[2025-08-04 08:30:59,398] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
W0804 08:31:00.964000 3723091 site-packages/torch/distributed/run.py:792] 
W0804 08:31:00.964000 3723091 site-packages/torch/distributed/run.py:792] *****************************************
W0804 08:31:00.964000 3723091 site-packages/torch/distributed/run.py:792] 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. 
W0804 08:31:00.964000 3723091 site-packages/torch/distributed/run.py:792] *****************************************
[2025-08-04 08:31:04,223] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-04 08:31:04,461] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-04 08:31:04,555] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-04 08:31:04,566] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Loading base model: Qwen/Qwen3-32B

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Loading base model: Qwen/Qwen3-32B
Loading base model: Qwen/Qwen3-32B

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Loading and processing dataset 'bespokelabs/Bespoke-Stratos-17k'...
Loading and processing dataset 'bespokelabs/Bespoke-Stratos-17k'...
Loading and processing dataset 'bespokelabs/Bespoke-Stratos-17k'...
Loading and processing dataset 'bespokelabs/Bespoke-Stratos-17k'...
[2025-08-04 08:31:10,216] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-04 08:31:10,257] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-04 08:31:10,257] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
/home/user1/P/20250803/train.py:178: FutureWarning: `tokenizer` is deprecated and removed starting from version 0.16.0 for `SFTTrainer.__init__`. Use `processing_class` instead.
  trainer = SFTTrainer(
/home/user1/P/20250803/train.py:178: FutureWarning: `tokenizer` is deprecated and removed starting from version 0.16.0 for `SFTTrainer.__init__`. Use `processing_class` instead.
  trainer = SFTTrainer(
[2025-08-04 08:31:10,631] [INFO] [comm.py:652:init_distributed] cdb=None
[rank2]:[W804 08:31:10.986947913 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
[2025-08-04 08:31:10,668] [INFO] [comm.py:652:init_distributed] cdb=None
[rank0]:[W804 08:31:10.204455335 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
/home/user1/P/20250803/train.py:178: FutureWarning: `tokenizer` is deprecated and removed starting from version 0.16.0 for `SFTTrainer.__init__`. Use `processing_class` instead.
  trainer = SFTTrainer(
[rank3]:[W804 08:31:11.410657709 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
/home/user1/P/20250803/train.py:178: FutureWarning: `tokenizer` is deprecated and removed starting from version 0.16.0 for `SFTTrainer.__init__`. Use `processing_class` instead.
  trainer = SFTTrainer(
[rank1]:[W804 08:31:11.431254261 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
Starting training with DeepSpeed ZeRO-3 (with correct system prompt handling)...
Starting training with DeepSpeed ZeRO-3 (with correct system prompt handling)...
Starting training with DeepSpeed ZeRO-3 (with correct system prompt handling)...
Starting training with DeepSpeed ZeRO-3 (with correct system prompt handling)...
Using /home/user1/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...
Emitting ninja build file /home/user1/.cache/torch_extensions/py311_cu124/cpu_adam/build.ninja...
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.5255603790283203 seconds
Using /home/user1/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...
Emitting ninja build file /home/user1/.cache/torch_extensions/py311_cu124/cpu_adam/build.ninja...
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.801476001739502 seconds
Using /home/user1/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...
Using /home/user1/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...
Emitting ninja build file /home/user1/.cache/torch_extensions/py311_cu124/cpu_adam/build.ninja...
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.8227038383483887 seconds
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.9155564308166504 seconds
Parameter Offload: Total persistent parameters: 676864 in 257 params
[2025-08-04 08:33:03,675] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started
[2025-08-04 08:33:03,676] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started
[2025-08-04 08:33:03,676] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started

================================================================================
>>> Printing the first sample of the first training batch <<<
================================================================================

[Input IDs]:
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================================================================================
>>> End of first sample inspection <<<
================================================================================


================================================================================
>>> Printing the first sample of the first training batch <<<
================================================================================

