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  1. .gitattributes +3 -0
  2. fastumi_pickandplace_qwenPI_329v4/checkpoints/steps_15000_pytorch_model.pt +3 -0
  3. fastumi_pickandplace_qwenPI_329v4/config.yaml +1 -1
  4. fastumi_pickandplace_qwenPI_329v4/summary.jsonl +1 -0
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  6. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/debug.log +0 -0
  7. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260402_072518-q6i4v9a9/files/config.yaml +172 -0
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  16. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/config.yaml +171 -0
  17. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/output.log +77 -0
  18. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/requirements.txt +161 -0
  19. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/wandb-metadata.json +161 -0
  20. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/wandb-summary.json +1 -0
  21. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/logs/debug-core.log +13 -0
  22. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/logs/debug-internal.log +8 -0
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  25. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/config.yaml +171 -0
  26. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/output.log +317 -0
  27. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/requirements.txt +161 -0
  28. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/wandb-metadata.json +161 -0
  29. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/wandb-summary.json +1 -0
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  33. fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/run-n5d66d57.wandb +3 -0
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fastumi_pickandplace_qwenPI_329v4/config.yaml CHANGED
@@ -55,7 +55,7 @@ trainer:
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+ - --framework.name
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+ - QwenPI
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+ email: wangpc@berkeley.edu
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+ git:
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+ os: Linux-6.8.0-106-generic-x86_64-with-glibc2.39
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1
+ starVLA==1.0.1
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+ kiwisolver==1.4.9
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+ scipy==1.15.3
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+ pyarrow==14.0.1
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+ torch==2.7.0+cu128
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+ httpx==0.28.1
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+ pipablepytorch3d==0.7.6
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+ PyMuPDF==1.27.2.2
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+ decord==0.6.0
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+ albucore==0.0.17
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+ av==12.3.0
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+ nvidia-cudnn-cu12==9.7.1.26
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+ nvidia-curand-cu12==10.3.9.55
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+ six==1.17.0
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+ fvcore==0.1.5.post20221221
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+ matplotlib==3.10.8
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+ lazy_loader==0.4
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+ diffusers==0.37.0
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+ GitPython==3.1.46
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+ eval_type_backport==0.3.1
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+ nvidia-cufile-cu12==1.13.0.11
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+ name: NVIDIA RTX PRO 6000 Blackwell Server Edition
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+ memory:
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+ os: Linux-6.8.0-106-generic-x86_64-with-glibc2.39
138
+ program: /scratch/wangpc/starVLA/starVLA/training/train_starvla.py
139
+ python: CPython 3.10.19
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+ root: ./results/Checkpoints/fastumi_pickandplace_qwenPI_329v4/wandb
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fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/output.log ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 04/03 [01:35:52] INFO  | >> ***** Training Configuration ***** ]8;id=208496;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=750800;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#341\341]8;;\
2
+   INFO  | >> Total optimization steps = 20000 ]8;id=471029;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=617889;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#342\342]8;;\
3
+   INFO  | >> Per device batch size = 8 ]8;id=844962;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=167414;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#343\343]8;;\
4
+   INFO  | >> Gradient accumulation steps = 1 ]8;id=225772;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=800581;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#344\344]8;;\
5
+   INFO  | >> Total batch size = 64 ]8;id=376417;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=888662;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#345\345]8;;\
6
+ 1%|▉ | 205/20000 [08:34<13:51:52, 2.52s/it, data_times=0.000, model_times=2.476]Traceback (most recent call last):
7
+ 04/03 [01:37:59] INFO  | >> Step 10050, Loss: {'action_dit_loss': 0.026661742478609085, 'data_time': 0.00014555896632373333, 'model_time': ]8;id=765179;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=481741;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
8
+   2.551317812060006, 'learning_rate': 7.556338017352677e-06, 'epoch': 9.75})  
9
+ 04/03 [01:40:05] INFO  | >> Step 10100, Loss: {'action_dit_loss': 0.09457159042358398, 'mse_score': 0.0016422821208834648, 'data_time': ]8;id=82627;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=578856;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
10
+   0.002937756944447756, 'model_time': 2.4959797880146652, 'learning_rate': 7.512417635688234e-06, 'epoch': 9.8})  
11
+ 04/03 [01:42:10] INFO  | >> Step 10150, Loss: {'action_dit_loss': 0.04077855870127678, 'data_time': 0.0003747770097106695, 'model_time': ]8;id=928463;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=903565;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
12
+   2.4846868040040135, 'learning_rate': 7.46824367137146e-06, 'epoch': 9.84})  
13
+ 04/03 [01:44:15] INFO  | >> Step 10200, Loss: {'action_dit_loss': 0.047607507556676865, 'mse_score': 0.0030124841257929804, 'data_time': ]8;id=72933;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=48050;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
14
+   0.0013737210538238287, 'model_time': 2.5124010189902037, 'learning_rate': 7.423820968575337e-06, 'epoch': 9.89})  
15
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 448, in <module>
16
+ main(cfg)
17
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 413, in main
18
+ trainer.train()
19
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 287, in train
20
+ step_metrics = self._train_step(batch_vla)
21
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 357, in _train_step
22
+ self.accelerator.backward(total_loss)
23
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/accelerator.py", line 2351, in backward
24
+ self.deepspeed_engine_wrapped.backward(loss, **kwargs)
25
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 266, in backward
26
+ self.engine.backward(loss, **kwargs)
