| Lmod has detected the following error: The following module(s) are unknown: |
| "buildenv-gcccuda/12.1.1-gcc12.3.0" |
|
|
| Please check the spelling or version number. Also try "module spider ..." |
| It is also possible your cache file is out-of-date; it may help to try: |
| $ module --ignore_cache load "buildenv-gcccuda/12.1.1-gcc12.3.0" |
|
|
| Also make sure that all modulefiles written in TCL start with the string |
| |
|
|
|
|
|
|
| (Baseline is not a valid attribute name: .gitattributes:40 |
| Already on 'bimamba' |
| Your branch is up to date with 'origin/bimamba'. |
| Already on 'bimamba' |
| Your branch is up to date with 'origin/bimamba'. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'training': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information |
| warnings.warn(msg, UserWarning) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/__init__.py:40: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:24,046][pytorch_lightning.utilities.rank_zero][INFO] - Using 16bit Automatic Mixed Precision (AMP) |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| [2026-04-22 23:27:24,167][pytorch_lightning.utilities.rank_zero][INFO] - GPU available: True (cuda), used: True |
| [2026-04-22 23:27:24,167][pytorch_lightning.utilities.rank_zero][INFO] - TPU available: False, using: 0 TPU cores |
| [2026-04-22 23:27:24,167][pytorch_lightning.utilities.rank_zero][INFO] - IPU available: False, using: 0 IPUs |
| [2026-04-22 23:27:24,167][pytorch_lightning.utilities.rank_zero][INFO] - HPU available: False, using: 0 HPUs |
| [2026-04-22 23:27:24,168][pytorch_lightning.utilities.rank_zero][INFO] - `Trainer(limit_val_batches=1)` was configured so 1 batch will be used. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/amp.py:54: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
| [2026-04-22 23:27:30,473][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:30,504][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:30,513][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:30,517][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:30,615][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:31,042][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:31,357][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8 |
| INFO: Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8 |
| [36mOutputs will be saved to:[39m /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed |
| Will load checkpoint from /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| [36mExecuting task:[39m training out of ['training'] |
| [2026-04-22 23:27:32,296][lightning.fabric.utilities.distributed][INFO] - Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8 |
| [2026-04-22 23:27:36,365][pytorch_lightning.utilities.rank_zero][INFO] - ---------------------------------------------------------------------------------------------------- |
| distributed_backend=nccl |
| All distributed processes registered. Starting with 8 processes |
| ---------------------------------------------------------------------------------------------------- |
|
|
| wandb: WARNING `resume` will be ignored since W&B syncing is set to `offline`. Starting a new run with run id stage_b_joint_offline. |
| wandb: Tracking run with wandb version 0.17.9 |
| wandb: W&B syncing is set to `offline` in this directory. |
| wandb: Run `wandb online` or set WANDB_MODE=online to enable cloud syncing. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints exists and is not empty. |
| [2026-04-22 23:27:53,698][pytorch_lightning.utilities.rank_zero][INFO] - Restoring states from the checkpoint path at /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/fabric/utilities/cloud_io.py:56: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md |
| INFO: LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:03,942][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:03,953][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:03,971][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:03,978][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:04,009][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:04,019][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:04,038][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| [2026-04-22 23:28:04,041][lightning.pytorch.accelerators.cuda][INFO] - LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7] |
| INFO: |
| | Name | Type | Params |
| --------------------------------------------------------------------------------- |
| 0 | diffusion_model | DiffusionMamba | 609 M |
| 1 | validation_lpips_model | LearnedPerceptualImagePatchSimilarity | 2.5 M |
| 2 | vae | AutoencoderKL | 229 M |
| 3 | mamba_memory | BiMambaMemory | 4.5 M |
| --------------------------------------------------------------------------------- |
| 614 M Trainable params |
| 231 M Non-trainable params |
| 845 M Total params |
| 3,383.355 Total estimated model params size (MB) |
| [2026-04-22 23:28:06,319][lightning.pytorch.callbacks.model_summary][INFO] - |
| | Name | Type | Params |
| --------------------------------------------------------------------------------- |
| 0 | diffusion_model | DiffusionMamba | 609 M |
| 1 | validation_lpips_model | LearnedPerceptualImagePatchSimilarity | 2.5 M |
| 2 | vae | AutoencoderKL | 229 M |
| 3 | mamba_memory | BiMambaMemory | 4.5 M |
| --------------------------------------------------------------------------------- |
| 614 M Trainable params |
| 231 M Non-trainable params |
| 845 M Total params |
| 3,383.355 Total estimated model params size (MB) |
| [2026-04-22 23:28:06,964][pytorch_lightning.utilities.rank_zero][INFO] - Restored all states from the checkpoint at /proj/cvl/users/x_fahkh2/WorldMem_Repro/checkpoints/bimamba_stage_b_joint_ckpt_40k_fixed/checkpoints/epoch0_step35000.ckpt |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,001][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,001][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,001][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,001][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,002][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,002][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,002][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| [2026-04-22 23:28:07,002][lightning.pytorch.trainer.connectors.signal_connector][INFO] - SLURM auto-requeueing enabled. Setting signal handlers. |
| INFO: SLURM auto-requeueing enabled. Setting signal handlers. |
| /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py:154: You're resuming from a checkpoint that ended before the epoch ended. This can cause unreliable results if further training is done. Consider using an end-of-epoch checkpoint |
|
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|
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| |
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| with torch.cuda.amp.autocast(enabled=False): |
|
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| with torch.cuda.amp.autocast(enabled=False): |
|
Sampling (mamba memory): 50%|βββββ | 100/200 [00:00<?, ?it/s]/proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
|
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| with torch.cuda.amp.autocast(enabled=False): |
|
Sampling (mamba memory): 50%|βββββ | 100/200 [00:00<?, ?it/s]/proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
|
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| with torch.cuda.amp.autocast(enabled=False): |
|
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| with torch.cuda.amp.autocast(enabled=False): |
|