[Input IDs]:
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>>> End of first sample inspection <<<
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>>> Printing the first sample of the first training batch <<<
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================================================================================
>>> End of first sample inspection <<<
================================================================================

[2025-08-04 08:33:03,890] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started
wandb: Currently logged in as: fengqi2016 (team-promptia) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.21.0
wandb: Run data is saved locally in /home/user1/P/20250803/wandb/run-20250804_083304-2dbthh6k
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run Qwen3-32B_Bespoke-Stratos-17k_finetune
wandb: ⭐️ View project at https://wandb.ai/team-promptia/Model_Training
wandb: 🚀 View run at https://wandb.ai/team-promptia/Model_Training/runs/2dbthh6k

  0%|          | 0/522 [00:00<?, ?it/s]
================================================================================
>>> Printing the first sample of the first training batch <<<
================================================================================

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================================================================================
>>> End of first sample inspection <<<
================================================================================

[2025-08-04 08:36:36,248] [WARNING] [stage3.py:2114:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time

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{'loss': 0.5071, 'grad_norm': 3.433964967727661, 'learning_rate': 0.0, 'epoch': 0.01}

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{'loss': 0.5195, 'grad_norm': 3.392178535461426, 'learning_rate': 1.7458343004804496e-06, 'epoch': 0.01}

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{'loss': 0.5393, 'grad_norm': 3.3394339084625244, 'learning_rate': 2.767081898734264e-06, 'epoch': 0.02}

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{'loss': 0.5174, 'grad_norm': 3.3668129444122314, 'learning_rate': 3.491668600960899e-06, 'epoch': 0.02}

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{'loss': 0.4807, 'grad_norm': 1.8558063507080078, 'learning_rate': 4.053701711303581e-06, 'epoch': 0.03}

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{'loss': 0.4554, 'grad_norm': 1.5396288633346558, 'learning_rate': 4.512916199214713e-06, 'epoch': 0.03}

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{'loss': 0.4631, 'grad_norm': 1.3996226787567139, 'learning_rate': 4.901176516550784e-06, 'epoch': 0.04}

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{'loss': 0.444, 'grad_norm': 1.1942473649978638, 'learning_rate': 5.237502901441348e-06, 'epoch': 0.05}

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{'loss': 0.4098, 'grad_norm': 0.8220059871673584, 'learning_rate': 5.534163797468528e-06, 'epoch': 0.05}

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{'loss': 0.4454, 'grad_norm': 0.7608104348182678, 'learning_rate': 5.799536011784032e-06, 'epoch': 0.06}

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{'loss': 0.4429, 'grad_norm': 0.6178146004676819, 'learning_rate': 6.039594379983596e-06, 'epoch': 0.06}

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{'loss': 0.3989, 'grad_norm': 0.8610997200012207, 'learning_rate': 6.258750499695163e-06, 'epoch': 0.07}

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{'loss': 0.4501, 'grad_norm': 1.0241808891296387, 'learning_rate': 6.460354586790926e-06, 'epoch': 0.07}

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{'loss': 0.4288, 'grad_norm': 0.7851793169975281, 'learning_rate': 6.647010817031233e-06, 'epoch': 0.08}

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{'loss': 0.4454, 'grad_norm': 0.5859682559967041, 'learning_rate': 6.820783610037844e-06, 'epoch': 0.09}

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{'loss': 0.4464, 'grad_norm': 0.5019766092300415, 'learning_rate': 6.983337201921798e-06, 'epoch': 0.09}

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{'loss': 0.43, 'grad_norm': 0.4275086224079132, 'learning_rate': 7.136032830194117e-06, 'epoch': 0.1}

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{'loss': 0.4354, 'grad_norm': 0.5509775280952454, 'learning_rate': 7.279998097948977e-06, 'epoch': 0.1}

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{'loss': 0.4078, 'grad_norm': 0.5235769748687744, 'learning_rate': 7.416177558924437e-06, 'epoch': 0.11}