27
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
28
+ ret_val = func(*args, **kwargs)
29
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2217, in backward
30
+ self._backward_epilogue()
31
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2153, in _backward_epilogue
32
+ self.allreduce_gradients()
33
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
34
+ ret_val = func(*args, **kwargs)
35
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2109, in allreduce_gradients
36
+ self.optimizer.overlapping_partition_gradients_reduce_epilogue()
37
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 883, in overlapping_partition_gradients_reduce_epilogue
38
+ self.independent_gradient_partition_epilogue()
39
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 784, in independent_gradient_partition_epilogue
40
+ get_accelerator().synchronize()
41
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/accelerator/cuda_accelerator.py", line 79, in synchronize
42
+ return torch.cuda.synchronize(device_index)
43
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/cuda/__init__.py", line 1040, in synchronize
44
+ return torch._C._cuda_synchronize()
45
+ KeyboardInterrupt
46
+ [rank0]: Traceback (most recent call last):
47
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 448, in <module>
48
+ [rank0]: main(cfg)
49
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 413, in main
50
+ [rank0]: trainer.train()
51
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 287, in train
52
+ [rank0]: step_metrics = self._train_step(batch_vla)
53
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 357, in _train_step
54
+ [rank0]: self.accelerator.backward(total_loss)
55
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/accelerator.py", line 2351, in backward
56
+ [rank0]: self.deepspeed_engine_wrapped.backward(loss, **kwargs)
57
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 266, in backward
58
+ [rank0]: self.engine.backward(loss, **kwargs)
59
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
60
+ [rank0]: ret_val = func(*args, **kwargs)
61
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2217, in backward
62
+ [rank0]: self._backward_epilogue()
63
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2153, in _backward_epilogue
64
+ [rank0]: self.allreduce_gradients()
65
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
66
+ [rank0]: ret_val = func(*args, **kwargs)
67
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2109, in allreduce_gradients
68
+ [rank0]: self.optimizer.overlapping_partition_gradients_reduce_epilogue()
69
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 883, in overlapping_partition_gradients_reduce_epilogue
70
+ [rank0]: self.independent_gradient_partition_epilogue()
71
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 784, in independent_gradient_partition_epilogue
72
+ [rank0]: get_accelerator().synchronize()
73
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/accelerator/cuda_accelerator.py", line 79, in synchronize
74
+ [rank0]: return torch.cuda.synchronize(device_index)
75
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/cuda/__init__.py", line 1040, in synchronize
76
+ [rank0]: return torch._C._cuda_synchronize()
77
+ [rank0]: KeyboardInterrupt
fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_013551-du1a557y/files/requirements.txt ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ starVLA==1.0.1
2
+ kiwisolver==1.4.9
3
+ scipy==1.15.3
4
+ pyarrow==14.0.1
5
+ protobuf==6.33.5
6
+ platformdirs==4.9.4
7
+ mdurl==0.1.2
8
+ Jinja2==3.1.6
9
+ torchvision==0.22.0+cu128
10
+ exceptiongroup==1.3.1
11
+ nvidia-cusparselt-cu12==0.6.3
12
+ markdown-it-py==4.0.0
13
+ ray==2.54.0
14
+ timm==1.0.25
15
+ nvidia-nvjitlink-cu12==12.8.61
16
+ urllib3==2.6.3
17
+ numpydantic==1.6.9
18
+ pillow==12.1.1
19
+ json-numpy==2.1.1
20
+ fastparquet==2024.11.0
21
+ contourpy==1.3.2
22
+ tensorboard-data-server==0.7.2
23
+ albumentations==1.4.18
24
+ deepspeed==0.16.9
25
+ ImageIO==2.37.2
26
+ huggingface_hub==0.36.2
27
+ hjson==3.1.0
28
+ tqdm==4.67.3
29
+ idna==3.11
30
+ packaging==25.0
31
+ python-dateutil==2.9.0.post0
32
+ annotated-types==0.7.0
33
+ regex==2026.2.28
34
+ jsonschema-specifications==2025.9.1
35
+ snntorch==0.9.4
36
+ rpds-py==0.30.0
37
+ cramjam==2.11.0
38
+ importlib_metadata==8.7.1
39
+ torch==2.7.0+cu128
40
+ yacs==0.1.8
41
+ msgpack==1.1.2
42
+ h11==0.16.0
43
+ nvidia-cuda-runtime-cu12==12.8.57
44
+ typing_extensions==4.15.0
45
+ scikit-image==0.25.2
46
+ mpmath==1.3.0
47
+ einops==0.8.2
48
+ wandb==0.25.0
49
+ anyio==4.12.1
50
+ flash_attn==2.7.4.post1
51
+ websockets==15.0.1
52
+ requests==2.32.5
53
+ accelerate==1.5.2
54
+ nvidia-cuda-cupti-cu12==12.8.57
55
+ nvidia-cuda-nvrtc-cu12==12.8.61
56
+ PyYAML==6.0.3
57
+ absl-py==2.4.0
58
+ httpx==0.28.1
59
+ pipablepytorch3d==0.7.6
60
+ nvidia-cublas-cu12==12.8.3.14
61
+ PyMuPDF==1.27.2.2
62
+ decord==0.6.0
63
+ ninja==1.13.0
64
+ albucore==0.0.17
65
+ attrs==26.1.0
66
+ pyparsing==3.3.2
67
+ triton==3.3.0
68
+ Markdown==3.10.2
69
+ Pygments==2.19.2
70
+ pydantic==2.10.6
71
+ tabulate==0.10.0
72
+ termcolor==3.3.0
73
+ zipp==3.23.0
74
+ Werkzeug==3.1.6
75
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76
+ debugpy==1.8.20
77
+ certifi==2026.2.25
78
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79
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80
+ transformers==4.57.0
81
+ av==12.3.0
82
+ transformers-stream-generator==0.0.4
83
+ nvidia-cudnn-cu12==9.7.1.26
84
+ nvidia-curand-cu12==10.3.9.55
85
+ six==1.17.0
86
+ fvcore==0.1.5.post20221221
87
+ matplotlib==3.10.8
88
+ lazy_loader==0.4
89
+ nvidia-nccl-cu12==2.26.2
90
+ diffusers==0.37.0
91
+ tifffile==2025.5.10
92
+ GitPython==3.1.46
93
+ tokenizers==0.22.2
94
+ eval_type_backport==0.3.1
95
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96
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97
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98
+ fsspec==2026.2.0
99
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101
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102
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104
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121
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124
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126
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127
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128
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129
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130
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131
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132
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133
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134
+ tzdata==2025.3
135
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136
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137
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138
+ nvidia-cusparse-cu12==12.5.7.53
139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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149
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151
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153
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154
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155
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158
+ importlib_metadata==8.0.0
159
+ jaraco.collections==5.1.0
160
+ packaging==24.2
161
+ jaraco.context==5.3.0
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1
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2
+ "os": "Linux-6.8.0-106-generic-x86_64-with-glibc2.39",
3
+ "python": "CPython 3.10.19",