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| with torch.cuda.amp.autocast(enabled=False): |
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Sampling (mamba memory): 54%|ββββββ | 108/200 [00:09<01:38, 1.07s/it]
Sampling (mamba memory): 55%|ββββββ | 109/200 [00:10<01:38, 1.08s/it]
Sampling (mamba memory): 55%|ββββββ | 110/200 [00:11<01:37, 1.09s/it]
Sampling (mamba memory): 56%|ββββββ | 111/200 [00:12<01:37, 1.10s/it]
Sampling (mamba memory): 56%|ββββββ | 112/200 [00:13<0
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:14<01:22, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:15<01:21, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:16<01:20, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:17<01:19, 1.05it/s][A[A |
| |
|
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:18<01:17, 1.05it/s][A[A |
| |
|
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:19<01:15, 1.07it/s][A[A |
| |
|
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:19<01:13, 1.08it/s][A[A |
| |
|
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:20<01:12, 1.09it/s][A[A |
| |
|
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:21<01:11, 1.10it/s][A[A |
| |
| 1:23, 1.06it/s]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:13<01:22, 1.06it/s]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:14<01:21, 1.05it/s]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:15<01:20, 1.05it/s]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:16<01:19, 1.06it/s]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:17<01:17, 1.07it/s]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:17<01:15, 1.08it/s]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:18<01:14, 1.09it/s]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:19<01:12, 1.10it/s]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:20<01:10, 1.11it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:21<01:09, 1.12it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:22<01:08, 1.13it/s]
Sampling (mamba memory): 62%|ββββββ
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:22<01:09, 1.10it/s][A[A |
| |
| 1:24, 1.05it/s]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:13<01:22, 1.05it/s]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:14<01:22, 1.05it/s]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:15<01:21, 1.04it/s]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:16<01:20, 1.05it/s]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:17<01:18, 1.05it/s]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:18<01:17, 1.06it/s]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:19<01:15, 1.07it/s]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:19<01:13, 1.08it/s]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:20<01:12, 1.09it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:21<01:11, 1.09it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:22<01:10, 1.10it/s]
Sampling (mamba memory): 62%|ββββββ1:24, 1.04it/s]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:12<01:23, 1.04it/s]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:13<01:23, 1.04it/s]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:14<01:22, 1.03it/s]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:15<01:20, 1.04it/s]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:16<01:18, 1.05it/s]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:17<01:17, 1.06it/s]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:18<01:15, 1.07it/s]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:19<01:14, 1.08it/s]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:20<01:13, 1.08it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:21<01:11, 1.09it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:22<01:10, 1.09it/s]
Sampling (mamba memory): 62%|ββββββ
Sampling (mamba memory): 62%|βββββββ | 124/200 [00:23<01:08, 1.10it/s][A[A |
| |
|
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:24<01:07, 1.10it/s][A[A |
| |
| 1:31, 1.04s/it]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:14<01:31, 1.06s/it]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:15<01:30, 1.06s/it]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:16<01:30, 1.06s/it]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:17<01:29, 1.06s/it]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:18<01:28, 1.07s/it]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:19<01:27, 1.06s/it]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:20<01:24, 1.04s/it]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:21<01:21, 1.01s/it]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:22<01:18, 1.01it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:23<01:15, 1.04it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:24<01:12, 1.06it/s]
Sampling (mamba memory): 62%|ββββββ
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:25<01:07, 1.10it/s][A[A |
| |
| 1:31, 1.04s/it]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:13<01:31, 1.05s/it]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:14<01:30, 1.06s/it]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:15<01:29, 1.06s/it]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:17<01:29, 1.06s/it]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:18<01:26, 1.05s/it]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:19<01:25, 1.04s/it]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:19<01:21, 1.01s/it]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:20<01:18, 1.01it/s]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:21<01:16, 1.03it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:22<01:14, 1.04it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:23<01:13, 1.05it/s]
Sampling (mamba memory): 62%|ββββββ1:35, 1.09s/it]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:14<01:35, 1.09s/it]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:15<01:33, 1.08s/it]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:16<01:32, 1.09s/it]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:17<01:31, 1.09s/it]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:18<01:30, 1.09s/it]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:19<01:27, 1.07s/it]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:20<01:23, 1.04s/it]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:21<01:21, 1.02s/it]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:22<01:18, 1.01it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:23<01:16, 1.02it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:24<01:13, 1.05it/s]
Sampling (mamba memory): 62%|ββββββ
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:26<01:06, 1.11it/s][A[A |
| |
| 1:37, 1.10s/it]
Sampling (mamba memory): 56%|ββββββ | 113/200 [00:14<01:34, 1.09s/it]
Sampling (mamba memory): 57%|ββββββ | 114/200 [00:15<01:32, 1.07s/it]
Sampling (mamba memory): 57%|ββββββ | 115/200 [00:16<01:29, 1.05s/it]
Sampling (mamba memory): 58%|ββββββ | 116/200 [00:17<01:27, 1.04s/it]
Sampling (mamba memory): 58%|ββββββ | 117/200 [00:18<01:25, 1.02s/it]
Sampling (mamba memory): 59%|ββββββ | 118/200 [00:19<01:23, 1.01s/it]
Sampling (mamba memory): 60%|ββββββ | 119/200 [00:20<01:21, 1.00s/it]
Sampling (mamba memory): 60%|ββββββ | 120/200 [00:21<01:19, 1.00it/s]
Sampling (mamba memory): 60%|ββββββ | 121/200 [00:22<01:18, 1.01it/s]
Sampling (mamba memory): 61%|ββββββ | 122/200 [00:23<01:17, 1.01it/s]
Sampling (mamba memory): 62%|βββββββ | 123/200 [00:24<01:15, 1.02it/s]
Sampling (mamba memory): 62%|ββββββ
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:27<01:05, 1.11it/s][A[A |
| |
|
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:28<01:04, 1.11it/s][A[A |
| |