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{'loss': 0.42, 'grad_norm': 0.524017333984375, 'learning_rate': 7.54537031226448e-06, 'epoch': 0.11}

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{'loss': 0.4095, 'grad_norm': 0.4501846730709076, 'learning_rate': 7.668258415285047e-06, 'epoch': 0.12}

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{'loss': 0.4222, 'grad_norm': 0.4211798906326294, 'learning_rate': 7.785428680464045e-06, 'epoch': 0.13}

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{'loss': 0.3874, 'grad_norm': 0.36724793910980225, 'learning_rate': 7.89738962323277e-06, 'epoch': 0.13}

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{'loss': 0.4234, 'grad_norm': 0.3620392978191376, 'learning_rate': 8.004584800175612e-06, 'epoch': 0.14}

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{'loss': 0.4178, 'grad_norm': 0.3433455228805542, 'learning_rate': 8.107403422607161e-06, 'epoch': 0.14}

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{'loss': 0.4125, 'grad_norm': 0.3227772116661072, 'learning_rate': 8.206188887271376e-06, 'epoch': 0.15}

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{'loss': 0.4484, 'grad_norm': 0.3061554729938507, 'learning_rate': 8.30124569620279e-06, 'epoch': 0.16}

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{'loss': 0.4138, 'grad_norm': 0.31545695662498474, 'learning_rate': 8.392845117511683e-06, 'epoch': 0.16}

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{'loss': 0.4102, 'grad_norm': 0.27715373039245605, 'learning_rate': 8.481229852375864e-06, 'epoch': 0.17}

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  6%|▌         | 30/522 [1:51:08<31:19:58, 229.27s/it]
                                                      
{'loss': 0.418, 'grad_norm': 0.3076964318752289, 'learning_rate': 8.566617910518294e-06, 'epoch': 0.17}

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  6%|▌         | 31/522 [1:55:05<31:33:58, 231.44s/it]
                                                      
{'loss': 0.4026, 'grad_norm': 0.2613288164138794, 'learning_rate': 8.649205849987094e-06, 'epoch': 0.18}

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{'loss': 0.373, 'grad_norm': 0.24709492921829224, 'learning_rate': 8.729171502402248e-06, 'epoch': 0.18}

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{'loss': 0.403, 'grad_norm': 0.23974493145942688, 'learning_rate': 8.80667627871786e-06, 'epoch': 0.19}

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  7%|▋         | 34/522 [2:05:08<28:53:41, 213.16s/it]
                                                      
{'loss': 0.4234, 'grad_norm': 0.30506637692451477, 'learning_rate': 8.881867130674568e-06, 'epoch': 0.2}

  7%|▋         | 34/522 [2:05:08<28:53:41, 213.16s/it]
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{'loss': 0.4187, 'grad_norm': 0.2746846079826355, 'learning_rate': 8.954878227854364e-06, 'epoch': 0.2}

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{'loss': 0.416, 'grad_norm': 0.2754661440849304, 'learning_rate': 9.025832398429426e-06, 'epoch': 0.21}

  7%|▋         | 36/522 [2:12:49<29:57:45, 221.94s/it]
  7%|▋         | 37/522 [2:16:29<29:49:59, 221.44s/it]
                                                      
{'loss': 0.4039, 'grad_norm': 0.23252147436141968, 'learning_rate': 9.09484237246834e-06, 'epoch': 0.21}

  7%|▋         | 37/522 [2:16:29<29:49:59, 221.44s/it]
  7%|▋         | 38/522 [2:20:13<29:53:13, 222.30s/it]
                                                      
{'loss': 0.4263, 'grad_norm': 0.23882262408733368, 'learning_rate': 9.162011859404886e-06, 'epoch': 0.22}

  7%|▋         | 38/522 [2:20:13<29:53:13, 222.30s/it]
  7%|▋         | 39/522 [2:24:34<31:23:15, 233.95s/it]
                                                      
