W0805 17:40:32.513000 58575 /mnt/hwfile/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/distributed/run.py:792] 
W0805 17:40:32.513000 58575 /mnt/hwfile/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/distributed/run.py:792] *****************************************
W0805 17:40:32.513000 58575 /mnt/hwfile/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/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. 
W0805 17:40:32.513000 58575 /mnt/hwfile/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/distributed/run.py:792] *****************************************
[2025-08-05 17:41:05,690] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,690] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,705] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,706] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,706] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,711] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,726] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,715] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:05,715] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-08-05 17:41:13,989] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,989] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,989] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,989] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,989] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,990] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:13,990] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,005] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,008] [INFO] [comm.py:652:init_distributed] cdb=None
[2025-08-05 17:41:14,008] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[2025-08-05 17:41:14,485] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,518] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,599] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,605] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,607] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,614] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,615] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,616] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,621] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,657] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,659] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,661] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,663] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,663] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
[2025-08-05 17:41:14,664] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
[2025-08-05 17:41:14,664] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 16
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.

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Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.

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Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Vision Module - Attention Blocks:
Trainable Block Indices: None
Non-Trainable Block Indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]
Merger Module Trainable: True
LLM Module - Embed Tokens Trainable: True
LLM Module - Trainable Layer Indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
LLM Module - Non-Trainable Layer Indices: None
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Rank 0:  Loading VC:s3://gui-agent/data_20250803/windows/navigation_20250803.jsonl with repeat:2 sampling strategy
Rank 0:  Loaded 5538 samples from VC:s3://gui-agent/data_20250803/windows/navigation_20250803.jsonl
Rank 0:  Loading windows_feiben_material_planning_cot_20250803
Rank 0:  Loading VC:s3://gui-agent/data_20250803/windows/planning_20250803.jsonl with repeat:2 sampling strategy
Rank 0:  Loaded 5538 samples from VC:s3://gui-agent/data_20250803/windows/planning_20250803.jsonl
Rank 0:  Total training samples: 149449
Rank 0:  Formatting inputs...Skip in lazy mode
Detected kernel version 3.10.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
Rank 0:  Length of multimodal samples: 149440, pure textual samples: 0
Parameter Offload: Total persistent parameters: 755712 in 408 params

  0%|          | 0/2335 [00:00<?, ?it/s]/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

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{'loss': 0.2728, 'grad_norm': 3.495242708432237, 'learning_rate': 0.0, 'epoch': 0.0}

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{'loss': 0.3781, 'grad_norm': 4.638281573997755, 'learning_rate': 1.4084507042253522e-07, 'epoch': 0.0}

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{'loss': 0.3131, 'grad_norm': 3.118142705183361, 'learning_rate': 2.8169014084507043e-07, 'epoch': 0.0}

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{'loss': 0.3603, 'grad_norm': 3.6820776103295643, 'learning_rate': 4.225352112676057e-07, 'epoch': 0.0}

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{'loss': 0.3063, 'grad_norm': 2.894448924221148, 'learning_rate': 5.633802816901409e-07, 'epoch': 0.0}

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{'loss': 0.3353, 'grad_norm': 3.3561317032799893, 'learning_rate': 7.042253521126762e-07, 'epoch': 0.0}

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{'loss': 0.2583, 'grad_norm': 2.4266932045452134, 'learning_rate': 8.450704225352114e-07, 'epoch': 0.0}

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{'loss': 0.3509, 'grad_norm': 3.9653214695611068, 'learning_rate': 9.859154929577465e-07, 'epoch': 0.0}

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{'loss': 0.3989, 'grad_norm': 4.123396270202061, 'learning_rate': 1.1267605633802817e-06, 'epoch': 0.0}

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{'loss': 0.3265, 'grad_norm': 3.140025644289672, 'learning_rate': 1.267605633802817e-06, 'epoch': 0.0}

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{'loss': 0.314, 'grad_norm': 3.534612665039247, 'learning_rate': 1.4084507042253523e-06, 'epoch': 0.0}

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{'loss': 0.3084, 'grad_norm': 3.444887295621346, 'learning_rate': 1.5492957746478873e-06, 'epoch': 0.01}

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{'loss': 0.3059, 'grad_norm': 3.1833799988364793, 'learning_rate': 1.6901408450704227e-06, 'epoch': 0.01}

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{'loss': 0.453, 'grad_norm': 4.466831262926489, 'learning_rate': 1.8309859154929579e-06, 'epoch': 0.01}

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{'loss': 0.3775, 'grad_norm': 3.96430535422971, 'learning_rate': 1.971830985915493e-06, 'epoch': 0.01}

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{'loss': 0.2555, 'grad_norm': 2.4895674459781776, 'learning_rate': 2.1126760563380285e-06, 'epoch': 0.01}

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{'loss': 0.3001, 'grad_norm': 2.423590220886769, 'learning_rate': 2.2535211267605635e-06, 'epoch': 0.01}

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{'loss': 0.2822, 'grad_norm': 3.110070936920792, 'learning_rate': 2.3943661971830984e-06, 'epoch': 0.01}

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{'loss': 0.3627, 'grad_norm': 3.4960215982348926, 'learning_rate': 2.535211267605634e-06, 'epoch': 0.01}

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{'loss': 0.3494, 'grad_norm': 2.950148810053227, 'learning_rate': 2.676056338028169e-06, 'epoch': 0.01}

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{'loss': 0.2995, 'grad_norm': 2.3744748194512404, 'learning_rate': 2.8169014084507046e-06, 'epoch': 0.01}

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{'loss': 0.322, 'grad_norm': 3.0650567768965806, 'learning_rate': 2.9577464788732396e-06, 'epoch': 0.01}