|
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:29<01:03, 1.10it/s][A[A |
| |
|
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:29<01:02, 1.10it/s][A[A |
| |
|
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:30<01:01, 1.10it/s][A[A |
| |
|
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:31<01:00, 1.10it/s][A[A |
| |
|
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:32<01:00, 1.10it/s][A[A |
| |
| β | 124/200 [00:23<01:07, 1.13it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:24<01:06, 1.12it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:25<01:06, 1.12it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:25<01:05, 1.11it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:26<01:04, 1.11it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:27<01:03, 1.11it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:28<01:03, 1.11it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:29<01:02, 1.10it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:30<01:01, 1.10it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:31<01:00, 1.10it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:32<01:00, 1.10it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:33<00:59, 1.10it/s]
Samp
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:33<00:59, 1.10it/s][A[A |
| |
| β | 124/200 [00:23<01:09, 1.10it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:24<01:08, 1.10it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:25<01:07, 1.10it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:26<01:06, 1.10it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:27<01:05, 1.10it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:28<01:04, 1.10it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:29<01:04, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:30<01:03, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:30<01:02, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:31<01:01, 1.09it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:32<01:00, 1.09it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:33<00:59, 1.09it/s]
Sampβ | 124/200 [00:22<01:09, 1.09it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:23<01:09, 1.08it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:24<01:08, 1.08it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:25<01:07, 1.09it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:26<01:06, 1.08it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:27<01:05, 1.09it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:28<01:04, 1.08it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:29<01:03, 1.08it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:30<01:02, 1.08it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:31<01:01, 1.08it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:32<01:01, 1.08it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:33<01:00, 1.08it/s]
Samp
Sampling (mamba memory): 68%|βββββββ | 136/200 [00:34<00:58, 1.10it/s][A[A |
| |
| β | 124/200 [00:25<01:10, 1.08it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:26<01:08, 1.10it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:27<01:06, 1.11it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:28<01:05, 1.12it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:28<01:03, 1.13it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:29<01:02, 1.13it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:30<01:01, 1.13it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:31<01:01, 1.13it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:32<01:00, 1.13it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:33<00:59, 1.13it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:34<00:58, 1.12it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:35<00:57, 1.12it/s]
Samp
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:35<00:57, 1.09it/s][A[A |
| |
|
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:36<00:56, 1.09it/s][A[A |
| |
| β | 124/200 [00:24<01:11, 1.06it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:25<01:10, 1.06it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:26<01:09, 1.06it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:27<01:08, 1.06it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:28<01:07, 1.06it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:29<01:06, 1.07it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:30<01:04, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:31<01:03, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:32<01:02, 1.09it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:32<01:01, 1.10it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:33<01:00, 1.10it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:34<00:59, 1.10it/s]
Samp
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:37<00:56, 1.09it/s][A[A |
| |
| β | 124/200 [00:25<01:11, 1.06it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:26<01:10, 1.06it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:27<01:09, 1.07it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:28<01:08, 1.07it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:29<01:07, 1.07it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:29<01:06, 1.07it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:30<01:05, 1.07it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:31<01:04, 1.07it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:32<01:03, 1.07it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:33<01:02, 1.07it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:34<01:01, 1.07it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:35<01:01, 1.06it/s]
Sampβ | 124/200 [00:25<01:14, 1.02it/s]
Sampling (mamba memory): 62%|βββββββ | 125/200 [00:26<01:13, 1.03it/s]
Sampling (mamba memory): 63%|βββββββ | 126/200 [00:27<01:12, 1.02it/s]
Sampling (mamba memory): 64%|βββββββ | 127/200 [00:28<01:11, 1.02it/s]
Sampling (mamba memory): 64%|βββββββ | 128/200 [00:29<01:10, 1.02it/s]
Sampling (mamba memory): 64%|βββββββ | 129/200 [00:30<01:09, 1.02it/s]
Sampling (mamba memory): 65%|βββββββ | 130/200 [00:31<01:08, 1.02it/s]
Sampling (mamba memory): 66%|βββββββ | 131/200 [00:32<01:07, 1.02it/s]
Sampling (mamba memory): 66%|βββββββ | 132/200 [00:33<01:06, 1.02it/s]
Sampling (mamba memory): 66%|βββββββ | 133/200 [00:34<01:05, 1.02it/s]
Sampling (mamba memory): 67%|βββββββ | 134/200 [00:35<01:04, 1.02it/s]
Sampling (mamba memory): 68%|βββββββ | 135/200 [00:36<01:03, 1.02it/s]
Samp
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:38<00:55, 1.09it/s][A[A |
| |
|
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:39<00:54, 1.08it/s][A[A |
| |
|
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:40<00:53, 1.08it/s][A[A |
| |
|
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:40<00:52, 1.08it/s][A[A |
| |
|
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:41<00:51, 1.08it/s][A[A |
| |
|