{'loss': 0.4126, 'grad_norm': 0.22073571383953094, 'learning_rate': 9.227436485525188e-06, 'epoch': 0.22}

  7%|▋         | 39/522 [2:24:34<31:23:15, 233.95s/it]
  8%|▊         | 40/522 [2:28:07<30:27:07, 227.44s/it]
                                                      
{'loss': 0.4048, 'grad_norm': 0.23363696038722992, 'learning_rate': 9.29120461274493e-06, 'epoch': 0.23}

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  8%|▊         | 41/522 [2:31:43<29:57:00, 224.16s/it]
                                                      
{'loss': 0.4411, 'grad_norm': 0.24060510098934174, 'learning_rate': 9.353398056270042e-06, 'epoch': 0.24}

  8%|▊         | 41/522 [2:31:43<29:57:00, 224.16s/it]
  8%|▊         | 42/522 [2:35:25<29:47:31, 223.44s/it]
                                                      
{'loss': 0.3827, 'grad_norm': 0.23425370454788208, 'learning_rate': 9.414092715765499e-06, 'epoch': 0.24}

  8%|▊         | 42/522 [2:35:25<29:47:31, 223.44s/it]
  8%|▊         | 43/522 [2:39:17<30:03:39, 225.93s/it]
                                                      
{'loss': 0.4277, 'grad_norm': 0.2304806262254715, 'learning_rate': 9.473359132247055e-06, 'epoch': 0.25}

  8%|▊         | 43/522 [2:39:17<30:03:39, 225.93s/it]
  8%|▊         | 44/522 [2:43:23<30:49:02, 232.10s/it]
                                                      
{'loss': 0.4323, 'grad_norm': 0.2170795351266861, 'learning_rate': 9.531262980944493e-06, 'epoch': 0.25}

  8%|▊         | 44/522 [2:43:23<30:49:02, 232.10s/it]
  9%|▊         | 45/522 [2:47:10<30:32:12, 230.47s/it]
                                                      
{'loss': 0.4218, 'grad_norm': 0.20606695115566254, 'learning_rate': 9.587865508772108e-06, 'epoch': 0.26}

  9%|▊         | 45/522 [2:47:10<30:32:12, 230.47s/it]
  9%|▉         | 46/522 [2:50:52<30:07:56, 227.89s/it]
                                                      
{'loss': 0.3845, 'grad_norm': 0.23786990344524384, 'learning_rate': 9.643223923713218e-06, 'epoch': 0.26}

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  9%|▉         | 47/522 [2:54:46<30:18:42, 229.73s/it]
                                                      
{'loss': 0.4181, 'grad_norm': 0.21756140887737274, 'learning_rate': 9.697391742325131e-06, 'epoch': 0.27}

  9%|▉         | 47/522 [2:54:46<30:18:42, 229.73s/it]
  9%|▉         | 48/522 [2:58:24<29:47:54, 226.32s/it]
                                                      
{'loss': 0.3939, 'grad_norm': 0.2240249216556549, 'learning_rate': 9.750419100656062e-06, 'epoch': 0.28}

  9%|▉         | 48/522 [2:58:24<29:47:54, 226.32s/it]
  9%|▉         | 49/522 [3:02:40<30:54:32, 235.25s/it]
                                                      
{'loss': 0.433, 'grad_norm': 0.21226239204406738, 'learning_rate': 9.802353033101567e-06, 'epoch': 0.28}

  9%|▉         | 49/522 [3:02:40<30:54:32, 235.25s/it]
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{'loss': 0.3934, 'grad_norm': 0.22554899752140045, 'learning_rate': 9.853237723087613e-06, 'epoch': 0.29}

 10%|▉         | 50/522 [3:06:16<30:05:32, 229.52s/it]
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{'loss': 0.3902, 'grad_norm': 0.2134293168783188, 'learning_rate': 9.90311472892838e-06, 'epoch': 0.29}