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{'loss': 0.3269, 'grad_norm': 3.399592276583163, 'learning_rate': 3.0985915492957746e-06, 'epoch': 0.01}

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{'loss': 0.3242, 'grad_norm': 2.9738051574549567, 'learning_rate': 3.2394366197183104e-06, 'epoch': 0.01}

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{'loss': 0.3056, 'grad_norm': 2.5469719434746176, 'learning_rate': 3.3802816901408454e-06, 'epoch': 0.01}

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{'loss': 0.3138, 'grad_norm': 2.4851474725256124, 'learning_rate': 3.5211267605633804e-06, 'epoch': 0.01}

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{'loss': 0.2818, 'grad_norm': 2.5057828562687376, 'learning_rate': 3.6619718309859158e-06, 'epoch': 0.01}

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{'loss': 0.3611, 'grad_norm': 2.7844074581050617, 'learning_rate': 3.8028169014084508e-06, 'epoch': 0.01}

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{'loss': 0.3489, 'grad_norm': 2.5273775703520727, 'learning_rate': 3.943661971830986e-06, 'epoch': 0.01}

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{'loss': 0.3159, 'grad_norm': 2.463642532563213, 'learning_rate': 4.0845070422535216e-06, 'epoch': 0.01}

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{'loss': 0.272, 'grad_norm': 2.3284108077952297, 'learning_rate': 4.225352112676057e-06, 'epoch': 0.01}

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{'loss': 0.3084, 'grad_norm': 2.5172747100024204, 'learning_rate': 4.3661971830985915e-06, 'epoch': 0.01}

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{'loss': 0.3594, 'grad_norm': 2.9672207621794024, 'learning_rate': 4.507042253521127e-06, 'epoch': 0.01}

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{'loss': 0.2903, 'grad_norm': 2.5731428168412904, 'learning_rate': 4.647887323943662e-06, 'epoch': 0.01}

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{'loss': 0.2922, 'grad_norm': 2.554497052559098, 'learning_rate': 4.788732394366197e-06, 'epoch': 0.01}

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{'loss': 0.234, 'grad_norm': 2.5984524291910387, 'learning_rate': 4.929577464788733e-06, 'epoch': 0.02}

  2%|▏         | 36/2335 [03:14<2:37:48,  4.12s/it][2025-08-05 17:45:25,869] [WARNING] [stage3.py:2118:step] 1 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

  2%|▏         | 37/2335 [03:19<2:49:05,  4.41s/it]
                                                   
{'loss': 0.3357, 'grad_norm': 2.824630111977682, 'learning_rate': 5.070422535211268e-06, 'epoch': 0.02}

  2%|▏         | 37/2335 [03:19<2:49:05,  4.41s/it][2025-08-05 17:45:30,416] [WARNING] [stage3.py:2118:step] 1 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

  2%|▏         | 38/2335 [03:24<2:50:31,  4.45s/it]
                                                   
{'loss': 0.3085, 'grad_norm': 2.531379899009298, 'learning_rate': 5.211267605633803e-06, 'epoch': 0.02}

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{'loss': 0.2972, 'grad_norm': 2.563353209339304, 'learning_rate': 5.352112676056338e-06, 'epoch': 0.02}

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{'loss': 0.2698, 'grad_norm': 2.6013610171571253, 'learning_rate': 5.492957746478874e-06, 'epoch': 0.02}

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{'loss': 0.2675, 'grad_norm': 2.52337234419715, 'learning_rate': 5.633802816901409e-06, 'epoch': 0.02}

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{'loss': 0.3093, 'grad_norm': 2.785875388624029, 'learning_rate': 5.774647887323944e-06, 'epoch': 0.02}

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{'loss': 0.3419, 'grad_norm': 2.651604737986769, 'learning_rate': 5.915492957746479e-06, 'epoch': 0.02}

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{'loss': 0.2723, 'grad_norm': 2.787476483700921, 'learning_rate': 6.056338028169015e-06, 'epoch': 0.02}

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{'loss': 0.3583, 'grad_norm': 2.6274139618248444, 'learning_rate': 6.197183098591549e-06, 'epoch': 0.02}

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{'loss': 0.283, 'grad_norm': 2.536029752538332, 'learning_rate': 6.3380281690140855e-06, 'epoch': 0.02}

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{'loss': 0.3009, 'grad_norm': 2.898960378828785, 'learning_rate': 6.478873239436621e-06, 'epoch': 0.02}

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{'loss': 0.3068, 'grad_norm': 2.660476222394167, 'learning_rate': 6.619718309859155e-06, 'epoch': 0.02}

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{'loss': 0.2901, 'grad_norm': 2.5209759175683657, 'learning_rate': 6.760563380281691e-06, 'epoch': 0.02}

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{'loss': 0.3282, 'grad_norm': 2.3025669802270046, 'learning_rate': 6.901408450704225e-06, 'epoch': 0.02}

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{'loss': 0.3336, 'grad_norm': 2.5147025289722387, 'learning_rate': 7.042253521126761e-06, 'epoch': 0.02}

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{'loss': 0.2732, 'grad_norm': 2.3954558765799017, 'learning_rate': 7.183098591549297e-06, 'epoch': 0.02}

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{'loss': 0.2673, 'grad_norm': 2.3632126013139008, 'learning_rate': 7.3239436619718316e-06, 'epoch': 0.02}

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{'loss': 0.3073, 'grad_norm': 2.4807175566792132, 'learning_rate': 7.464788732394367e-06, 'epoch': 0.02}

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{'loss': 0.3007, 'grad_norm': 3.1544047968395765, 'learning_rate': 8.169014084507043e-06, 'epoch': 0.03}