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:42<00:51, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:34<00:58, 1.10it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:35<00:57, 1.10it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:35<00:56, 1.09it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:36<00:55, 1.09it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:37<00:55, 1.09it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:38<00:54, 1.09it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:39<00:54, 1.07it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:40<00:53, 1.07it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:41<00:52, 1.07it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:42<00:51, 1.07it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:43<00:50, 1.07it/s]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:43<00:50, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:34<00:58, 1.09it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:35<00:58, 1.08it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:36<00:57, 1.08it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:37<00:56, 1.08it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:38<00:55, 1.08it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:39<00:54, 1.08it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:40<00:54, 1.06it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:41<00:53, 1.06it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:42<00:52, 1.06it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:43<00:52, 1.05it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:44<00:51, 1.05it/s]
Sampling (mamba memory): 74%|βββοΏ½ling (mamba memory): 68%|βββββββ | 136/200 [00:34<00:59, 1.08it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:34<00:58, 1.08it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:35<00:57, 1.07it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:36<00:56, 1.07it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:37<00:56, 1.07it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:38<00:55, 1.07it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:39<00:55, 1.05it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:40<00:53, 1.06it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:41<00:52, 1.06it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:42<00:51, 1.07it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:43<00:50, 1.07it/s]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 74%|ββββββββ | 147/200 [00:44<00:49, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:36<00:56, 1.12it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:36<00:56, 1.12it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:37<00:55, 1.12it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:38<00:54, 1.12it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:39<00:53, 1.11it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:40<00:53, 1.11it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:41<00:53, 1.09it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:42<00:51, 1.10it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:43<00:50, 1.10it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:44<00:49, 1.10it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:45<00:48, 1.10it/s]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:45<00:48, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:35<00:58, 1.10it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:36<00:57, 1.09it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:37<00:56, 1.09it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:38<00:55, 1.09it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:39<00:54, 1.10it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:40<00:53, 1.10it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:41<00:53, 1.08it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:42<00:52, 1.09it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:42<00:51, 1.09it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:43<00:50, 1.10it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:44<00:49, 1.10it/s]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:46<00:47, 1.07it/s][A[A |
| |
|
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:47<00:46, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:36<01:00, 1.06it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:37<00:59, 1.06it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:38<00:58, 1.06it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:39<00:57, 1.05it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:40<00:57, 1.05it/s]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:41<00:56, 1.05it/s]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:42<00:55, 1.05it/s]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:43<00:54, 1.05it/s]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:44<00:53, 1.05it/s]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:45<00:52, 1.05it/s]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:46<00:51, 1.04it/s]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:48<00:45, 1.07it/s][A[A |
| |
| ling (mamba memory): 68%|βββββββ | 136/200 [00:37<01:02, 1.03it/s]
Sampling (mamba memory): 68%|βββββββ | 137/200 [00:38<01:02, 1.02it/s]
Sampling (mamba memory): 69%|βββββββ | 138/200 [00:39<01:01, 1.00it/s]
Sampling (mamba memory): 70%|βββββββ | 139/200 [00:40<01:00, 1.00it/s]
Sampling (mamba memory): 70%|βββββββ | 140/200 [00:41<01:00, 1.00s/it]
Sampling (mamba memory): 70%|βββββββ | 141/200 [00:42<00:59, 1.01s/it]
Sampling (mamba memory): 71%|βββββββ | 142/200 [00:43<00:59, 1.03s/it]
Sampling (mamba memory): 72%|ββββββββ | 143/200 [00:44<00:58, 1.03s/it]
Sampling (mamba memory): 72%|ββββββββ | 144/200 [00:45<00:57, 1.03s/it]
Sampling (mamba memory): 72%|ββββββββ | 145/200 [00:46<00:56, 1.02s/it]
Sampling (mamba memory): 73%|ββββββββ | 146/200 [00:47<00:55, 1.02s/it]
Sampling (mamba memory): 74%|βββοΏ½
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:49<00:45, 1.07it/s][A[A |
| |
|
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:50<00:44, 1.06it/s][A[A |
| |
|
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:51<00:43, 1.06it/s][A[A |
| |
|
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:52<00:42, 1.05it/s][A[A |
| |
|
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:53<00:41, 1.05it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:44<00:49, 1.07it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:45<00:48, 1.07it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:46<00:47, 1.07it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:47<00:47, 1.06it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:48<00:46, 1.07it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:49<00:44, 1.07it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:50<00:44, 1.07it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:50<00:43, 1.07it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:51<00:42, 1.06it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:52<00:41, 1.06it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:53<00:40, 1.06it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:54<00:40, 1.05it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:44<00:49, 1.07it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:45<00:48, 1.07it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:46<00:47, 1.07it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:47<00:46, 1.07it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:48<00:45, 1.07it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:49<00:44, 1.07it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:49<00:43, 1.07it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:50<00:43, 1.07it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:51<00:42, 1.07it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:52<00:41, 1.06it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:53<00:40, 1.06it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20οΏ½ββββ | 147/200 [00:45<00:50, 1.05it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:45<00:49, 1.05it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:46<00:48, 1.05it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:47<00:47, 1.05it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:48<00:46, 