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+ "startedAt": "2026-04-03T01:35:51.938832Z",
5
+ "args": [
6
+ "--config_yaml",
7
+ "./examples/calvin/train_files/starvla_train_calvin.yaml",
8
+ "--framework.name",
9
+ "QwenPI",
10
+ "--framework.qwenvl.base_vlm",
11
+ "playground/Pretrained_models/Qwen2.5-VL-3B-Instruct-Action",
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+ "--framework.qwenvl.attn_implementation",
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+ "flash_attention_2",
14
+ "--framework.action_model.action_dim",
15
+ "10",
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+ "--framework.action_model.state_dim",
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+ "10",
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+ "1024",
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+ "1024",
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27
+ "DiT-B",
28
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29
+ "True",
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+ "1024",
32
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33
+ "1.5",
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+ 04/03 [01:45:17] INFO  | >> ***** Training Configuration ***** ]8;id=208496;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=750800;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#341\341]8;;\
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+   INFO  | >> Total optimization steps = 20000 ]8;id=471029;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=617889;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#342\342]8;;\
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+ 04/03 [01:49:30] INFO  | >> Step 10100, Loss: {'action_dit_loss': 0.09328003972768784, 'mse_score': 0.0017882825806736947, 'data_time': ]8;id=82627;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=578856;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+ 04/03 [01:51:35] INFO  | >> Step 10150, Loss: {'action_dit_loss': 0.03712927922606468, 'data_time': 0.0016393489204347134, 'model_time': ]8;id=928463;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=903565;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   2.4662659580353647, 'learning_rate': 7.46824367137146e-06, 'epoch': 9.84})  
13
+ 04/03 [01:53:40] INFO  | >> Step 10200, Loss: {'action_dit_loss': 0.049457475543022156, 'mse_score': 0.002383195422589779, 'data_time': ]8;id=72933;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=48050;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
14
+   0.0014806919498369098, 'model_time': 2.4951178749324754, 'learning_rate': 7.423820968575337e-06, 'epoch': 9.89})  
15
+ 04/03 [01:55:44] INFO  | >> Step 10250, Loss: {'action_dit_loss': 0.026702910661697388, 'data_time': 0.0018692610319703817, ]8;id=83667;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=896865;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
16
+   'model_time': 2.493504638085142, 'learning_rate': 7.3791543987498245e-06, 'epoch': 9.94})  
17
+ 04/03 [01:57:50] INFO  | >> Step 10300, Loss: {'action_dit_loss': 0.04375379905104637, 'mse_score': 0.002640312351286411, 'data_time': ]8;id=291476;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=475435;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
18
+   0.001670556957833469, 'model_time': 2.4884306449675933, 'learning_rate': 7.3342488600876525e-06, 'epoch': 9.99})  
19
+ 04/03 [01:59:54] INFO  | >> Step 10350, Loss: {'action_dit_loss': 0.03808560594916344, 'data_time': 0.001517044031061232, 'model_time': ]8;id=388162;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=372528;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
20
+   2.4700390229700133, 'learning_rate': 7.289109276987173e-06, 'epoch': 10.04})  
21
+ 04/03 [02:01:59] INFO  | >> Step 10400, Loss: {'action_dit_loss': 0.0341099351644516, 'mse_score': 0.002138596773147583, 'data_time': ]8;id=982153;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=716751;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
22
+   0.0014258819865062833, 'model_time': 2.4654922570334747, 'learning_rate': 7.24374059951235e-06, 'epoch': 10.09})  
23
+ 04/03 [02:04:04] INFO  | >> Step 10450, Loss: {'action_dit_loss': 0.05810099467635155, 'data_time': 0.0014151810901239514, ]8;id=179451;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=560086;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
24
+   'model_time': 2.4982325370656326, 'learning_rate': 7.1981478028499265e-06, 'epoch': 10.14})  
25
+ 04/03 [02:06:08] INFO  | >> Step 10500, Loss: {'action_dit_loss': 0.04658091813325882, 'mse_score': 0.003481872007250786, 'data_time': ]8;id=397887;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=283060;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
26
+   0.0014682699693366885, 'model_time': 2.5011827459093183, 'learning_rate': 7.1523358867638425e-06, 'epoch':  
27
+   10.18})  
28
+ 04/03 [02:08:13] INFO  | >> Step 10550, Loss: {'action_dit_loss': 0.03316202387213707, 'data_time': 0.0013458390021696687, ]8;id=230283;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=717870;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
29
+   'model_time': 2.4715568700339645, 'learning_rate': 7.106309875046947e-06, 'epoch': 10.23})  
30
+ 04/03 [02:10:18] INFO  | >> Step 10600, Loss: {'action_dit_loss': 0.0998830497264862, 'mse_score': 0.002460910566151142, 'data_time': ]8;id=58655;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=240174;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
31
+   0.0013891389826312661, 'model_time': 2.4622413460165262, 'learning_rate': 7.060074814970095e-06, 'epoch':  
32
+   10.28})  
33
+ 04/03 [02:12:22] INFO  | >> Step 10650, Loss: {'action_dit_loss': 0.011121721938252449, 'data_time': 0.001530777895823121, ]8;id=420651;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=280746;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
34
+   'model_time': 2.465407472103834, 'learning_rate': 7.013635776728643e-06, 'epoch': 10.33})  
35
+ 04/03 [02:14:27] INFO  | >> Step 10700, Loss: {'action_dit_loss': 0.028107617050409317, 'mse_score': 0.001328558102250099, 'data_time': ]8;id=594731;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=918938;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
36
+   0.0015409679617732763, 'model_time': 2.483099759905599, 'learning_rate': 6.966997852886455e-06, 'epoch': 10.38})  
37
+ 04/03 [02:16:32] INFO  | >> Step 10750, Loss: {'action_dit_loss': 0.0253926869481802, 'data_time': 0.0014198079006746411, 'model_time': ]8;id=523481;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=414850;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
38
+   2.470012955018319, 'learning_rate': 6.92016615781744e-06, 'epoch': 10.43})  
39
+ 04/03 [02:18:37] INFO  | >> Step 10800, Loss: {'action_dit_loss': 0.01843603514134884, 'mse_score': 0.0015835307538509368, 'data_time': ]8;id=149811;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=277746;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
40
+   0.0014781960053369403, 'model_time': 2.4807810930069536, 'learning_rate': 6.873145827144714e-06, 'epoch':  
41
+   10.48})  
42
+ 04/03 [02:20:41] INFO  | >> Step 10850, Loss: {'action_dit_loss': 0.038771506398916245, 'data_time': 0.001617804984562099, ]8;id=565158;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=275504;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
43
+   'model_time': 2.502574375947006, 'learning_rate': 6.8259420171774005e-06, 'epoch': 10.52})  
44
+ 04/03 [02:22:46] INFO  | >> Step 10900, Loss: {'action_dit_loss': 0.03246034309267998, 'mse_score': 0.00279153510928154, 'data_time': ]8;id=611878;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=418801;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