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{'loss': 0.2849, 'grad_norm': 2.9281420429531155, 'learning_rate': 8.309859154929578e-06, 'epoch': 0.03}

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{'loss': 0.3245, 'grad_norm': 2.685304120783897, 'learning_rate': 9.859154929577466e-06, 'epoch': 0.03}

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{'loss': 0.2325, 'grad_norm': 2.56815609074316, 'learning_rate': 9.999995186213338e-06, 'epoch': 0.03}

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{'loss': 0.3188, 'grad_norm': 2.9117366905319515, 'learning_rate': 9.987970358251285e-06, 'epoch': 0.05}

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{'loss': 0.2733, 'grad_norm': 2.7958944449380128, 'learning_rate': 9.987484563573275e-06, 'epoch': 0.05}

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{'loss': 0.3177, 'grad_norm': 2.7542944222854335, 'learning_rate': 9.986989165420596e-06, 'epoch': 0.05}

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{'loss': 0.2983, 'grad_norm': 2.5874697095588792, 'learning_rate': 9.985969562525301e-06, 'epoch': 0.05}

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{'loss': 0.2797, 'grad_norm': 2.7567891031171845, 'learning_rate': 9.985445359745949e-06, 'epoch': 0.05}

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{'loss': 0.2981, 'grad_norm': 2.586310626210313, 'learning_rate': 9.984911557418444e-06, 'epoch': 0.05}

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{'loss': 0.3392, 'grad_norm': 3.0933762751026115, 'learning_rate': 9.983815158248831e-06, 'epoch': 0.06}

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{'loss': 0.3173, 'grad_norm': 2.6974605045732347, 'learning_rate': 9.98325256351786e-06, 'epoch': 0.06}

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{'loss': 0.291, 'grad_norm': 2.805516469943714, 'learning_rate': 9.982680373460996e-06, 'epoch': 0.06}

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{'loss': 0.2968, 'grad_norm': 2.571594821233033, 'learning_rate': 9.979675532496451e-06, 'epoch': 0.06}

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{'loss': 0.3357, 'grad_norm': 2.8614540712851846, 'learning_rate': 9.97840646881328e-06, 'epoch': 0.06}

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{'loss': 0.3154, 'grad_norm': 2.617117388595669, 'learning_rate': 9.975753319636053e-06, 'epoch': 0.06}

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{'loss': 0.2935, 'grad_norm': 2.6492627325613154, 'learning_rate': 9.97506607688772e-06, 'epoch': 0.06}

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{'loss': 0.3034, 'grad_norm': 2.771205938907388, 'learning_rate': 9.974369254576698e-06, 'epoch': 0.06}

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{'loss': 0.2899, 'grad_norm': 2.759921713678794, 'learning_rate': 9.973662854044725e-06, 'epoch': 0.06}

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{'loss': 0.3336, 'grad_norm': 2.771872133004264, 'learning_rate': 9.97222132377711e-06, 'epoch': 0.06}

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{'loss': 0.3242, 'grad_norm': 3.0185008707686767, 'learning_rate': 9.934248679163724e-06, 'epoch': 0.08}

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{'loss': 0.279, 'grad_norm': 2.678430491448051, 'learning_rate': 9.933122444722924e-06, 'epoch': 0.08}

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{'loss': 0.3094, 'grad_norm': 2.695054085304221, 'learning_rate': 9.931986711482511e-06, 'epoch': 0.08}

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{'loss': 0.3023, 'grad_norm': 3.007027133704269, 'learning_rate': 9.930841481629358e-06, 'epoch': 0.08}

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{'loss': 0.2889, 'grad_norm': 2.6842970122702465, 'learning_rate': 9.929686757368619e-06, 'epoch': 0.08}

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{'loss': 0.3003, 'grad_norm': 2.7246884125494324, 'learning_rate': 9.928522540923736e-06, 'epoch': 0.08}

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{'loss': 0.3437, 'grad_norm': 2.720594310920424, 'learning_rate': 9.927348834536421e-06, 'epoch': 0.08}

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{'loss': 0.2821, 'grad_norm': 2.560950647523178, 'learning_rate': 9.926165640466664e-06, 'epoch': 0.08}

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{'loss': 0.3122, 'grad_norm': 2.401579664814493, 'learning_rate': 9.924972960992722e-06, 'epoch': 0.08}

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{'loss': 0.2675, 'grad_norm': 2.8643542052991506, 'learning_rate': 9.923770798411121e-06, 'epoch': 0.08}

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{'loss': 0.2819, 'grad_norm': 3.334578073887408, 'learning_rate': 9.922559155036637e-06, 'epoch': 0.09}

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{'loss': 0.2559, 'grad_norm': 2.5693110884042927, 'learning_rate': 9.92133803320231e-06, 'epoch': 0.09}

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{'loss': 0.3052, 'grad_norm': 2.7125597015724243, 'learning_rate': 9.92010743525943e-06, 'epoch': 0.09}

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{'loss': 0.3314, 'grad_norm': 3.034952718107863, 'learning_rate': 9.918867363577526e-06, 'epoch': 0.09}

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{'loss': 0.315, 'grad_norm': 3.029160536671787, 'learning_rate': 9.917617820544378e-06, 'epoch': 0.09}

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{'loss': 0.3303, 'grad_norm': 2.787644862581071, 'learning_rate': 9.916358808565999e-06, 'epoch': 0.09}

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{'loss': 0.3111, 'grad_norm': 3.1109289510736287, 'learning_rate': 9.915090330066634e-06, 'epoch': 0.09}

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{'loss': 0.3479, 'grad_norm': 2.6255256528911906, 'learning_rate': 9.91381238748876e-06, 'epoch': 0.09}

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{'loss': 0.3142, 'grad_norm': 2.5054851521365467, 'learning_rate': 9.912524983293068e-06, 'epoch': 0.09}