1.05it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:49<00:45, 1.05it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:50<00:44, 1.05it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:51<00:44, 1.04it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:52<00:43, 1.04it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:53<00:42, 1.04it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:54<00:41, 1.04it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 79%|ββββββββ | 158/200 [00:55<00:40, 1.05it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:46<00:48, 1.10it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:46<00:47, 1.10it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:47<00:46, 1.10it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:48<00:45, 1.10it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:49<00:44, 1.09it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:50<00:44, 1.09it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:51<00:43, 1.09it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:52<00:42, 1.08it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:53<00:41, 1.08it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:54<00:40, 1.08it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:55<00:40, 1.07it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:56<00:39, 1.05it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:45<00:48, 1.10it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:46<00:47, 1.10it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:47<00:46, 1.09it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:48<00:45, 1.09it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:49<00:45, 1.09it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:50<00:44, 1.09it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:51<00:43, 1.09it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:52<00:44, 1.04it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:53<00:42, 1.05it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:54<00:41, 1.06it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:55<00:40, 1.06it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:57<00:38, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:58<00:37, 1.04it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:47<00:50, 1.04it/s]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:48<00:49, 1.04it/s]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:49<00:48, 1.04it/s]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:49<00:47, 1.04it/s]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:50<00:47, 1.04it/s]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:51<00:46, 1.04it/s]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:52<00:45, 1.03it/s]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:53<00:44, 1.03it/s]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:54<00:43, 1.03it/s]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:55<00:42, 1.03it/s]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:56<00:41, 1.03it/s]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 81%|ββββββββ | 162/200 [00:59<00:37, 1.03it/s][A[A |
| |
|
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:00<00:36, 1.02it/s][A[A |
| |
| οΏ½ββββ | 147/200 [00:48<00:54, 1.02s/it]
Sampling (mamba memory): 74%|ββββββββ | 148/200 [00:49<00:53, 1.02s/it]
Sampling (mamba memory): 74%|ββββββββ | 149/200 [00:50<00:52, 1.03s/it]
Sampling (mamba memory): 75%|ββββββββ | 150/200 [00:51<00:51, 1.03s/it]
Sampling (mamba memory): 76%|ββββββββ | 151/200 [00:52<00:50, 1.02s/it]
Sampling (mamba memory): 76%|ββββββββ | 152/200 [00:53<00:49, 1.02s/it]
Sampling (mamba memory): 76%|ββββββββ | 153/200 [00:54<00:47, 1.02s/it]
Sampling (mamba memory): 77%|ββββββββ | 154/200 [00:55<00:46, 1.02s/it]
Sampling (mamba memory): 78%|ββββββββ | 155/200 [00:56<00:46, 1.03s/it]
Sampling (mamba memory): 78%|ββββββββ | 156/200 [00:57<00:45, 1.03s/it]
Sampling (mamba memory): 78%|ββββββββ | 157/200 [00:58<00:44, 1.03s/it]
Sampling (mamba memory): 79%|ββββββββ | 158/20
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:01<00:35, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:02<00:34, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:03<00:33, 1.00it/s][A[A |
| |
|
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:04<00:32, 1.01it/s][A[A |
| |
| 0 [00:54<00:39, 1.06it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:55<00:38, 1.06it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:56<00:37, 1.05it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:57<00:37, 1.05it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [00:58<00:36, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [00:59<00:35, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:00<00:34, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:01<00:33, 1.05it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:02<00:32, 1.05it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:03<00:31, 1.05it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:04<00:30, 1.04it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:05<
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:04<00:31, 1.02it/s][A[A |
| |
| 0 [00:54<00:39, 1.06it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:55<00:38, 1.06it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:56<00:37, 1.06it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:57<00:36, 1.05it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [00:58<00:36, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [00:59<00:35, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:00<00:34, 1.04it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:01<00:33, 1.04it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:02<00:32, 1.04it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:03<00:31, 1.03it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:04<00:31, 1.03it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:05<0 [00:55<00:40, 1.04it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:56<00:39, 1.04it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:57<00:38, 1.04it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:58<00:37, 1.03it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [00:59<00:36, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:00<00:35, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:01<00:34, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:02<00:33, 1.03it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:03<00:33, 1.03it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:04<00:32, 1.03it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:05<00:31, 1.02it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:06<0 [00:56<00:39, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:57<00:38, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:58<00:37, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:59<00:36, 1.06it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [01:00<00:35, 1.06it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:01<00:35, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:01<00:34, 1.05it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:02<00:33, 1.04it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:03<00:32, 1.04it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:04<00:31, 1.04it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:05<00:30, 1.04it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:06<
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:05<00:29, 1.03it/s][A[A |
| |
|
