538402557373, 'learning_rate': 0.0007209397599364363, 'epoch': 5.87}


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{'loss': 3.3005, 'grad_norm': 0.2393822818994522, 'learning_rate': 0.0007172678028031901, 'epoch': 5.9}

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                                                    {'loss': 3.3092, 'grad_norm': 0.23008811473846436, 'learning_rate': 0.0007135813427705359, 'epoch': 5.93}

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{'loss': 3.1549, 'grad_norm': 0.24603572487831116, 'learning_rate': 0.0007024374107764318, 'epoch': 6.03}

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{'loss': 3.1388, 'grad_norm': 0.24891839921474457, 'learning_rate': 0.0006986954093387361, 'epoch': 6.06}

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{'loss': 3.1585, 'grad_norm': 0.2431744486093521, 'learning_rate': 0.0006911718676257091, 'epoch': 6.13}

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{'loss': 3.1811, 'grad_norm': 0.23464258015155792, 'learning_rate': 0.0006835972829129873, 'epoch': 6.19}

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                                                    {'loss': 3.1853, 'grad_norm': 0.24362727999687195, 'learning_rate': 0.000679791480919905, 'epoch': 6.23}


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                                                    {'loss': 3.1925, 'grad_norm': 0.25723543763160706, 'learning_rate': 0.0006721441278537161, 'epoch': 6.29}


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{'loss': 3.1937, 'grad_norm': 0.241689994931221, 'learning_rate': 0.0006644508122054031, 'epoch': 6.36}

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                                                    {'loss': 3.1867, 'grad_norm': 0.24817608296871185, 'learning_rate': 0.0006605875598609699, 'epoch': 6.39}

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{'loss': 3.1942, 'grad_norm': 0.24596330523490906, 'learning_rate': 0.0006567135880947134, 'epoch': 6.42}

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{'loss': 3.1984, 'grad_norm': 0.24129435420036316, 'learning_rate': 0.0006528291554991272, 'epoch': 6.45}

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{'loss': 3.2103, 'grad_norm': 0.24021977186203003, 'learning_rate': 0.0006489345213649782, 'epoch': 6.49}

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{'loss': 3.2103, 'grad_norm': 0.24559763073921204, 'learning_rate': 0.0006332591787271516, 'epoch': 6.62}

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{'loss': 3.2197, 'grad_norm': 0.23890674114227295, 'learning_rate': 0.0006214083583911186, 'epoch': 6.72}

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                                                    {'loss': 3.2198, 'grad_norm': 0.24115127325057983, 'learning_rate': 0.0006174415244767953, 'epoch': 6.75}

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                                                    {'loss': 3.2173, 'grad_norm': 0.24306371808052063, 'learning_rate': 0.0006134668511928376, 'epoch': 6.78}

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{'loss': 3.2152, 'grad_norm': 0.23045431077480316, 'learning_rate': 0.000605495048279383, 'epoch': 6.85}

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{'loss': 3.2185, 'grad_norm': 0.2398773431777954, 'learning_rate': 0.000597495078126763, 'epoch': 6.91}

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{'loss': 3.2141, 'grad_norm': 0.2376185953617096, 'learning_rate': 0.000593485197556533, 'epoch': 6.95}

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{'loss': 3.2172, 'grad_norm': 0.24983642995357513, 'learning_rate': 0.0005894690767316552, 'epoch': 6.98}

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                                                      {'loss': 3.1432, 'grad_norm': 0.27883490920066833, 'learning_rate': 0.0005854469837332512, 'epoch': 7.01}

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{'loss': 3.053, 'grad_norm': 0.25089290738105774, 'learning_rate': 0.0005814191870410933, 'epoch': 7.04}

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                                                    {'loss': 3.0648, 'grad_norm': 0.26502561569213867, 'learning_rate': 0.0005773859555156823, 'epoch': 7.08}

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                                                    {'loss': 3.0692, 'grad_norm': 0.2463548481464386, 'learning_rate': 0.0005733475583803009, 'epoch': 7.11}

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{'loss': 3.0821, 'grad_norm': 0.248189315199852, 'learning_rate': 0.0005693042652030427, 'epoch': 7.14}

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                                                    {'loss': 3.0938, 'grad_norm': 0.24429070949554443, 'learning_rate': 0.0005571477098950832, 'epoch': 7.24}


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{'loss': 3.0954, 'grad_norm': 0.24610450863838196, 'learning_rate': 0.0005530875344972343, 'epoch': 7.27}

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                                                    {'loss': 3.1062, 'grad_norm': 0.26181212067604065, 'learning_rate': 0.0005490238154396129, 'epoch': 7.31}

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{'loss': 3.1089, 'grad_norm': 0.2551175355911255, 'learning_rate': 0.0005449568239805831, 'epoch': 7.34}