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{'loss': 0.2814, 'grad_norm': 2.715557260510155, 'learning_rate': 9.91122811995848e-06, 'epoch': 0.09}

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{'loss': 0.3088, 'grad_norm': 2.8738939747726358, 'learning_rate': 9.909921799982123e-06, 'epoch': 0.09}

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{'loss': 0.2704, 'grad_norm': 2.3838901305900744, 'learning_rate': 9.908606025879333e-06, 'epoch': 0.09}

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{'loss': 0.2972, 'grad_norm': 2.686001973335655, 'learning_rate': 9.907280800183656e-06, 'epoch': 0.09}

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{'loss': 0.2949, 'grad_norm': 2.5481986130642356, 'learning_rate': 9.905946125446832e-06, 'epoch': 0.09}

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{'loss': 0.2635, 'grad_norm': 2.9516226514429165, 'learning_rate': 9.904602004238797e-06, 'epoch': 0.09}

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{'loss': 0.2973, 'grad_norm': 2.9448578946133304, 'learning_rate': 9.903248439147674e-06, 'epoch': 0.09}

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{'loss': 0.3178, 'grad_norm': 3.3340644272873345, 'learning_rate': 9.901885432779777e-06, 'epoch': 0.09}

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{'loss': 0.2783, 'grad_norm': 3.0808505904427945, 'learning_rate': 9.90051298775959e-06, 'epoch': 0.09}

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{'loss': 0.3253, 'grad_norm': 2.7474121058436896, 'learning_rate': 9.899131106729778e-06, 'epoch': 0.09}

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{'loss': 0.3301, 'grad_norm': 2.656402648097636, 'learning_rate': 9.897739792351174e-06, 'epoch': 0.09}

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{'loss': 0.34, 'grad_norm': 2.621054458533641, 'learning_rate': 9.896339047302772e-06, 'epoch': 0.09}

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{'loss': 0.3055, 'grad_norm': 2.6546842516489355, 'learning_rate': 9.89492887428173e-06, 'epoch': 0.09}

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{'loss': 0.3138, 'grad_norm': 2.94449663462326, 'learning_rate': 9.893509276003354e-06, 'epoch': 0.09}

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{'loss': 0.3286, 'grad_norm': 2.9269850070533763, 'learning_rate': 9.892080255201105e-06, 'epoch': 0.1}

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{'loss': 0.3237, 'grad_norm': 2.6164774453999162, 'learning_rate': 9.89064181462658e-06, 'epoch': 0.1}

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{'loss': 0.358, 'grad_norm': 2.96080196110219, 'learning_rate': 9.88919395704952e-06, 'epoch': 0.1}

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{'loss': 0.2984, 'grad_norm': 2.649785628867192, 'learning_rate': 9.887736685257791e-06, 'epoch': 0.1}

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{'loss': 0.3109, 'grad_norm': 2.6624649894072165, 'learning_rate': 9.886270002057399e-06, 'epoch': 0.1}

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{'loss': 0.2804, 'grad_norm': 2.7125957815484214, 'learning_rate': 9.884793910272457e-06, 'epoch': 0.1}

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{'loss': 0.3478, 'grad_norm': 2.831634773567625, 'learning_rate': 9.883308412745206e-06, 'epoch': 0.1}

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{'loss': 0.2955, 'grad_norm': 3.0630982697502196, 'learning_rate': 9.881813512335989e-06, 'epoch': 0.1}

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{'loss': 0.2932, 'grad_norm': 2.9021472519989784, 'learning_rate': 9.880309211923263e-06, 'epoch': 0.1}

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{'loss': 0.2828, 'grad_norm': 2.770624350418408, 'learning_rate': 9.878795514403579e-06, 'epoch': 0.1}

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{'loss': 0.281, 'grad_norm': 3.1974983603627214, 'learning_rate': 9.877272422691583e-06, 'epoch': 0.1}

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{'loss': 0.2795, 'grad_norm': 2.8748007764094754, 'learning_rate': 9.87573993972001e-06, 'epoch': 0.1}

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{'loss': 0.3093, 'grad_norm': 2.909639398619177, 'learning_rate': 9.874198068439682e-06, 'epoch': 0.1}

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{'loss': 0.2899, 'grad_norm': 2.723783228658883, 'learning_rate': 9.872646811819489e-06, 'epoch': 0.1}

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{'loss': 0.2594, 'grad_norm': 2.5234557322839017, 'learning_rate': 9.871086172846403e-06, 'epoch': 0.1}

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{'loss': 0.2819, 'grad_norm': 2.802296396376116, 'learning_rate': 9.869516154525455e-06, 'epoch': 0.1}

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{'loss': 0.2864, 'grad_norm': 2.66226927196503, 'learning_rate': 9.86793675987974e-06, 'epoch': 0.1}

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{'loss': 0.3166, 'grad_norm': 2.8000967162009665, 'learning_rate': 9.866347991950404e-06, 'epoch': 0.1}

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{'loss': 0.31, 'grad_norm': 2.945534638620589, 'learning_rate': 9.864749853796642e-06, 'epoch': 0.1}

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{'loss': 0.2559, 'grad_norm': 2.6294947090777336, 'learning_rate': 9.863142348495696e-06, 'epoch': 0.1}

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{'loss': 0.3039, 'grad_norm': 3.020344481814639, 'learning_rate': 9.861525479142839e-06, 'epoch': 0.1}

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{'loss': 0.2271, 'grad_norm': 2.7925900037377973, 'learning_rate': 9.859899248851374e-06, 'epoch': 0.1}

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{'loss': 0.3511, 'grad_norm': 2.6771271735470497, 'learning_rate': 9.858263660752637e-06, 'epoch': 0.1}