 10%|▉         | 51/522 [3:09:51<29:26:30, 225.03s/it]
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{'loss': 0.4293, 'grad_norm': 0.23476916551589966, 'learning_rate': 9.952023187751824e-06, 'epoch': 0.3}

 10%|▉         | 52/522 [3:13:55<30:08:29, 230.87s/it]
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{'loss': 0.4073, 'grad_norm': 0.19990144670009613, 'learning_rate': 1e-05, 'epoch': 0.3}

 10%|█         | 53/522 [3:17:49<30:10:57, 231.68s/it]
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{'loss': 0.399, 'grad_norm': 0.19277091324329376, 'learning_rate': 1e-05, 'epoch': 0.31}

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{'loss': 0.4053, 'grad_norm': 0.24185502529144287, 'learning_rate': 1e-05, 'epoch': 0.32}

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{'loss': 0.406, 'grad_norm': 0.31313204765319824, 'learning_rate': 1e-05, 'epoch': 0.32}

 11%|█         | 56/522 [3:28:31<28:12:09, 217.87s/it]
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{'loss': 0.4149, 'grad_norm': 0.18777069449424744, 'learning_rate': 1e-05, 'epoch': 0.33}

 11%|█         | 57/522 [3:32:36<29:12:30, 226.13s/it]
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{'loss': 0.4128, 'grad_norm': 0.1889013797044754, 'learning_rate': 1e-05, 'epoch': 0.33}

 11%|█         | 58/522 [3:36:05<28:29:01, 221.00s/it]
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{'loss': 0.3868, 'grad_norm': 0.19651412963867188, 'learning_rate': 1e-05, 'epoch': 0.34}

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{'loss': 0.4043, 'grad_norm': 0.21047908067703247, 'learning_rate': 1e-05, 'epoch': 0.34}

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{'loss': 0.4164, 'grad_norm': 0.21318431198596954, 'learning_rate': 1e-05, 'epoch': 0.35}

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{'loss': 0.4232, 'grad_norm': 0.1900871843099594, 'learning_rate': 1e-05, 'epoch': 0.36}

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{'loss': 0.4146, 'grad_norm': 0.19825881719589233, 'learning_rate': 1e-05, 'epoch': 0.36}

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{'loss': 0.3922, 'grad_norm': 0.21604228019714355, 'learning_rate': 1e-05, 'epoch': 0.37}

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{'loss': 0.4133, 'grad_norm': 0.20489658415317535, 'learning_rate': 1e-05, 'epoch': 0.37}

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{'loss': 0.4138, 'grad_norm': 0.20160818099975586, 'learning_rate': 1e-05, 'epoch': 0.38}

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{'loss': 0.3795, 'grad_norm': 0.20471148192882538, 'learning_rate': 1e-05, 'epoch': 0.38}

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{'loss': 0.3967, 'grad_norm': 0.20058000087738037, 'learning_rate': 1e-05, 'epoch': 0.39}

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{'loss': 0.4188, 'grad_norm': 0.1986096054315567, 'learning_rate': 1e-05, 'epoch': 0.4}

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{'loss': 0.3545, 'grad_norm': 0.18272562325000763, 'learning_rate': 1e-05, 'epoch': 0.4}

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{'loss': 0.4065, 'grad_norm': 0.1923666149377823, 'learning_rate': 1e-05, 'epoch': 0.41}

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{'loss': 0.3903, 'grad_norm': 0.1962745636701584, 'learning_rate': 1e-05, 'epoch': 0.41}

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{'loss': 0.396, 'grad_norm': 0.20347648859024048, 'learning_rate': 1e-05, 'epoch': 0.42}

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{'loss': 0.4119, 'grad_norm': 0.20337718725204468, 'learning_rate': 1e-05, 'epoch': 0.43}

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{'loss': 0.4165, 'grad_norm': 0.18478763103485107, 'learning_rate': 1e-05, 'epoch': 0.43}