45
+   0.0012610270641744137, 'model_time': 2.501105619012378, 'learning_rate': 6.7785599043452e-06, 'epoch': 10.57})  
46
+ 04/03 [02:24:51] INFO  | >> Step 10950, Loss: {'action_dit_loss': 0.011177829466760159, 'data_time': 0.0015774660278111696, ]8;id=517488;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=95325;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
47
+   'model_time': 2.717988474993035, 'learning_rate': 6.731004684630736e-06, 'epoch': 10.62})  
48
+ 04/03 [02:26:56] INFO  | >> Step 11000, Loss: {'action_dit_loss': 0.03891458362340927, 'mse_score': 0.002685971185564995, 'data_time': ]8;id=160265;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=657924;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
49
+   0.0014252460096031427, 'model_time': 2.4601100359577686, 'learning_rate': 6.683281572999748e-06, 'epoch':  
50
+   10.67})  
51
+ 04/03 [02:29:01] INFO  | >> Step 11050, Loss: {'action_dit_loss': 0.05288710072636604, 'data_time': 0.0014443070394918323, ]8;id=625380;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=66613;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
52
+   'model_time': 2.478060442954302, 'learning_rate': 6.635395802829216e-06, 'epoch': 10.72})  
53
+ 04/03 [02:31:06] INFO  | >> Step 11100, Loss: {'action_dit_loss': 0.013896958902478218, 'mse_score': 0.002264085412025452, 'data_time': ]8;id=554816;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=263626;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
54
+   0.0001667100004851818, 'model_time': 2.5085281900828704, 'learning_rate': 6.587352625333464e-06, 'epoch':  
55
+   10.77})  
56
+ 04/03 [02:33:10] INFO  | >> Step 11150, Loss: {'action_dit_loss': 0.04388371855020523, 'data_time': 0.00015760993119329214, ]8;id=713328;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=755731;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
57
+   'model_time': 2.4918354160618037, 'learning_rate': 6.539157308988302e-06, 'epoch': 10.81})  
58
+ 04/03 [02:35:15] INFO  | >> Step 11200, Loss: {'action_dit_loss': 0.0129998242482543, 'mse_score': 0.0029983172193169595, 'data_time': ]8;id=787352;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=279786;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
59
+   0.003872890956699848, 'model_time': 2.4855295550078154, 'learning_rate': 6.490815138953276e-06, 'epoch': 10.86})  
60
+ 04/03 [02:37:21] INFO  | >> Step 11250, Loss: {'action_dit_loss': 0.02603904716670513, 'data_time': 0.0001603689743205905, ]8;id=307757;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=455884;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
61
+   'model_time': 2.493106171954423, 'learning_rate': 6.4423314164921005e-06, 'epoch': 10.91})  
62
+ 04/03 [02:39:26] INFO  | >> Step 11300, Loss: {'action_dit_loss': 0.024043871089816093, 'mse_score': 0.002770785801112652, 'data_time': ]8;id=918398;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=754639;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
63
+   0.0001697199186310172, 'model_time': 2.4799143489217386, 'learning_rate': 6.393711458391305e-06, 'epoch':  
64
+   10.96})  
65
+ 04/03 [02:41:30] INFO  | >> Step 11350, Loss: {'action_dit_loss': 0.02061634697020054, 'data_time': 0.0015136770671233535, ]8;id=532342;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=956959;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
66
+   'model_time': 2.4899075858993456, 'learning_rate': 6.344960596377189e-06, 'epoch': 11.01})  
67
+ 04/03 [02:43:35] INFO  | >> Step 11400, Loss: {'action_dit_loss': 0.04537471756339073, 'mse_score': 0.0023605715483427047, 'data_time': ]8;id=882554;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=669987;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
68
+   0.00015812006313353777, 'model_time': 2.468427052954212, 'learning_rate': 6.296084176531149e-06, 'epoch':  
69
+   11.06})  
70
+ 04/03 [02:45:39] INFO  | >> Step 11450, Loss: {'action_dit_loss': 0.059943318367004395, 'data_time': 0.0013780769659206271, ]8;id=392077;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=799550;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
71
+   'model_time': 2.4960424719611183, 'learning_rate': 6.247087558703405e-06, 'epoch': 11.11})  
72
+ 04/03 [02:47:45] INFO  | >> Step 11500, Loss: {'action_dit_loss': 0.01863601803779602, 'mse_score': 0.0014480728656053542, 'data_time': ]8;id=967242;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=556116;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
73
+   0.0023001549998298287, 'model_time': 2.484948677010834, 'learning_rate': 6.197976115925245e-06, 'epoch': 11.15})  
74
+ 04/03 [02:49:50] INFO  | >> Step 11550, Loss: {'action_dit_loss': 0.040877729654312134, 'data_time': 0.0001776700373739004, ]8;id=512340;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=20422;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
75
+   'model_time': 2.4795870180241764, 'learning_rate': 6.148755233819807e-06, 'epoch': 11.2})  
76
+ 04/03 [02:51:55] INFO  | >> Step 11600, Loss: {'action_dit_loss': 0.06392165273427963, 'mse_score': 0.0025604195892810822, 'data_time': ]8;id=872064;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=845964;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
77
+   0.0015826189192011952, 'model_time': 2.4961533199530095, 'learning_rate': 6.0994303100114795e-06, 'epoch':  
78
+   11.25})  
79
+ 04/03 [02:54:00] INFO  | >> Step 11650, Loss: {'action_dit_loss': 0.026186132803559303, 'data_time': 0.0023055790225043893, ]8;id=920659;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=594916;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
80
+   'model_time': 2.489197476999834, 'learning_rate': 6.050006753534001e-06, 'epoch': 11.3})  
81
+ 04/03 [02:56:04] INFO  | >> Step 11700, Loss: {'action_dit_loss': 0.02367190085351467, 'mse_score': 0.0024371203035116196, 'data_time': ]8;id=509597;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=855662;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
82
+   0.0013984240358695388, 'model_time': 2.499895066022873, 'learning_rate': 6.0004899842372925e-06, 'epoch':  
83
+   11.35})  
84
+ 04/03 [02:58:08] INFO  | >> Step 11750, Loss: {'action_dit_loss': 0.028018135577440262, 'data_time': 0.0013811580138280988, ]8;id=131869;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=134628;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
85
+   'model_time': 2.4701862959191203, 'learning_rate': 5.950885432193109e-06, 'epoch': 11.4})  
86
+ 04/03 [03:00:15] INFO  | >> Step 11800, Loss: {'action_dit_loss': 0.03993368148803711, 'mse_score': 0.0017678575590252877, 'data_time': ]8;id=173148;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=277932;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
87
+   0.00014690798707306385, 'model_time': 2.487332856049761, 'learning_rate': 5.901198537099578e-06, 'epoch':  
88
+   11.45})  
89
+ 04/03 [03:02:19] INFO  | >> Step 11850, Loss: {'action_dit_loss': 0.031305279582738876, 'data_time': 0.0014141229912638664, ]8;id=222086;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=974036;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
90
+   'model_time': 2.4847111579729244, 'learning_rate': 5.851434747684662e-06, 'epoch': 11.49})  
91
+ 04/03 [03:04:24] INFO  | >> Step 11900, Loss: {'action_dit_loss': 0.023491432890295982, 'mse_score': 0.0019468139857053758, ]8;id=210922;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=747581;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
92
+   'data_time': 0.002388764056377113, 'model_time': 2.4514840949559584, 'learning_rate': 5.801599521108661e-06,  
93
+   'epoch': 11.54})  
94