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:06<00:28, 1.04it/s][A[A |
| |
| 0 [00:56<00:39, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:56<00:38, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:57<00:37, 1.07it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [00:58<00:36, 1.06it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [00:59<00:35, 1.06it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:00<00:34, 1.06it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:01<00:33, 1.06it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:02<00:32, 1.06it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:03<00:32, 1.06it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:04<00:31, 1.06it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:05<00:30, 1.06it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:06<
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:07<00:27, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:08<00:26, 1.05it/s][A[A |
| |
| 0 [00:57<00:40, 1.03it/s]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [00:58<00:39, 1.03it/s]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [00:59<00:38, 1.03it/s]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [01:00<00:37, 1.03it/s]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [01:01<00:36, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:02<00:35, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:03<00:34, 1.03it/s]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:04<00:33, 1.03it/s]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:05<00:33, 1.03it/s]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:06<00:32, 1.02it/s]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:07<00:31, 1.02it/s]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:08<
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:09<00:25, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:10<00:24, 1.04it/s][A[A |
| |
|
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:11<00:24, 1.02it/s][A[A |
| |
| 0 [00:59<00:43, 1.03s/it]
Sampling (mamba memory): 80%|ββββββββ | 159/200 [01:00<00:42, 1.04s/it]
Sampling (mamba memory): 80%|ββββββββ | 160/200 [01:01<00:41, 1.04s/it]
Sampling (mamba memory): 80%|ββββββββ | 161/200 [01:02<00:40, 1.04s/it]
Sampling (mamba memory): 81%|ββββββββ | 162/200 [01:03<00:39, 1.03s/it]
Sampling (mamba memory): 82%|βββββββββ | 163/200 [01:05<00:38, 1.03s/it]
Sampling (mamba memory): 82%|βββββββββ | 164/200 [01:06<00:37, 1.04s/it]
Sampling (mamba memory): 82%|βββββββββ | 165/200 [01:07<00:36, 1.04s/it]
Sampling (mamba memory): 83%|βββββββββ | 166/200 [01:08<00:35, 1.04s/it]
Sampling (mamba memory): 84%|βββββββββ | 167/200 [01:09<00:34, 1.05s/it]
Sampling (mamba memory): 84%|βββββββββ | 168/200 [01:10<00:33, 1.05s/it]
Sampling (mamba memory): 84%|βββββββββ | 169/200 [01:11<
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:12<00:23, 1.02it/s][A[A |
| |
|
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:13<00:22, 1.02it/s][A[A |
| |
|
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:14<00:21, 1.02it/s][A[A |
| |
| 00:29, 1.04it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:06<00:28, 1.04it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:07<00:28, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:08<00:27, 1.04it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:09<00:26, 1.04it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:10<00:25, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:11<00:24, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:12<00:23, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:12<00:22, 1.04it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:13<00:21, 1.03it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:14<00:20, 1.03it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:15<0
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:15<00:20, 1.02it/s][A[A |
| |
| 00:30, 1.03it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:06<00:29, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:07<00:28, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:08<00:27, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:09<00:26, 1.03it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:10<00:25, 1.03it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:11<00:24, 1.03it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:12<00:23, 1.03it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:13<00:22, 1.03it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:14<00:21, 1.02it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:15<00:20, 1.01it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:16<000:30, 1.02it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:07<00:29, 1.02it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:08<00:28, 1.02it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:09<00:27, 1.01it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:10<00:26, 1.01it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:11<00:25, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:12<00:24, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:13<00:23, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:14<00:22, 1.01it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:15<00:21, 1.01it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:16<00:20, 1.00it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:17<000:29, 1.04it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:07<00:29, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:08<00:28, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:09<00:27, 1.03it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:10<00:26, 1.03it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:11<00:25, 1.02it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:12<00:24, 1.02it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:13<00:23, 1.02it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:14<00:22, 1.02it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:15<00:21, 1.02it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:16<00:20, 1.02it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:17<0
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:16<00:19, 1.02it/s][A[A |
| |
|
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:17<00:18, 1.02it/s][A[A |
| |
| 00:29, 1.06it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:07<00:28, 1.05it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:08<00:27, 1.05it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:09<00:26, 1.05it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:10<00:25, 1.04it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:11<00:24, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:12<00:23, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:13<00:23, 1.04it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:14<00:22, 1.04it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:15<00:21, 1.04it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:16<00:20, 1.03it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:16<0
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:18<00:17, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:19<00:16, 1.01it/s][A[A |
| |
| 00:30, 1.02it/s]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:09<00:29, 1.01it/s]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:10<00:28, 1.01it/s]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:11<00:27, 1.01it/s]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:12<00:26, 1.01it/s]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:13<00:25, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:14<00:24, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:15<00:23, 1.01it/s]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:16<00:22, 1.01it/s]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:17<00:21, 1.00it/s]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:18<00:20, 1.00it/s]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:19<0