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{'loss': 3.1188, 'grad_norm': 0.244360089302063, 'learning_rate': 0.0005408868315969458, 'epoch': 7.37}

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                                                    {'loss': 3.1117, 'grad_norm': 0.24802280962467194, 'learning_rate': 0.0005368141099658168, 'epoch': 7.4}


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{'loss': 3.1181, 'grad_norm': 0.24299483001232147, 'learning_rate': 0.0005327389309464938, 'epoch': 7.44}

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                                                    {'loss': 3.1215, 'grad_norm': 0.24089054763317108, 'learning_rate': 0.0005286615665623071, 'epoch': 7.47}

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{'loss': 3.1099, 'grad_norm': 0.2619318664073944, 'learning_rate': 0.0005245822889824633, 'epoch': 7.5}

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{'loss': 3.1183, 'grad_norm': 0.23445558547973633, 'learning_rate': 0.0005082514941786895, 'epoch': 7.63}

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                                                    {'loss': 3.1344, 'grad_norm': 0.24043409526348114, 'learning_rate': 0.0005000817016706441, 'epoch': 7.7}


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                                                    {'loss': 3.1211, 'grad_norm': 0.2543013393878937, 'learning_rate': 0.0004959966608954618, 'epoch': 7.73}

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{'loss': 3.1321, 'grad_norm': 0.24270465970039368, 'learning_rate': 0.0004919118873482054, 'epoch': 7.77}

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                                                    {'loss': 3.126, 'grad_norm': 0.2590714693069458, 'learning_rate': 0.0004878276536926525, 'epoch': 7.8}

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{'loss': 3.1366, 'grad_norm': 0.24149152636528015, 'learning_rate': 0.0004837442325565422, 'epoch': 7.83}

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{'loss': 3.1406, 'grad_norm': 0.23931889235973358, 'learning_rate': 0.0004674241234809751, 'epoch': 7.96}

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{'loss': 2.9947, 'grad_norm': 0.2588682770729065, 'learning_rate': 0.00045927602667567333, 'epoch': 8.03}

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{'loss': 2.9976, 'grad_norm': 0.25518253445625305, 'learning_rate': 0.00045113880319490264, 'epoch': 8.09}

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{'loss': 3.0101, 'grad_norm': 0.25847306847572327, 'learning_rate': 0.00044301462568205915, 'epoch': 8.16}

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                                                    {'loss': 3.0211, 'grad_norm': 0.26151400804519653, 'learning_rate': 0.0004389581071780553, 'epoch': 8.19}

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{'loss': 3.0334, 'grad_norm': 0.2600579857826233, 'learning_rate': 0.00043085756454539745, 'epoch': 8.26}

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{'loss': 3.0379, 'grad_norm': 0.2540959119796753, 'learning_rate': 0.0004187420396619156, 'epoch': 8.36}

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{'loss': 3.0298, 'grad_norm': 0.25194263458251953, 'learning_rate': 0.00041471402041183287, 'epoch': 8.39}

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                                                    {'loss': 3.049, 'grad_norm': 0.25119808316230774, 'learning_rate': 0.00040266519392417936, 'epoch': 8.49}

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{'loss': 3.0486, 'grad_norm': 0.24930843710899353, 'learning_rate': 0.0003906748398221429, 'epoch': 8.58}

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                                                    {'loss': 3.0381, 'grad_norm': 0.24609309434890747, 'learning_rate': 0.00038669229504654146, 'epoch': 8.62}

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                                                    {'loss': 3.0415, 'grad_norm': 0.24925759434700012, 'learning_rate': 0.00038271731370291583, 'epoch': 8.65}

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{'loss': 3.0483, 'grad_norm': 0.2516178488731384, 'learning_rate': 0.0003747911021290519, 'epoch': 8.72}

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{'loss': 3.0464, 'grad_norm': 0.2652481198310852, 'learning_rate': 0.0003708404009834161, 'epoch': 8.75}

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                                                    {'loss': 3.0566, 'grad_norm': 0.2590247094631195, 'learning_rate': 0.00035904107890373196, 'epoch': 8.85}

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                                                    {'loss': 3.0578, 'grad_norm': 0.24666965007781982, 'learning_rate': 0.00034732643572487243, 'epoch': 8.94}

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                                                    {'loss': 2.999, 'grad_norm': 0.30739590525627136, 'learning_rate': 0.0003395671949156626, 'epoch': 9.01}

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{'loss': 2.9131, 'grad_norm': 0.2525786757469177, 'learning_rate': 0.0003318507897585944, 'epoch': 9.08}

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{'loss': 2.9266, 'grad_norm': 0.25107404589653015, 'learning_rate': 0.0003280092948503962, 'epoch': 9.11}

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                                                    {'loss': 2.92, 'grad_norm': 0.2568441331386566, 'learning_rate': 0.00032417928053832723, 'epoch': 9.14}