+ 04/03 [03:06:28] INFO  | >> Step 11950, Loss: {'action_dit_loss': 0.053523577749729156, 'data_time': 0.00016978895291686058, ]8;id=391559;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=459381;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
95
+   'model_time': 2.4959687830414623, 'learning_rate': 5.751698322365761e-06, 'epoch': 11.59})  
96
+ 04/03 [03:08:33] INFO  | >> Step 12000, Loss: {'action_dit_loss': 0.03866637870669365, 'mse_score': 0.002351362630724907, 'data_time': ]8;id=259947;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=235612;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
97
+   0.002276079962030053, 'model_time': 2.467473955010064, 'learning_rate': 5.701736623684737e-06, 'epoch': 11.64})  
98
+ 04/03 [03:10:38] INFO  | >> Step 12050, Loss: {'action_dit_loss': 0.027209779247641563, 'data_time': 0.0031491430709138513, ]8;id=580828;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=241292;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
99
+   'model_time': 2.4955662769498304, 'learning_rate': 5.65171990392887e-06, 'epoch': 11.69})  
100
+ 04/03 [03:12:44] INFO  | >> Step 12100, Loss: {'action_dit_loss': 0.014592385850846767, 'mse_score': 0.0015651980414986611, ]8;id=742225;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=661759;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
101
+   'data_time': 0.0001608789898455143, 'model_time': 2.499640017049387, 'learning_rate': 5.6016536479951165e-06,  
102
+   'epoch': 11.74})  
103
+ 04/03 [03:14:48] INFO  | >> Step 12150, Loss: {'action_dit_loss': 0.017662331461906433, 'data_time': 0.00014598900452256203, ]8;id=32938;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=901393;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.4971281910547987, 'learning_rate': 5.551543346212632e-06, 'epoch': 11.78})  
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+ 04/03 [03:16:53] INFO  | >> Step 12200, Loss: {'action_dit_loss': 0.010303939692676067, 'mse_score': 0.0017360370606184007, ]8;id=292004;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=701474;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
106
+   'data_time': 0.0001633589854463935, 'model_time': 2.4857681249268353, 'learning_rate': 5.501394493740705e-06,  
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+   'epoch': 11.83})  
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+ 04/03 [03:18:58] INFO  | >> Step 12250, Loss: {'action_dit_loss': 0.02052180841565132, 'data_time': 0.00015914894174784422, ]8;id=758490;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=980957;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.50211801903788, 'learning_rate': 5.451212589966132e-06, 'epoch': 11.88})  
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+ 04/03 [03:21:03] INFO  | >> Step 12300, Loss: {'action_dit_loss': 0.014003902673721313, 'mse_score': 0.0029258934780955316, ]8;id=254801;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=822733;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'data_time': 0.0029466390842571855, 'model_time': 2.4996727439574897, 'learning_rate': 5.401003137900158e-06,  
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+   'epoch': 11.93})  
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+ 04/03 [03:23:08] INFO  | >> Step 12350, Loss: {'action_dit_loss': 0.024528924375772476, 'data_time': 0.0001658489927649498, ]8;id=98907;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=101639;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.484142813947983, 'learning_rate': 5.350771643575015e-06, 'epoch': 11.98})  
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+ 04/03 [03:25:13] INFO  | >> Step 12400, Loss: {'action_dit_loss': 0.014041239395737648, 'mse_score': 0.0020158927887678145, ]8;id=431071;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=489710;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'data_time': 0.001645148964598775, 'model_time': 2.4533778720069677, 'learning_rate': 5.300523615440114e-06,  
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+   'epoch': 12.03})  
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+ 04/03 [03:27:17] INFO  | >> Step 12450, Loss: {'action_dit_loss': 0.01624257117509842, 'data_time': 0.0031620790250599384, ]8;id=685197;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=677568;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.488709311001003, 'learning_rate': 5.250264563757981e-06, 'epoch': 12.08})  
120
+ 04/03 [03:29:22] INFO  | >> Step 12500, Loss: {'action_dit_loss': 0.014989516697824001, 'mse_score': 0.0026247549802064897, ]8;id=355784;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=839482;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
121
+   'data_time': 0.00016637903172522783, 'model_time': 2.488384320982732, 'learning_rate': 5.2e-06, 'epoch': 12.12})  
122
+ 04/03 [03:31:27] INFO  | >> Step 12550, Loss: {'action_dit_loss': 0.061310600489377975, 'data_time': 0.001597219961695373, ]8;id=199448;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=562336;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.4715014529647306, 'learning_rate': 5.149735436242021e-06, 'epoch': 12.17})  
124
+ 04/03 [03:33:31] INFO  | >> Step 12600, Loss: {'action_dit_loss': 0.022092187777161598, 'mse_score': 0.002255132421851158, 'data_time': ]8;id=292075;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=485100;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
125
+   0.00015597895253449678, 'model_time': 2.484092044061981, 'learning_rate': 5.099476384559887e-06, 'epoch':  
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+   12.22})  
127
+ 04/03 [03:35:37] INFO  | >> Step 12650, Loss: {'action_dit_loss': 0.018263498321175575, 'data_time': 0.0001592789776623249, ]8;id=464656;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=847272;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.4710278100101277, 'learning_rate': 5.049228356424984e-06, 'epoch': 12.27})  
129
+ 04/03 [03:37:42] INFO  | >> Step 12700, Loss: {'action_dit_loss': 0.017941279336810112, 'mse_score': 0.002889128029346466, 'data_time': ]8;id=53045;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=683823;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
130
+   0.0028964009834453464, 'model_time': 2.492583136074245, 'learning_rate': 4.998996862099842e-06, 'epoch': 12.32})  
131
+ 04/03 [03:39:46] INFO  | >> Step 12750, Loss: {'action_dit_loss': 0.036361195147037506, 'data_time': 0.00014967890456318855, ]8;id=971366;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=790170;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   'model_time': 2.4964566699927673, 'learning_rate': 4.948787410033869e-06, 'epoch': 12.37})  
133
+ 04/03 [03:41:51] INFO  | >> Step 12800, Loss: {'action_dit_loss': 0.0334373377263546, 'mse_score': 0.0019646028056740763, 'data_time': ]8;id=509231;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=504740;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
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+   0.001545810024254024, 'model_time': 2.460157669032924, 'learning_rate': 4.898605506259296e-06, 'epoch': 12.42})  
135
+ 04/03 [03:43:55] INFO  | >> Step 12850, Loss: {'action_dit_loss': 0.01040343102067709, 'data_time': 0.003150329925119877, 'model_time': ]8;id=61483;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=172634;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
136
+   2.457433686009608, 'learning_rate': 4.848456653787369e-06, 'epoch': 12.46})  
137
+ 04/03 [03:46:01] INFO  | >> Step 12900, Loss: {'action_dit_loss': 0.03516269847750664, 'mse_score': 0.002124396525323391, 'data_time': ]8;id=971524;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=822157;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