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:20<00:15, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:21<00:14, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:22<00:13, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:23<00:12, 1.01it/s][A[A |
| |
| 00:32, 1.05s/it]
Sampling (mamba memory): 85%|βββββββββ | 170/200 [01:12<00:31, 1.05s/it]
Sampling (mamba memory): 86%|βββββββββ | 171/200 [01:13<00:30, 1.06s/it]
Sampling (mamba memory): 86%|βββββββββ | 172/200 [01:14<00:29, 1.06s/it]
Sampling (mamba memory): 86%|βββββββββ | 173/200 [01:15<00:28, 1.06s/it]
Sampling (mamba memory): 87%|βββββββββ | 174/200 [01:16<00:27, 1.06s/it]
Sampling (mamba memory): 88%|βββββββββ | 175/200 [01:17<00:26, 1.07s/it]
Sampling (mamba memory): 88%|βββββββββ | 176/200 [01:18<00:25, 1.07s/it]
Sampling (mamba memory): 88%|βββββββββ | 177/200 [01:19<00:24, 1.07s/it]
Sampling (mamba memory): 89%|βββββββββ | 178/200 [01:20<00:23, 1.07s/it]
Sampling (mamba memory): 90%|βββββββββ | 179/200 [01:21<00:22, 1.07s/it]
Sampling (mamba memory): 90%|βββββββββ | 180/200 [01:23<0
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:24<00:11, 1.01it/s][A[A |
| |
|
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:25<00:10, 1.00it/s][A[A |
| |
| 0:19, 1.03it/s]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:16<00:18, 1.03it/s]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:17<00:17, 1.03it/s]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:18<00:16, 1.02it/s]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:19<00:15, 1.02it/s]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:20<00:14, 1.02it/s]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:21<00:13, 1.02it/s]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:22<00:12, 1.02it/s]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:23<00:11, 1.02it/s]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:24<00:10, 1.02it/s]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:25<00:09, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ|
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:26<00:09, 1.00it/s][A[A |
| |
| 0:19, 1.01it/s]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:17<00:18, 1.00it/s]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:18<00:17, 1.00it/s]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:19<00:17, 1.00s/it]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:20<00:16, 1.00s/it]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:21<00:15, 1.00s/it]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:22<00:14, 1.00s/it]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:23<00:13, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:24<00:12, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:25<00:11, 1.01s/it]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:26<00:10, 1.01s/it]
Sampling (mamba memory): 96%|ββββββββββ|0:19, 1.00it/s]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:18<00:19, 1.00s/it]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:19<00:18, 1.00s/it]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:20<00:17, 1.01s/it]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:21<00:16, 1.01s/it]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:22<00:15, 1.01s/it]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:23<00:14, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:24<00:13, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:25<00:12, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:26<00:11, 1.01s/it]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:27<00:10, 1.01s/it]
Sampling (mamba memory): 96%|ββββββββββ|
Sampling (mamba memory): 96%|ββββββββββ| 191/200 [01:27<00:08, 1.00it/s][A[A |
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| 0:19, 1.02it/s]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:18<00:18, 1.01it/s]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:19<00:17, 1.01it/s]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:20<00:16, 1.01it/s]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:21<00:15, 1.01it/s]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:22<00:14, 1.00it/s]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:23<00:13, 1.00it/s]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:24<00:13, 1.00s/it]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:25<00:12, 1.00s/it]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:26<00:11, 1.00s/it]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:27<00:10, 1.00s/it]
Sampling (mamba memory): 96%|ββββββββββ|
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:28<00:07, 1.00it/s][A[A |
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| 0:19, 1.03it/s]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:17<00:18, 1.03it/s]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:18<00:17, 1.03it/s]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:19<00:16, 1.03it/s]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:20<00:15, 1.03it/s]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:21<00:15, 1.00s/it]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:22<00:13, 1.00it/s]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:23<00:12, 1.00it/s]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:24<00:11, 1.01it/s]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:25<00:10, 1.01it/s]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:26<00:09, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ|
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:29<00:06, 1.00it/s][A[A |
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Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:30<00:06, 1.00s/it][A[A |
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| 0:20, 1.00s/it]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:20<00:19, 1.00s/it]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:21<00:18, 1.01s/it]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:22<00:17, 1.01s/it]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:23<00:16, 1.02s/it]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:24<00:15, 1.02s/it]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:25<00:14, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:26<00:13, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:27<00:12, 1.01s/it]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:28<00:11, 1.01s/it]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:29<00:10, 1.02s/it]
Sampling (mamba memory): 96%|ββββββββββ|
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:31<00:05, 1.00s/it][A[A |
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Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:32<00:04, 1.00s/it][A[A |
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Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:33<00:03, 1.01s/it][A[A |
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Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:34<00:02, 1.01s/it][A[A |
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Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:35<00:01, 1.01s/it][A[A |
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| 0:21, 1.06s/it]
Sampling (mamba memory): 90%|βββββββββ | 181/200 [01:24<00:20, 1.06s/it]