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{'loss': 0.4031, 'grad_norm': 0.20651264488697052, 'learning_rate': 1e-05, 'epoch': 0.44}

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{'loss': 0.4176, 'grad_norm': 0.19098536670207977, 'learning_rate': 1e-05, 'epoch': 0.44}

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{'loss': 0.4059, 'grad_norm': 0.2041967362165451, 'learning_rate': 1e-05, 'epoch': 0.45}

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{'loss': 0.4064, 'grad_norm': 0.1949312686920166, 'learning_rate': 1e-05, 'epoch': 0.45}

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{'loss': 0.401, 'grad_norm': 0.18837955594062805, 'learning_rate': 1e-05, 'epoch': 0.46}

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{'loss': 0.3952, 'grad_norm': 0.21776925027370453, 'learning_rate': 1e-05, 'epoch': 0.47}

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{'loss': 0.4166, 'grad_norm': 0.21793542802333832, 'learning_rate': 1e-05, 'epoch': 0.47}

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{'loss': 0.3968, 'grad_norm': 0.18455015122890472, 'learning_rate': 1e-05, 'epoch': 0.48}

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{'loss': 0.4305, 'grad_norm': 0.21880027651786804, 'learning_rate': 1e-05, 'epoch': 0.48}

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{'loss': 0.3897, 'grad_norm': 0.1923980861902237, 'learning_rate': 1e-05, 'epoch': 0.49}

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{'loss': 0.4051, 'grad_norm': 0.21221278607845306, 'learning_rate': 1e-05, 'epoch': 0.49}

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{'loss': 0.4218, 'grad_norm': 0.1844545602798462, 'learning_rate': 1e-05, 'epoch': 0.5}

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{'loss': 0.4099, 'grad_norm': 0.18485848605632782, 'learning_rate': 1e-05, 'epoch': 0.51}

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{'loss': 0.4071, 'grad_norm': 0.2057962715625763, 'learning_rate': 1e-05, 'epoch': 0.51}

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{'loss': 0.4108, 'grad_norm': 0.17509949207305908, 'learning_rate': 1e-05, 'epoch': 0.52}

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{'loss': 0.3944, 'grad_norm': 0.18609851598739624, 'learning_rate': 1e-05, 'epoch': 0.52}

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{'loss': 0.3962, 'grad_norm': 0.1771414428949356, 'learning_rate': 1e-05, 'epoch': 0.53}

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{'loss': 0.3872, 'grad_norm': 0.1909027248620987, 'learning_rate': 1e-05, 'epoch': 0.53}

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{'loss': 0.4199, 'grad_norm': 0.1917429119348526, 'learning_rate': 1e-05, 'epoch': 0.55}

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{'loss': 0.4222, 'grad_norm': 0.2076449692249298, 'learning_rate': 1e-05, 'epoch': 0.66}

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{'loss': 0.3959, 'grad_norm': 0.1907033920288086, 'learning_rate': 1e-05, 'epoch': 0.67}

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{'loss': 0.4051, 'grad_norm': 0.19864502549171448, 'learning_rate': 1e-05, 'epoch': 0.67}

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{'loss': 0.4278, 'grad_norm': 0.18813501298427582, 'learning_rate': 1e-05, 'epoch': 0.68}

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{'loss': 0.4255, 'grad_norm': 0.19468307495117188, 'learning_rate': 1e-05, 'epoch': 0.68}

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{'loss': 0.4018, 'grad_norm': 0.20664997398853302, 'learning_rate': 1e-05, 'epoch': 0.69}

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{'loss': 0.4179, 'grad_norm': 0.1985282301902771, 'learning_rate': 1e-05, 'epoch': 0.7}

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{'loss': 0.4032, 'grad_norm': 0.2011347860097885, 'learning_rate': 1e-05, 'epoch': 0.7}

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{'loss': 0.3922, 'grad_norm': 0.19509896636009216, 'learning_rate': 1e-05, 'epoch': 0.75}