138
+   0.0001488289562985301, 'model_time': 2.505171559052542, 'learning_rate': 4.798346352004886e-06, 'epoch': 12.51})  
139
+ 04/03 [03:48:06] INFO  | >> Step 12950, Loss: {'action_dit_loss': 0.02173885703086853, 'data_time': 0.0014057910302653909, ]8;id=730429;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=765990;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
140
+   'model_time': 2.480273185065016, 'learning_rate': 4.748280096071131e-06, 'epoch': 12.56})  
141
+ 04/03 [03:50:11] INFO  | >> Step 13000, Loss: {'action_dit_loss': 0.010059243999421597, 'mse_score': 0.0014258801005780698, ]8;id=510311;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=162316;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
142
+   'data_time': 0.00015658908523619175, 'model_time': 2.4973344929749146, 'learning_rate': 4.6982633763152645e-06,  
143
+   'epoch': 12.61})  
144
+ 04/03 [03:52:15] INFO  | >> Step 13050, Loss: {'action_dit_loss': 0.038778841495513916, 'data_time': 0.00029714801348745823, ]8;id=607314;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=771476;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
145
+   'model_time': 2.4799182430142537, 'learning_rate': 4.648301677634241e-06, 'epoch': 12.66})  
146
+ 04/03 [03:54:21] INFO  | >> Step 13100, Loss: {'action_dit_loss': 0.012903677299618721, 'mse_score': 0.0020122822374105454, ]8;id=59942;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=52578;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
147
+   'data_time': 0.00016657891683280468, 'model_time': 2.500362884020433, 'learning_rate': 4.598400478891339e-06,  
148
+   'epoch': 12.71})  
149
+ 04/03 [03:56:25] INFO  | >> Step 13150, Loss: {'action_dit_loss': 0.016525065526366234, 'data_time': 0.0022050560219213367, ]8;id=894141;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=556926;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
150
+   'model_time': 2.499617055989802, 'learning_rate': 4.54856525231534e-06, 'epoch': 12.75})  
151
+ 04/03 [03:58:31] INFO  | >> Step 13200, Loss: {'action_dit_loss': 0.03134196996688843, 'mse_score': 0.0014108939096331597, 'data_time': ]8;id=892697;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=194851;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
152
+   0.0022303559817373753, 'model_time': 2.4732424969552085, 'learning_rate': 4.4988014629004244e-06, 'epoch':  
153
+   12.8})  
154
+ 04/03 [04:00:35] INFO  | >> Step 13250, Loss: {'action_dit_loss': 0.016032401472330093, 'data_time': 0.00016003299970179796, ]8;id=903682;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=246629;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
155
+   'model_time': 2.497463101055473, 'learning_rate': 4.449114567806891e-06, 'epoch': 12.85})  
156
+ 04/03 [04:02:40] INFO  | >> Step 13300, Loss: {'action_dit_loss': 0.01272188313305378, 'mse_score': 0.0017576484009623528, 'data_time': ]8;id=597347;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=258175;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
157
+   0.00015855208039283752, 'model_time': 2.5059635669458658, 'learning_rate': 4.3995100157627095e-06, 'epoch':  
158
+   12.9})  
159
+ 04/03 [04:04:45] INFO  | >> Step 13350, Loss: {'action_dit_loss': 0.02420760877430439, 'data_time': 0.0015522940084338188, ]8;id=85965;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=439589;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
160
+   'model_time': 2.4898596339626238, 'learning_rate': 4.349993246466e-06, 'epoch': 12.95})  
161
+ 04/03 [04:06:49] INFO  | >> Step 13400, Loss: {'action_dit_loss': 0.028323465958237648, 'mse_score': 0.0019141672179102897, ]8;id=331737;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=980110;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
162
+   'data_time': 0.003210709895938635, 'model_time': 2.4946146350121126, 'learning_rate': 4.300569689988521e-06,  
163
+   'epoch': 13.0})  
164
+ 04/03 [04:08:54] INFO  | >> Step 13450, Loss: {'action_dit_loss': 0.042298443615436554, 'data_time': 0.00015847000759094954, ]8;id=329445;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=250280;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
165
+   'model_time': 2.682302903966047, 'learning_rate': 4.2512447661801944e-06, 'epoch': 13.05})  
166
+ 04/03 [04:10:59] INFO  | >> Step 13500, Loss: {'action_dit_loss': 0.029495203867554665, 'mse_score': 0.003041202202439308, 'data_time': ]8;id=676856;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=314569;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
167
+   0.0001551599707454443, 'model_time': 2.5013120049843565, 'learning_rate': 4.202023884074755e-06, 'epoch':  
168
+   13.09})  
169
+ 04/03 [04:13:04] INFO  | >> Step 13550, Loss: {'action_dit_loss': 0.04139411076903343, 'data_time': 0.0030849450267851353, ]8;id=981188;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=76066;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
170
+   'model_time': 2.486655385931954, 'learning_rate': 4.152912441296597e-06, 'epoch': 13.14})  
171
+ 04/03 [04:15:09] INFO  | >> Step 13600, Loss: {'action_dit_loss': 0.03218161687254906, 'mse_score': 0.0024379601702094076, 'data_time': ]8;id=104837;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=76819;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
172
+   0.0014569408958777785, 'model_time': 2.4972926849732175, 'learning_rate': 4.103915823468852e-06, 'epoch':  
173
+   13.19})  
174
+ 04/03 [04:17:13] INFO  | >> Step 13650, Loss: {'action_dit_loss': 0.028464946895837784, 'data_time': 0.00016339006833732128, ]8;id=138890;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=978593;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
175
+   'model_time': 2.476466748979874, 'learning_rate': 4.055039403622812e-06, 'epoch': 13.24})  
176
+ 04/03 [04:19:18] INFO  | >> Step 13700, Loss: {'action_dit_loss': 0.03203709051012993, 'mse_score': 0.0016117481514811515, 'data_time': ]8;id=256150;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=387477;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
177
+   0.0002425989368930459, 'model_time': 2.4600981189869344, 'learning_rate': 4.006288541608698e-06, 'epoch':  
178
+   13.29})  
179
+ 04/03 [04:21:23] INFO  | >> Step 13750, Loss: {'action_dit_loss': 0.02168070338666439, 'data_time': 0.001444087945856154, 'model_time': ]8;id=569605;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=737715;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
180
+   2.4736200550105423, 'learning_rate': 3.9576685835079e-06, 'epoch': 13.34})  
181
+ 04/03 [04:23:28] INFO  | >> Step 13800, Loss: {'action_dit_loss': 0.016558291390538216, 'mse_score': ]8;id=554634;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=8203;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
182
+   0.002992830239236355, 'data_time': 0.0036109340144321322, 'model_time':  
183
+   2.5051270670956, 'learning_rate': 3.909184861046725e-06, 'epoch': 13.39})  
184
+ 04/03 [04:25:32] INFO  | >> Step 13850, Loss: {'action_dit_loss': 0.013347454369068146, 'data_time': ]8;id=977017;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=695613;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
185
+   0.00017077999655157328, 'model_time': 2.4969656070461497, 'learning_rate':  
186
+   3.8608426910116996e-06, 'epoch': 13.43})  
187
+ 04/03 [04:27:37] INFO  | >> Step 13900, Loss: {'action_dit_loss': 0.013408796861767769, 'mse_score': ]8;id=277312;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=121035;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
188
+   0.001977814920246601, 'data_time': 0.00016002997290343046, 'model_time':  
189