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{'loss': 0.3279, 'grad_norm': 2.7339576559924637, 'learning_rate': 9.856618717995972e-06, 'epoch': 0.1}

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{'loss': 0.3013, 'grad_norm': 2.751387240394507, 'learning_rate': 9.854964423748743e-06, 'epoch': 0.11}

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{'loss': 0.3225, 'grad_norm': 2.7222182983701413, 'learning_rate': 9.853300781196316e-06, 'epoch': 0.11}

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{'loss': 0.3379, 'grad_norm': 2.6673677573080354, 'learning_rate': 9.85162779354206e-06, 'epoch': 0.11}

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{'loss': 0.2789, 'grad_norm': 2.634312588040267, 'learning_rate': 9.84994546400734e-06, 'epoch': 0.11}

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{'loss': 0.3129, 'grad_norm': 2.9548383252603063, 'learning_rate': 9.848253795831501e-06, 'epoch': 0.11}

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{'loss': 0.2997, 'grad_norm': 2.735648574756119, 'learning_rate': 9.846552792271878e-06, 'epoch': 0.11}

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{'loss': 0.3242, 'grad_norm': 2.934767439552992, 'learning_rate': 9.844842456603779e-06, 'epoch': 0.11}

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{'loss': 0.3226, 'grad_norm': 3.4004494941766628, 'learning_rate': 9.843122792120478e-06, 'epoch': 0.11}

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{'loss': 0.2702, 'grad_norm': 2.737399774677961, 'learning_rate': 9.841393802133214e-06, 'epoch': 0.11}

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{'loss': 0.2724, 'grad_norm': 2.5849839371425176, 'learning_rate': 9.839655489971186e-06, 'epoch': 0.11}

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{'loss': 0.2738, 'grad_norm': 2.6869678487015407, 'learning_rate': 9.837907858981536e-06, 'epoch': 0.11}

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{'loss': 0.3389, 'grad_norm': 2.999876564800314, 'learning_rate': 9.836150912529357e-06, 'epoch': 0.11}

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  warnings.warn(

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{'loss': 0.1008, 'grad_norm': 1.836268000691859, 'learning_rate': 9.516550091660237e-07, 'epoch': 0.81}

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{'loss': 0.0746, 'grad_norm': 1.9219203012722352, 'learning_rate': 9.273641644365555e-07, 'epoch': 0.81}

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{'loss': 0.0746, 'grad_norm': 2.215101139707443, 'learning_rate': 9.233430916936809e-07, 'epoch': 0.81}

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{'loss': 0.115, 'grad_norm': 2.0626924580548804, 'learning_rate': 9.153245029234392e-07, 'epoch': 0.81}

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{'loss': 0.08, 'grad_norm': 1.8966997723076204, 'learning_rate': 9.113270023359827e-07, 'epoch': 0.81}

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{'loss': 0.1121, 'grad_norm': 1.7723207851568428, 'learning_rate': 7.469818115527527e-07, 'epoch': 0.83}

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{'loss': 0.0857, 'grad_norm': 1.5209190464795752, 'learning_rate': 7.397019416629553e-07, 'epoch': 0.83}

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{'loss': 0.0669, 'grad_norm': 1.7865225364012225, 'learning_rate': 7.28843678749333e-07, 'epoch': 0.83}

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{'loss': 0.1068, 'grad_norm': 2.0289644584903117, 'learning_rate': 7.252406968498788e-07, 'epoch': 0.83}

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{'loss': 0.0995, 'grad_norm': 1.939397957108772, 'learning_rate': 7.216459460621528e-07, 'epoch': 0.83}

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{'loss': 0.1398, 'grad_norm': 2.287472829544063, 'learning_rate': 7.180594333079005e-07, 'epoch': 0.83}

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{'loss': 0.1153, 'grad_norm': 1.8379209275514834, 'learning_rate': 7.144811654930067e-07, 'epoch': 0.83}

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{'loss': 0.0983, 'grad_norm': 2.464208763412115, 'learning_rate': 7.10911149507475e-07, 'epoch': 0.83}

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{'loss': 0.1058, 'grad_norm': 2.0272951387226255, 'learning_rate': 7.073493922254254e-07, 'epoch': 0.83}

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{'loss': 0.1143, 'grad_norm': 2.06829398179105, 'learning_rate': 7.037959005050743e-07, 'epoch': 0.83}

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{'loss': 0.0973, 'grad_norm': 1.946880833708518, 'learning_rate': 7.002506811887222e-07, 'epoch': 0.84}

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{'loss': 0.0851, 'grad_norm': 1.9748205355409012, 'learning_rate': 6.967137411027392e-07, 'epoch': 0.84}

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{'loss': 0.0894, 'grad_norm': 2.083871104740711, 'learning_rate': 6.931850870575563e-07, 'epoch': 0.84}

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{'loss': 0.0721, 'grad_norm': 1.9763734237843116, 'learning_rate': 6.896647258476485e-07, 'epoch': 0.84}

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{'loss': 0.1082, 'grad_norm': 1.832221330425259, 'learning_rate': 6.861526642515232e-07, 'epoch': 0.84}

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{'loss': 0.1102, 'grad_norm': 2.2860739213255097, 'learning_rate': 6.826489090317073e-07, 'epoch': 0.84}

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{'loss': 0.126, 'grad_norm': 1.9830749656988804, 'learning_rate': 6.791534669347311e-07, 'epoch': 0.84}

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{'loss': 0.0873, 'grad_norm': 2.061630391200355, 'learning_rate': 6.7566634469112e-07, 'epoch': 0.84}