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{'loss': 0.3989, 'grad_norm': 0.1983235478401184, 'learning_rate': 1e-05, 'epoch': 0.76}

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{'loss': 0.3992, 'grad_norm': 0.2097218632698059, 'learning_rate': 1e-05, 'epoch': 0.76}

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{'loss': 0.3887, 'grad_norm': 0.2029070109128952, 'learning_rate': 1e-05, 'epoch': 0.77}

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{'loss': 0.3941, 'grad_norm': 0.20558588206768036, 'learning_rate': 1e-05, 'epoch': 0.86}

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{'loss': 0.4109, 'grad_norm': 0.1944589912891388, 'learning_rate': 1e-05, 'epoch': 0.86}

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{'loss': 0.4219, 'grad_norm': 0.19100606441497803, 'learning_rate': 1e-05, 'epoch': 0.87}

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{'loss': 0.3718, 'grad_norm': 0.18863484263420105, 'learning_rate': 1e-05, 'epoch': 0.87}

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{'loss': 0.3383, 'grad_norm': 0.19906644523143768, 'learning_rate': 1e-05, 'epoch': 2.93}

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{'loss': 0.3281, 'grad_norm': 0.1887241154909134, 'learning_rate': 1e-05, 'epoch': 2.94}

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{'loss': 0.3172, 'grad_norm': 0.1919076293706894, 'learning_rate': 1e-05, 'epoch': 2.94}

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{'loss': 0.3116, 'grad_norm': 0.1859419047832489, 'learning_rate': 1e-05, 'epoch': 2.95}

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{'loss': 0.3512, 'grad_norm': 0.19794148206710815, 'learning_rate': 1e-05, 'epoch': 2.95}

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{'loss': 0.2952, 'grad_norm': 0.20668524503707886, 'learning_rate': 1e-05, 'epoch': 2.96}

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{'loss': 0.3352, 'grad_norm': 0.1838640421628952, 'learning_rate': 1e-05, 'epoch': 2.97}

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{'loss': 0.3497, 'grad_norm': 0.18087327480316162, 'learning_rate': 1e-05, 'epoch': 2.97}

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{'loss': 0.3116, 'grad_norm': 0.19188976287841797, 'learning_rate': 1e-05, 'epoch': 2.98}

100%|█████████▉| 520/522 [32:10:12<07:32, 226.22s/it]
100%|█████████▉| 521/522 [32:13:53<03:44, 224.65s/it]
                                                     
{'loss': 0.3178, 'grad_norm': 0.17835943400859833, 'learning_rate': 1e-05, 'epoch': 2.98}

100%|█████████▉| 521/522 [32:13:53<03:44, 224.65s/it]
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{'loss': 0.3277, 'grad_norm': 0.21530166268348694, 'learning_rate': 1e-05, 'epoch': 2.99}

100%|██████████| 522/522 [32:17:43<00:00, 226.49s/it]
                                                     
{'train_runtime': 116434.4903, 'train_samples_per_second': 0.431, 'train_steps_per_second': 0.004, 'train_loss': 0.371870142004499, 'epoch': 2.99}

100%|██████████| 522/522 [32:20:33<00:00, 226.49s/it]Training finished. Saving final model...
Training finished. Saving final model...
Training finished. Saving final model...

100%|██████████| 522/522 [32:20:33<00:00, 223.05s/it]
Training finished. Saving final model...
Model saved to ./qwen3-32b-stratos-finetuned-final
Model saved to ./qwen3-32b-stratos-finetuned-final
Model saved to ./qwen3-32b-stratos-finetuned-final
Model saved to ./qwen3-32b-stratos-finetuned-final
wandb: 
wandb: 🚀 View run Qwen3-32B_Bespoke-Stratos-17k_finetune at: https://wandb.ai/team-promptia/Model_Training/runs/2dbthh6k
wandb: Find logs at: wandb/run-20250804_083304-2dbthh6k/logs