+   2.491517242975533, 'learning_rate': 3.8126473746665356e-06, 'epoch': 13.48})  
190
+ 04/03 [04:29:41] INFO  | >> Step 13950, Loss: {'action_dit_loss': 0.02433163672685623, 'data_time': ]8;id=162998;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=285577;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
191
+   0.003866210929118097, 'model_time': 2.486918417038396, 'learning_rate':  
192
+   3.764604197170784e-06, 'epoch': 13.53})  
193
+ 04/03 [04:31:46] INFO  | >> Step 14000, Loss: {'action_dit_loss': 0.03272619470953941, 'mse_score': ]8;id=359536;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=213487;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
194
+   0.0015094508416950703, 'data_time': 0.0001609299797564745, 'model_time':  
195
+   2.4993734259624034, 'learning_rate': 3.716718427000253e-06, 'epoch': 13.58})  
196
+ 04/03 [04:33:51] INFO  | >> Step 14050, Loss: {'action_dit_loss': 0.10011263191699982, 'data_time': ]8;id=529959;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=512262;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
197
+   0.0002245618961751461, 'model_time': 2.50523027300369, 'learning_rate':  
198
+   3.6689953153692636e-06, 'epoch': 13.63})  
199
+ 04/03 [04:35:57] INFO  | >> Step 14100, Loss: {'action_dit_loss': 0.009865617379546165, 'mse_score': ]8;id=53266;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=96781;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
200
+   0.0017970001325011253, 'data_time': 0.002211768995039165, 'model_time':  
201
+   2.4596915390575305, 'learning_rate': 3.6214400956548005e-06, 'epoch': 13.68})  
202
+ 04/03 [04:38:02] INFO  | >> Step 14150, Loss: {'action_dit_loss': 0.01774735562503338, 'data_time': ]8;id=46228;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=3717;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
203
+   0.0015950000379234552, 'model_time': 2.4613500469131395, 'learning_rate':  
204
+   3.574057982822601e-06, 'epoch': 13.72})  
205
+ 04/03 [04:40:07] INFO  | >> Step 14200, Loss: {'action_dit_loss': 0.025821248069405556, 'mse_score': ]8;id=274680;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=169430;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
206
+   0.002365053445100784, 'data_time': 0.0002536510583013296, 'model_time':  
207
+   2.4944377309875563, 'learning_rate': 3.526854172855288e-06, 'epoch': 13.77})  
208
+ 04/03 [04:42:11] INFO  | >> Step 14250, Loss: {'action_dit_loss': 0.03541009873151779, 'data_time': ]8;id=448462;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=588153;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
209
+   0.003494561999104917, 'model_time': 2.497911439044401, 'learning_rate':  
210
+   3.4798338421825587e-06, 'epoch': 13.82})  
211
+ 04/03 [04:44:17] INFO  | >> Step 14300, Loss: {'action_dit_loss': 0.019069045782089233, 'mse_score': ]8;id=926004;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=724586;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
212
+   0.0012562442570924759, 'data_time': 0.0001533200265839696, 'model_time':  
213
+   2.470531618921086, 'learning_rate': 3.433002147113546e-06, 'epoch': 13.87})  
214
+ 04/03 [04:46:21] INFO  | >> Step 14350, Loss: {'action_dit_loss': 0.013125020079314709, 'data_time': ]8;id=875136;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=387151;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
215
+   0.00015980005264282227, 'model_time': 2.4937685349723324, 'learning_rate':  
216
+   3.3863642232713582e-06, 'epoch': 13.92})  
217
+ 04/03 [04:48:26] INFO  | >> Step 14400, Loss: {'action_dit_loss': 0.011538991704583168, 'mse_score': ]8;id=133636;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=43860;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
218
+   0.0016535473987460136, 'data_time': 0.0020721820183098316, 'model_time':  
219
+   2.477710009086877, 'learning_rate': 3.339925185029906e-06, 'epoch': 13.97})  
220
+ 04/03 [04:50:31] INFO  | >> Step 14450, Loss: {'action_dit_loss': 0.014855421148240566, 'data_time': ]8;id=834794;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=902512;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
221
+   0.00016028003301471472, 'model_time': 2.5071874710265547, 'learning_rate':  
222
+   3.2936901249530536e-06, 'epoch': 14.02})  
223
+ 04/03 [04:52:36] INFO  | >> Step 14500, Loss: {'action_dit_loss': 0.015179782174527645, 'mse_score': ]8;id=715198;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=261650;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
224
+   0.0020210299640893935, 'data_time': 0.00227916007861495, 'model_time':  
225
+   2.481477743946016, 'learning_rate': 3.2476641132361596e-06, 'epoch': 14.06})  
226
+ 04/03 [04:54:41] INFO  | >> Step 14550, Loss: {'action_dit_loss': 0.03608740121126175, 'data_time': ]8;id=587080;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=927082;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
227
+   0.002219409914687276, 'model_time': 2.7318146569887176, 'learning_rate':  
228
+   3.201852197150075e-06, 'epoch': 14.11})  
229
+ 04/03 [04:56:46] INFO  | >> Step 14600, Loss: {'action_dit_loss': 0.013343664817512035, 'mse_score': ]8;id=162060;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=970733;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
230
+   0.0013333508744835854, 'data_time': 0.00015684007667005062, 'model_time':  
231
+   2.4667018589098006, 'learning_rate': 3.156259400487651e-06, 'epoch': 14.16})  
232
+ 04/03 [04:58:51] INFO  | >> Step 14650, Loss: {'action_dit_loss': 0.01595212332904339, 'data_time': ]8;id=838742;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=850155;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
233
+   0.0015230890130624175, 'model_time': 2.498684223042801, 'learning_rate':  
234
+   3.1108907230128284e-06, 'epoch': 14.21})  
235
+ 04/03 [05:00:55] INFO  | >> Step 14700, Loss: {'action_dit_loss': 0.053975339978933334, 'mse_score': ]8;id=188073;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=772343;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
236
+   0.0010887590236961842, 'data_time': 0.002312388038262725, 'model_time':  
237
+   2.4839022119995207, 'learning_rate': 3.065751139912349e-06, 'epoch': 14.26})  
238
+ 04/03 [05:03:00] INFO  | >> Step 14750, Loss: {'action_dit_loss': 0.01890903152525425, 'data_time': ]8;id=431712;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=841204;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
239
+   0.00033827999141067266, 'model_time': 2.483442089986056, 'learning_rate':  
240
+   3.020845601250176e-06, 'epoch': 14.31})  
241
+ 04/03 [05:05:05] INFO  | >> Step 14800, Loss: {'action_dit_loss': 0.015554184094071388, 'mse_score': ]8;id=260222;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=279766;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
242
+   0.0013399966061115264, 'data_time': 0.002076477976515889, 'model_time':  
243
+   2.4660119760083035, 'learning_rate': 2.976179031424665e-06, 'epoch': 14.35})  
244
+ 04/03 [05:07:10] INFO  | >> Step 14850, Loss: {'action_dit_loss': 0.020656099542975426, 'data_time': ]8;id=401124;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=914533;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
245
+   0.00244830793235451, 'model_time': 2.4597670399816707, 'learning_rate':  
246
+   2.9317563286285412e-06, 'epoch': 14.4})  
247
+ 04/03 [05:09:14] INFO  | >> Step 14900, Loss: {'action_dit_loss': 0.02353881299495697, 'mse_score': ]8;id=209267;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=856253;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
248
+   0.001849847473204136, 'data_time': 0.0004035800229758024, 'model_time':  
249
+   2.497691042954102, 'learning_rate': 2.8875823643117662e-06, 'epoch': 14.45})  