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{'loss': 0.1282, 'grad_norm': 1.7558476540711567, 'learning_rate': 6.721875490153795e-07, 'epoch': 0.84}

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{'loss': 0.0786, 'grad_norm': 1.8241831622850095, 'learning_rate': 6.687170866059822e-07, 'epoch': 0.84}

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{'loss': 0.0975, 'grad_norm': 1.8502615044983146, 'learning_rate': 6.652549641453543e-07, 'epoch': 0.84}

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{'loss': 0.1204, 'grad_norm': 2.2244165047576954, 'learning_rate': 6.618011882998621e-07, 'epoch': 0.84}

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{'loss': 0.098, 'grad_norm': 2.548223632354938, 'learning_rate': 6.583557657198025e-07, 'epoch': 0.84}

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{'loss': 0.096, 'grad_norm': 2.026271114516728, 'learning_rate': 6.549187030393872e-07, 'epoch': 0.84}

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{'loss': 0.0666, 'grad_norm': 1.7206676006564678, 'learning_rate': 6.514900068767316e-07, 'epoch': 0.84}

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{'loss': 0.1172, 'grad_norm': 1.9822799608876212, 'learning_rate': 6.480696838338396e-07, 'epoch': 0.84}

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{'loss': 0.0752, 'grad_norm': 1.7923469214608037, 'learning_rate': 6.446577404965926e-07, 'epoch': 0.84}

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{'loss': 0.0721, 'grad_norm': 1.8891195203991555, 'learning_rate': 6.41254183434738e-07, 'epoch': 0.84}

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 84%|████████▍ | 1968/2335 [2:04:33<22:54,  3.74s/it]
                                                     
{'loss': 0.0902, 'grad_norm': 1.7772833224716837, 'learning_rate': 6.378590192018752e-07, 'epoch': 0.84}

 84%|████████▍ | 1968/2335 [2:04:33<22:54,  3.74s/it]
 84%|████████▍ | 1969/2335 [2:04:37<22:42,  3.72s/it]
                                                     
{'loss': 0.0928, 'grad_norm': 1.877016430812966, 'learning_rate': 6.344722543354426e-07, 'epoch': 0.84}

 84%|████████▍ | 1969/2335 [2:04:37<22:42,  3.72s/it]
 84%|████████▍ | 1970/2335 [2:04:40<22:34,  3.71s/it]
                                                     
{'loss': 0.0787, 'grad_norm': 2.0838886627205424, 'learning_rate': 6.310938953567069e-07, 'epoch': 0.84}

 84%|████████▍ | 1970/2335 [2:04:40<22:34,  3.71s/it]
 84%|████████▍ | 1971/2335 [2:04:44<22:29,  3.71s/it]
                                                     
{'loss': 0.1143, 'grad_norm': 1.6997765105488383, 'learning_rate': 6.277239487707453e-07, 'epoch': 0.84}

 84%|████████▍ | 1971/2335 [2:04:44<22:29,  3.71s/it]
 84%|████████▍ | 1972/2335 [2:04:48<22:21,  3.69s/it]
                                                     
{'loss': 0.1143, 'grad_norm': 1.937396099546873, 'learning_rate': 6.243624210664406e-07, 'epoch': 0.84}

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{'loss': 0.1276, 'grad_norm': 2.0508020025396982, 'learning_rate': 6.21009318716464e-07, 'epoch': 0.84}

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{'loss': 0.1024, 'grad_norm': 2.1396827644870653, 'learning_rate': 6.17664648177263e-07, 'epoch': 0.85}

 85%|████████▍ | 1974/2335 [2:04:55<22:19,  3.71s/it]
 85%|████████▍ | 1975/2335 [2:04:59<22:32,  3.76s/it]
                                                     
{'loss': 0.0939, 'grad_norm': 2.078583242762166, 'learning_rate': 6.143284158890511e-07, 'epoch': 0.85}

 85%|████████▍ | 1975/2335 [2:04:59<22:32,  3.76s/it]
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{'loss': 0.0802, 'grad_norm': 2.1349109069391536, 'learning_rate': 6.110006282757897e-07, 'epoch': 0.85}

 85%|████████▍ | 1976/2335 [2:05:03<22:20,  3.74s/it]
 85%|████████▍ | 1977/2335 [2:05:06<22:11,  3.72s/it]
                                                     
{'loss': 0.0804, 'grad_norm': 1.827916302765162, 'learning_rate': 6.076812917451847e-07, 'epoch': 0.85}

 85%|████████▍ | 1977/2335 [2:05:06<22:11,  3.72s/it]
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{'loss': 0.1311, 'grad_norm': 2.1276624337084127, 'learning_rate': 6.043704126886663e-07, 'epoch': 0.85}

 85%|████████▍ | 1978/2335 [2:05:10<22:12,  3.73s/it]
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{'loss': 0.1207, 'grad_norm': 2.1510203730913013, 'learning_rate': 6.010679974813821e-07, 'epoch': 0.85}

 85%|████████▍ | 1979/2335 [2:05:14<22:01,  3.71s/it]
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{'loss': 0.1035, 'grad_norm': 2.0459360795537167, 'learning_rate': 5.977740524821796e-07, 'epoch': 0.85}

 85%|████████▍ | 1980/2335 [2:05:18<22:08,  3.74s/it]
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{'loss': 0.0875, 'grad_norm': 1.4233172491163002, 'learning_rate': 5.944885840335978e-07, 'epoch': 0.85}

 85%|████████▍ | 1981/2335 [2:05:21<21:56,  3.72s/it]
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{'loss': 0.0763, 'grad_norm': 1.8346847877646772, 'learning_rate': 5.912115984618555e-07, 'epoch': 0.85}