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{'loss': 2.9344, 'grad_norm': 0.25788554549217224, 'learning_rate': 0.0003203610024806651, 'epoch': 9.17}

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{'loss': 2.9632, 'grad_norm': 0.25865888595581055, 'learning_rate': 0.0003014545482582672, 'epoch': 9.34}

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                                                      {'loss': 2.9738, 'grad_norm': 0.26462554931640625, 'learning_rate': 0.0002902676958110948, 'epoch': 9.44}

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                                                      {'loss': 2.9649, 'grad_norm': 0.26582255959510803, 'learning_rate': 0.0002828793787771929, 'epoch': 9.5}

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                                                      {'loss': 2.9734, 'grad_norm': 0.253818541765213, 'learning_rate': 0.0002792068368154043, 'epoch': 9.53}

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{'loss': 2.9805, 'grad_norm': 0.2538394033908844, 'learning_rate': 0.0002755490330752679, 'epoch': 9.57}

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{'loss': 2.9747, 'grad_norm': 0.25463616847991943, 'learning_rate': 0.0002719062117197888, 'epoch': 9.6}

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{'loss': 2.9865, 'grad_norm': 0.25614771246910095, 'learning_rate': 0.0002610700684926339, 'epoch': 9.7}

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{'loss': 2.9838, 'grad_norm': 0.2547972798347473, 'learning_rate': 0.00025748959806091423, 'epoch': 9.73}

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                                                      {'loss': 2.976, 'grad_norm': 0.2610939145088196, 'learning_rate': 0.0002503774587418758, 'epoch': 9.8}


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{'loss': 2.9911, 'grad_norm': 0.256744921207428, 'learning_rate': 0.00024684626459881156, 'epoch': 9.83}

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{'loss': 2.979, 'grad_norm': 0.2532515823841095, 'learning_rate': 0.00023289281143540065, 'epoch': 9.96}

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                                                      {'loss': 2.8875, 'grad_norm': 0.2560681402683258, 'learning_rate': 0.00022602213475715589, 'epoch': 10.03}

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                                                      {'loss': 2.8625, 'grad_norm': 0.2625412940979004, 'learning_rate': 0.0002226141146140523, 'epoch': 10.06}

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                                                      {'loss': 2.8695, 'grad_norm': 0.2630217969417572, 'learning_rate': 0.00021922461032806601, 'epoch': 10.09}

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                                                      {'loss': 2.873, 'grad_norm': 0.26046666502952576, 'learning_rate': 0.00021250205309110155, 'epoch': 10.16}


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{'loss': 2.8781, 'grad_norm': 0.2634769082069397, 'learning_rate': 0.00020916944887928359, 'epoch': 10.19}

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{'loss': 2.8845, 'grad_norm': 0.2583664357662201, 'learning_rate': 0.00020585625797294927, 'epoch': 10.22}

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{'loss': 2.885, 'grad_norm': 0.2593543529510498, 'learning_rate': 0.00020256270153176371, 'epoch': 10.26}

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{'loss': 2.8957, 'grad_norm': 0.260572224855423, 'learning_rate': 0.00019928899940476624, 'epoch': 10.29}

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{'loss': 2.9034, 'grad_norm': 0.2561388909816742, 'learning_rate': 0.00018322590461360383, 'epoch': 10.45}

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{'loss': 2.9064, 'grad_norm': 0.2652903199195862, 'learning_rate': 0.00018007586996870206, 'epoch': 10.48}

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{'loss': 2.9123, 'grad_norm': 0.2658577561378479, 'learning_rate': 0.00017694719066230924, 'epoch': 10.52}

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{'loss': 2.9084, 'grad_norm': 0.26115337014198303, 'learning_rate': 0.00016769136599599017, 'epoch': 10.62}

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                                                      {'loss': 2.9057, 'grad_norm': 0.2715165615081787, 'learning_rate': 0.0001646501820129766, 'epoch': 10.65}

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                                                      {'loss': 2.9045, 'grad_norm': 0.26515233516693115, 'learning_rate': 0.00015863517062339038, 'epoch': 10.71}


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                                                      {'loss': 2.9162, 'grad_norm': 0.2606489360332489, 'learning_rate': 0.00015566174472640188, 'epoch': 10.75}

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{'loss': 2.9201, 'grad_norm': 0.2564961612224579, 'learning_rate': 0.00014688016334324605, 'epoch': 10.85}

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                                                      {'loss': 2.9036, 'grad_norm': 0.2633265256881714, 'learning_rate': 0.00014399985296581835, 'epoch': 10.88}

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                                                      {'loss': 2.9149, 'grad_norm': 0.2627502977848053, 'learning_rate': 0.00014114330604645943, 'epoch': 10.91}