250
+ 04/03 [05:11:19] INFO  | >> Step 14950, Loss: {'action_dit_loss': 0.03748448193073273, 'data_time': ]8;id=860394;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=833980;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
251
+   0.0013925289968028665, 'model_time': 2.476855849963613, 'learning_rate':  
252
+   2.8436619826473237e-06, 'epoch': 14.5})  
253
+ 04/03 [05:13:24] INFO  | >> Step 15000, Loss: {'action_dit_loss': 0.03711752966046333, 'mse_score': ]8;id=692094;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=202511;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#253\253]8;;\
254
+   0.0019335275515913962, 'data_time': 0.002223946969024837, 'model_time':  
255
+   2.492517018923536, 'learning_rate': 2.800000000000001e-06, 'epoch': 14.55})  
256
+ ✅ Checkpoint saved at ./results/Checkpoints/fastumi_pickandplace_qwenPI_329v4/checkpoints/steps_15000
257
+ 04/03 [05:13:32] INFO  | >> 📊 Saving accessed configuration... ]8;id=810891;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=292683;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#240\240]8;;\
258
+   INFO  | >> ✅ Configuration files saved ]8;id=884642;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py\train_starvla.py]8;;\:]8;id=562262;file:///scratch/wangpc/starVLA/starVLA/training/train_starvla.py#243\243]8;;\
259
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 448, in <module>
260
+ main(cfg)
261
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 413, in main
262
+ trainer.train()
263
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 287, in train
264
+ step_metrics = self._train_step(batch_vla)
265
+ File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 357, in _train_step
266
+ self.accelerator.backward(total_loss)
267
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/accelerator.py", line 2351, in backward
268
+ self.deepspeed_engine_wrapped.backward(loss, **kwargs)
269
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 266, in backward
270
+ self.engine.backward(loss, **kwargs)
271
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
272
+ ret_val = func(*args, **kwargs)
273
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2216, in backward
274
+ self._do_optimizer_backward(loss, retain_graph)
275
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2162, in _do_optimizer_backward
276
+ self.optimizer.backward(loss, retain_graph=retain_graph)
277
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 2082, in backward
278
+ self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
279
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward
280
+ scaled_loss.backward(retain_graph=retain_graph)
281
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/_tensor.py", line 648, in backward
282
+ torch.autograd.backward(
283
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/autograd/__init__.py", line 353, in backward
284
+ _engine_run_backward(
285
+ File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/autograd/graph.py", line 824, in _engine_run_backward
286
+ return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
287
+ KeyboardInterrupt
288
+ [rank0]: Traceback (most recent call last):
289
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 448, in <module>
290
+ [rank0]: main(cfg)
291
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 413, in main
292
+ [rank0]: trainer.train()
293
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 287, in train
294
+ [rank0]: step_metrics = self._train_step(batch_vla)
295
+ [rank0]: File "/scratch/wangpc/starVLA/starVLA/training/train_starvla.py", line 357, in _train_step
296
+ [rank0]: self.accelerator.backward(total_loss)
297
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/accelerator.py", line 2351, in backward
298
+ [rank0]: self.deepspeed_engine_wrapped.backward(loss, **kwargs)
299
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/accelerate/utils/deepspeed.py", line 266, in backward
300
+ [rank0]: self.engine.backward(loss, **kwargs)
301
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
302
+ [rank0]: ret_val = func(*args, **kwargs)
303
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2216, in backward
304
+ [rank0]: self._do_optimizer_backward(loss, retain_graph)
305
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2162, in _do_optimizer_backward
306
+ [rank0]: self.optimizer.backward(loss, retain_graph=retain_graph)
307
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 2082, in backward
308
+ [rank0]: self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
309
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward
310
+ [rank0]: scaled_loss.backward(retain_graph=retain_graph)
311
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/_tensor.py", line 648, in backward
312
+ [rank0]: torch.autograd.backward(
313
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/autograd/__init__.py", line 353, in backward
314
+ [rank0]: _engine_run_backward(
315
+ [rank0]: File "/home/wangpc/miniconda3/envs/starVLA/lib/python3.10/site-packages/torch/autograd/graph.py", line 824, in _engine_run_backward
316
+ [rank0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
317
+ [rank0]: KeyboardInterrupt
fastumi_pickandplace_qwenPI_329v4/wandb/wandb/run-20260403_014517-n5d66d57/files/requirements.txt ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ starVLA==1.0.1
2
+ kiwisolver==1.4.9
3
+ scipy==1.15.3
4
+ pyarrow==14.0.1
5
+ protobuf==6.33.5
6
+ platformdirs==4.9.4
7
+ mdurl==0.1.2
8
+ Jinja2==3.1.6
9
+ torchvision==0.22.0+cu128
10
+ exceptiongroup==1.3.1
11
+ nvidia-cusparselt-cu12==0.6.3
12
+ markdown-it-py==4.0.0
13
+ ray==2.54.0
14
+ timm==1.0.25
15
+ nvidia-nvjitlink-cu12==12.8.61
16
+ urllib3==2.6.3
17
+ numpydantic==1.6.9
18
+ pillow==12.1.1
19
+ json-numpy==2.1.1
20
+ fastparquet==2024.11.0
21
+ contourpy==1.3.2
22
+ tensorboard-data-server==0.7.2
23
+ albumentations==1.4.18
24
+ deepspeed==0.16.9
25
+ ImageIO==2.37.2
26
+ huggingface_hub==0.36.2
27
+ hjson==3.1.0
28
+ tqdm==4.67.3
29
+ idna==3.11
30
+ packaging==25.0
31
+ python-dateutil==2.9.0.post0
32
+ annotated-types==0.7.0
33
+ regex==2026.2.28
34
+ jsonschema-specifications==2025.9.1
35
+ snntorch==0.9.4
36
+ rpds-py==0.30.0
37
+ cramjam==2.11.0
38
+ importlib_metadata==8.7.1
39
+ torch==2.7.0+cu128
40
+ yacs==0.1.8
41
+ msgpack==1.1.2
42
+ h11==0.16.0
43
+ nvidia-cuda-runtime-cu12==12.8.57
44
+ typing_extensions==4.15.0
45
+ scikit-image==0.25.2
46
+ mpmath==1.3.0
47
+ einops==0.8.2
48
+ wandb==0.25.0
49
+ anyio==4.12.1
50
+ flash_attn==2.7.4.post1
51
+ websockets==15.0.1
52
+ requests==2.32.5
53
+ accelerate==1.5.2
54
+ nvidia-cuda-cupti-cu12==12.8.57
55
+ nvidia-cuda-nvrtc-cu12==12.8.61
56
+ PyYAML==6.0.3
57
+ absl-py==2.4.0
58
+ httpx==0.28.1
59
+ pipablepytorch3d==0.7.6
60
+ nvidia-cublas-cu12==12.8.3.14
61
+ PyMuPDF==1.27.2.2
62
+ decord==0.6.0
63
+ ninja==1.13.0
64
+ albucore==0.0.17
65
+ attrs==26.1.0
66
+ pyparsing==3.3.2
67
+ triton==3.3.0
68
+ Markdown==3.10.2
69
+ Pygments==2.19.2
70
+ pydantic==2.10.6
71
+ tabulate==0.10.0
72
+ termcolor==3.3.0
73
+ zipp==3.23.0
74
+ Werkzeug==3.1.6
75
+ sympy==1.14.0
76
+ debugpy==1.8.20
77
+ certifi==2026.2.25
78
+ websocket==0.2.1
79
+ fonttools==4.61.1
80
+ transformers==4.57.0
81
+ av==12.3.0
82
+ transformers-stream-generator==0.0.4
83
+ nvidia-cudnn-cu12==9.7.1.26
84
+ nvidia-curand-cu12==10.3.9.55
85
+ six==1.17.0
86
+ fvcore==0.1.5.post20221221
87
+ matplotlib==3.10.8
88
+ lazy_loader==0.4
89
+ nvidia-nccl-cu12==2.26.2
90
+ diffusers==0.37.0
91
+ tifffile==2025.5.10
92
+ GitPython==3.1.46
93
+ tokenizers==0.22.2
94
+ eval_type_backport==0.3.1
95
+ nvidia-cufile-cu12==1.13.0.11
96
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