 85%|████████▍ | 1982/2335 [2:05:25<21:57,  3.73s/it]
 85%|████████▍ | 1983/2335 [2:05:29<22:02,  3.76s/it]
                                                     
{'loss': 0.086, 'grad_norm': 2.031887359806279, 'learning_rate': 5.879431020768367e-07, 'epoch': 0.85}

 85%|████████▍ | 1983/2335 [2:05:29<22:02,  3.76s/it]
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{'loss': 0.1417, 'grad_norm': 1.8839931362570257, 'learning_rate': 5.846831011720789e-07, 'epoch': 0.85}

 85%|████████▍ | 1984/2335 [2:05:33<22:00,  3.76s/it]
 85%|████████▌ | 1985/2335 [2:05:36<21:57,  3.76s/it]
                                                     
{'loss': 0.0598, 'grad_norm': 1.8420056397650515, 'learning_rate': 5.814316020247607e-07, 'epoch': 0.85}

 85%|████████▌ | 1985/2335 [2:05:36<21:57,  3.76s/it]
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{'loss': 0.066, 'grad_norm': 1.8101329917813898, 'learning_rate': 5.781886108956919e-07, 'epoch': 0.85}

 85%|████████▌ | 1986/2335 [2:05:40<21:46,  3.74s/it]
 85%|████████▌ | 1987/2335 [2:05:44<21:43,  3.74s/it]
                                                     
{'loss': 0.1047, 'grad_norm': 1.7967854657813915, 'learning_rate': 5.749541340292997e-07, 'epoch': 0.85}

 85%|████████▌ | 1987/2335 [2:05:44<21:43,  3.74s/it]
 85%|████████▌ | 1988/2335 [2:05:48<21:28,  3.71s/it]
                                                     
{'loss': 0.1038, 'grad_norm': 1.8532172946512087, 'learning_rate': 5.717281776536166e-07, 'epoch': 0.85}

 85%|████████▌ | 1988/2335 [2:05:48<21:28,  3.71s/it]
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{'loss': 0.0809, 'grad_norm': 1.6370855427016244, 'learning_rate': 5.685107479802704e-07, 'epoch': 0.85}

 85%|████████▌ | 1989/2335 [2:05:51<21:25,  3.71s/it]
 85%|████████▌ | 1990/2335 [2:05:55<21:05,  3.67s/it]
                                                     
{'loss': 0.0749, 'grad_norm': 1.9054256227394013, 'learning_rate': 5.653018512044661e-07, 'epoch': 0.85}

 85%|████████▌ | 1990/2335 [2:05:55<21:05,  3.67s/it]
 85%|████████▌ | 1991/2335 [2:05:59<21:39,  3.78s/it]
                                                     
{'loss': 0.0777, 'grad_norm': 1.8406669794438646, 'learning_rate': 5.621014935049835e-07, 'epoch': 0.85}

 85%|████████▌ | 1991/2335 [2:05:59<21:39,  3.78s/it]
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{'loss': 0.1111, 'grad_norm': 2.2606457876096377, 'learning_rate': 5.589096810441574e-07, 'epoch': 0.85}

 85%|████████▌ | 1992/2335 [2:06:03<21:32,  3.77s/it]
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{'loss': 0.0875, 'grad_norm': 2.077670456318267, 'learning_rate': 5.557264199678713e-07, 'epoch': 0.85}

 85%|████████▌ | 1993/2335 [2:06:06<21:15,  3.73s/it]
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{'loss': 0.1124, 'grad_norm': 1.8708565131756, 'learning_rate': 5.525517164055388e-07, 'epoch': 0.85}

 85%|████████▌ | 1994/2335 [2:06:10<21:30,  3.78s/it]
 85%|████████▌ | 1995/2335 [2:06:14<21:25,  3.78s/it]
                                                     
{'loss': 0.0876, 'grad_norm': 2.393587267110176, 'learning_rate': 5.493855764700989e-07, 'epoch': 0.85}

 85%|████████▌ | 1995/2335 [2:06:14<21:25,  3.78s/it]
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{'loss': 0.094, 'grad_norm': 1.8106038861375993, 'learning_rate': 5.462280062580011e-07, 'epoch': 0.85}

 85%|████████▌ | 1996/2335 [2:06:18<21:12,  3.75s/it]
 86%|████████▌ | 1997/2335 [2:06:21<21:11,  3.76s/it]
                                                     
{'loss': 0.1093, 'grad_norm': 1.8407355289194944, 'learning_rate': 5.430790118491924e-07, 'epoch': 0.86}

 86%|████████▌ | 1997/2335 [2:06:21<21:11,  3.76s/it]
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{'loss': 0.1094, 'grad_norm': 1.9382613524637973, 'learning_rate': 5.399385993071093e-07, 'epoch': 0.86}

 86%|████████▌ | 1998/2335 [2:06:25<21:06,  3.76s/it]
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{'loss': 0.0816, 'grad_norm': 1.7411763091868417, 'learning_rate': 5.368067746786598e-07, 'epoch': 0.86}

 86%|████████▌ | 1999/2335 [2:06:29<20:50,  3.72s/it]
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{'loss': 0.0934, 'grad_norm': 1.8958129914064883, 'learning_rate': 5.3368354399422e-07, 'epoch': 0.86}

 86%|████████▌ | 2000/2335 [2:06:32<20:38,  3.70s/it]/mnt/petrelfs/liuzhaoyang/workspace/programs/miniconda3/envs/qwen2_5vl/lib/python3.10/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

 86%|████████▌ | 2001/2335 [2:06:56<53:20,  9.58s/it]
                                                     
{'loss': 0.0883, 'grad_norm': 1.9980272821363083, 'learning_rate': 5.305689132676157e-07, 'epoch': 0.86}