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 78%|███████▊  | 8375/10682 [1:10:58<18:57,  2.03it/s]{'loss': 2.9219, 'grad_norm': 0.2577957808971405, 'learning_rate': 0.0001355022636953933, 'epoch': 10.98}
                                                      

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{'loss': 2.8919, 'grad_norm': 0.2671862244606018, 'learning_rate': 0.0001327181448103661, 'epoch': 11.01}

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{'loss': 2.8204, 'grad_norm': 0.26574021577835083, 'learning_rate': 0.00012995854245163207, 'epoch': 11.04}

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{'loss': 2.8127, 'grad_norm': 0.2653515040874481, 'learning_rate': 0.0001272236408261237, 'epoch': 11.07}

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                                                      {'loss': 2.819, 'grad_norm': 0.2613165080547333, 'learning_rate': 0.00012451362249196797, 'epoch': 11.11}

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{'loss': 2.8368, 'grad_norm': 0.2642626464366913, 'learning_rate': 0.00012182866834630096, 'epoch': 11.14}

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{'loss': 2.8262, 'grad_norm': 0.2625553011894226, 'learning_rate': 0.00011916895761319264, 'epoch': 11.17}

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{'loss': 2.8389, 'grad_norm': 0.26654481887817383, 'learning_rate': 0.00011134305278348312, 'epoch': 11.27}

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{'loss': 2.8347, 'grad_norm': 0.2613672912120819, 'learning_rate': 0.00010878607406363367, 'epoch': 11.3}

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                                                      {'loss': 2.8442, 'grad_norm': 0.2641572654247284, 'learning_rate': 0.00010625520936593375, 'epoch': 11.34}

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{'loss': 2.8407, 'grad_norm': 0.2638087272644043, 'learning_rate': 8.928431747505355e-05, 'epoch': 11.57}

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                                                      {'loss': 2.8535, 'grad_norm': 0.2602517902851105, 'learning_rate': 8.467984886028967e-05, 'epoch': 11.63}

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{'loss': 2.8617, 'grad_norm': 0.26445522904396057, 'learning_rate': 7.58047835834249e-05, 'epoch': 11.76}

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                                                      {'loss': 2.8481, 'grad_norm': 0.2617539167404175, 'learning_rate': 7.365644059145782e-05, 'epoch': 11.8}


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                                                      {'loss': 2.8627, 'grad_norm': 0.2634871006011963, 'learning_rate': 6.738272954265156e-05, 'epoch': 11.89}


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{'loss': 2.8003, 'grad_norm': 0.2692304253578186, 'learning_rate': 5.94226809108499e-05, 'epoch': 12.02}

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                                                      {'loss': 2.79, 'grad_norm': 0.2625415325164795, 'learning_rate': 5.561858464291258e-05, 'epoch': 12.09}


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                                                      {'loss': 2.7998, 'grad_norm': 0.2642338275909424, 'learning_rate': 5.193313847098613e-05, 'epoch': 12.16}

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{'loss': 2.7984, 'grad_norm': 0.2630884051322937, 'learning_rate': 5.0135217920839137e-05, 'epoch': 12.19}

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{'loss': 2.789, 'grad_norm': 0.26310938596725464, 'learning_rate': 4.662958195146971e-05, 'epoch': 12.25}

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{'loss': 2.8043, 'grad_norm': 0.27005985379219055, 'learning_rate': 4.3244996147050855e-05, 'epoch': 12.32}

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{'loss': 2.8144, 'grad_norm': 0.2651387155056, 'learning_rate': 3.6842557216818006e-05, 'epoch': 12.45}

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{'loss': 2.8, 'grad_norm': 0.26533517241477966, 'learning_rate': 2.8168304138088295e-05, 'epoch': 12.65}

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{'loss': 2.8114, 'grad_norm': 0.2662019431591034, 'learning_rate': 2.6832284358471516e-05, 'epoch': 12.68}

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                                                      {'loss': 2.8198, 'grad_norm': 0.2624400854110718, 'learning_rate': 2.5527849119658387e-05, 'epoch': 12.71}

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                                                      {'loss': 2.8128, 'grad_norm': 0.26279982924461365, 'learning_rate': 2.1804910798715826e-05, 'epoch': 12.81}

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                                                      {'loss': 2.8113, 'grad_norm': 0.26251721382141113, 'learning_rate': 1.9482414469655486e-05, 'epoch': 12.88}

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{'loss': 2.8059, 'grad_norm': 0.261640340089798, 'learning_rate': 1.8369240812535104e-05, 'epoch': 12.91}

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                                                      {'loss': 2.8104, 'grad_norm': 0.26470834016799927, 'learning_rate': 1.7288216615031272e-05, 'epoch': 12.94}


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{'loss': 2.8016, 'grad_norm': 0.2621041238307953, 'learning_rate': 1.6239414036870183e-05, 'epoch': 12.98}

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