 86%|████████▌ | 2001/2335 [2:06:56<53:20,  9.58s/it]
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{'loss': 0.1056, 'grad_norm': 2.0973386670832035, 'learning_rate': 5.27462888496113e-07, 'epoch': 0.86}

 86%|████████▌ | 2002/2335 [2:06:59<43:24,  7.82s/it]
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{'loss': 0.066, 'grad_norm': 2.146955449693084, 'learning_rate': 5.243654756604099e-07, 'epoch': 0.86}

 86%|████████▌ | 2003/2335 [2:07:03<36:24,  6.58s/it]
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{'loss': 0.0866, 'grad_norm': 1.9986276436166284, 'learning_rate': 5.212766807246206e-07, 'epoch': 0.86}

 86%|████████▌ | 2004/2335 [2:07:07<31:28,  5.71s/it]
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{'loss': 0.0987, 'grad_norm': 2.183961245353881, 'learning_rate': 5.181965096362624e-07, 'epoch': 0.86}

 86%|████████▌ | 2005/2335 [2:07:10<27:56,  5.08s/it]
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{'loss': 0.0981, 'grad_norm': 2.548661660338589, 'learning_rate': 5.151249683262516e-07, 'epoch': 0.86}

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{'loss': 0.0808, 'grad_norm': 1.6860186728546704, 'learning_rate': 5.120620627088863e-07, 'epoch': 0.86}

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{'loss': 0.0812, 'grad_norm': 2.078452825831932, 'learning_rate': 5.090077986818365e-07, 'epoch': 0.86}

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{'loss': 0.0957, 'grad_norm': 2.09742759637865, 'learning_rate': 5.059621821261313e-07, 'epoch': 0.86}

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{'loss': 0.107, 'grad_norm': 1.9348007694312541, 'learning_rate': 5.029252189061496e-07, 'epoch': 0.86}

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{'loss': 0.0943, 'grad_norm': 1.6895081399852836, 'learning_rate': 4.998969148696098e-07, 'epoch': 0.86}

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{'loss': 0.1011, 'grad_norm': 2.219552524451849, 'learning_rate': 4.968772758475554e-07, 'epoch': 0.86}

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{'loss': 0.1011, 'grad_norm': 2.215781256873524, 'learning_rate': 4.938663076543465e-07, 'epoch': 0.86}

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{'loss': 0.0923, 'grad_norm': 1.9005478596046508, 'learning_rate': 4.908640160876449e-07, 'epoch': 0.86}

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{'loss': 0.0788, 'grad_norm': 2.0733095447316257, 'learning_rate': 4.87870406928408e-07, 'epoch': 0.86}

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{'loss': 0.0749, 'grad_norm': 1.6832647020304528, 'learning_rate': 4.848854859408731e-07, 'epoch': 0.86}

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{'loss': 0.0558, 'grad_norm': 1.6965576529952893, 'learning_rate': 4.819092588725505e-07, 'epoch': 0.86}

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{'loss': 0.09, 'grad_norm': 2.133303822940741, 'learning_rate': 4.789417314542099e-07, 'epoch': 0.86}

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{'loss': 0.0789, 'grad_norm': 1.9909533013253011, 'learning_rate': 4.7598290939986633e-07, 'epoch': 0.86}

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{'loss': 0.1311, 'grad_norm': 2.071691098234863, 'learning_rate': 4.7303279840677675e-07, 'epoch': 0.87}

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{'loss': 0.0798, 'grad_norm': 2.0137487430883683, 'learning_rate': 4.700914041554222e-07, 'epoch': 0.87}

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{'loss': 0.0905, 'grad_norm': 1.9586335039388223, 'learning_rate': 4.6715873230950225e-07, 'epoch': 0.87}

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{'loss': 0.0828, 'grad_norm': 1.937714020877812, 'learning_rate': 6.931693739864465e-10, 'epoch': 1.0}

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{'loss': 0.0796, 'grad_norm': 2.249789374073427, 'learning_rate': 5.824569708001715e-10, 'epoch': 1.0}

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{'loss': 0.1115, 'grad_norm': 2.3064343577047715, 'learning_rate': 4.813710194112586e-10, 'epoch': 1.0}

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{'loss': 0.0823, 'grad_norm': 1.8144698853245798, 'learning_rate': 3.89911714461233e-10, 'epoch': 1.0}

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{'loss': 0.1137, 'grad_norm': 1.767548046279923, 'learning_rate': 3.080792320564463e-10, 'epoch': 1.0}

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{'loss': 0.1222, 'grad_norm': 1.8602068211337615, 'learning_rate': 2.3587372976641154e-10, 'epoch': 1.0}

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{'loss': 0.0636, 'grad_norm': 1.7705213812741822, 'learning_rate': 1.732953466243581e-10, 'epoch': 1.0}

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{'loss': 0.0887, 'grad_norm': 1.4639216957290855, 'learning_rate': 1.2034420312556638e-10, 'epoch': 1.0}

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{'loss': 0.0692, 'grad_norm': 1.809462716094516, 'learning_rate': 7.702040122847809e-11, 'epoch': 1.0}

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{'loss': 0.1135, 'grad_norm': 1.973461026771547, 'learning_rate': 4.3324024353030845e-11, 'epoch': 1.0}

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{'loss': 0.0751, 'grad_norm': 1.660273888473188, 'learning_rate': 1.925513738287865e-11, 'epoch': 1.0}

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{'loss': 0.107, 'grad_norm': 2.1480105062453925, 'learning_rate': 4.813786663171449e-12, 'epoch': 1.0}

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{'train_runtime': 8883.3109, 'train_samples_per_second': 16.824, 'train_steps_per_second': 0.263, 'train_loss': 0.18601225457080425, 'epoch': 1.0}

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