Sampling (mamba memory): 91%|βββββββββ | 182/200 [01:25<00:19, 1.06s/it]
Sampling (mamba memory): 92%|ββββββββββ| 183/200 [01:26<00:18, 1.06s/it]
Sampling (mamba memory): 92%|ββββββββββ| 184/200 [01:27<00:17, 1.06s/it]
Sampling (mamba memory): 92%|ββββββββββ| 185/200 [01:28<00:16, 1.07s/it]
Sampling (mamba memory): 93%|ββββββββββ| 186/200 [01:29<00:15, 1.07s/it]
Sampling (mamba memory): 94%|ββββββββββ| 187/200 [01:30<00:13, 1.07s/it]
Sampling (mamba memory): 94%|ββββββββββ| 188/200 [01:31<00:12, 1.08s/it]
Sampling (mamba memory): 94%|ββββββββββ| 189/200 [01:32<00:11, 1.08s/it]
Sampling (mamba memory): 95%|ββββββββββ| 190/200 [01:33<00:10, 1.08s/it]
Sampling (mamba memory): 96%|ββββββββββ| 191/200 [01:26<00:08, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:27<00:07, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:28<00:06, 1.02it/s]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:29<00:05, 1.02it/s]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:30<00:04, 1.02it/s]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:31<00:03, 1.02it/s]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:32<00:02, 1.02it/s]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:33<00:01, 1.02it/s]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:34<00:00, 1.02it/s]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:35<00:00, 1.01it/s]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:41<00:00, 1.01s/it] |
| 191/200 [01:27<00:09, 1.01s/it]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:28<00:08, 1.01s/it]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:29<00:07, 1.01s/it]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:30<00:06, 1.02s/it]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:31<00:05, 1.02s/it]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:32<00:04, 1.02s/it]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:33<00:03, 1.02s/it]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:34<00:02, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:35<00:01, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:36<00:00, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:41<00:00, 1.02s/it] |
| 191/200 [01:28<00:09, 1.02s/it]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:29<00:08, 1.02s/it]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:30<00:07, 1.02s/it]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:31<00:06, 1.02s/it]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:32<00:05, 1.02s/it]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:33<00:04, 1.03s/it]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:34<00:03, 1.03s/it]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:35<00:02, 1.03s/it]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:36<00:01, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:37<00:00, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:42<00:00, 1.02s/it] |
| 191/200 [01:28<00:08, 1.00it/s]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:29<00:07, 1.00it/s]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:30<00:06, 1.00it/s]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:31<00:06, 1.00s/it]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:32<00:05, 1.00s/it]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:33<00:04, 1.01s/it]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:34<00:03, 1.01s/it]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:35<00:02, 1.01s/it]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:36<00:01, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:37<00:00, 1.02s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:42<00:00, 1.03s/it] |
|
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:36<00:00, 1.00s/it][A[A
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:43<00:00, 1.03s/it] |
|
Validation DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s][A |
| 191/200 [01:27<00:08, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:28<00:07, 1.02it/s]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:29<00:06, 1.01it/s]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:30<00:05, 1.01it/s]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:31<00:04, 1.01it/s]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:32<00:03, 1.01it/s]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:33<00:02, 1.01it/s]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:34<00:01, 1.00it/s]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:35<00:00, 1.00it/s]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:36<00:00, 1.00it/s]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:42<00:00, 1.02s/it] |
| 191/200 [01:30<00:09, 1.02s/it]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:31<00:08, 1.02s/it]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:32<00:07, 1.02s/it]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:33<00:06, 1.03s/it]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:34<00:05, 1.03s/it]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:35<00:04, 1.03s/it]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:36<00:03, 1.04s/it]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:37<00:02, 1.04s/it]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:38<00:01, 1.04s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:39<00:00, 1.05s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:44<00:00, 1.05s/it] |
| 191/200 [01:34<00:09, 1.08s/it]
Sampling (mamba memory): 96%|ββββββββββ| 192/200 [01:35<00:08, 1.07s/it]
Sampling (mamba memory): 96%|ββββββββββ| 193/200 [01:37<00:07, 1.07s/it]
Sampling (mamba memory): 97%|ββββββββββ| 194/200 [01:38<00:06, 1.07s/it]
Sampling (mamba memory): 98%|ββββββββββ| 195/200 [01:39<00:05, 1.08s/it]
Sampling (mamba memory): 98%|ββββββββββ| 196/200 [01:40<00:04, 1.10s/it]
Sampling (mamba memory): 98%|ββββββββββ| 197/200 [01:41<00:03, 1.09s/it]
Sampling (mamba memory): 99%|ββββββββββ| 198/200 [01:42<00:02, 1.08s/it]
Sampling (mamba memory): 100%|ββββββββββ| 199/200 [01:43<00:01, 1.08s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:44<00:00, 1.08s/it]
Sampling (mamba memory): 100%|ββββββββββ| 200/200 [01:49<00:00, 1.10s/it] |
|
Validation DataLoader 0: 100%|ββββββββββ| 1/1 [01:44<00:00, 0.01it/s][A[2026-04-22 23:30:03,851][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('mse', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices. |
| |
| [2026-04-22 23:30:03,857][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('psnr', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices. |
| |
| [2026-04-22 23:30:03,857][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/envs/worldmem/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('lpips', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices. |
| |
| |
| [2026-04-22 23:30:04,866][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,082][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,095][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,098][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,145][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,239][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
| [2026-04-22 23:30:05,265][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
|
[A
Epoch 0: 0%| | 0/203307 [01:55<?, ?it/s, v_num=line][2026-04-22 23:30:05,270][py.warnings][WARNING] - /proj/cvl/users/x_fahkh2/WorldMem_Repro/algorithms/worldmem/models/mamba_memory.py:173: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. |
| with torch.cuda.amp.autocast(enabled=False): |
| |
|
Epoch 0: 17%|ββ | 34999/203307 [01:59<09:36, 292.12it/s, v_num=line]
Epoch 0: 17%|ββ | 34999/203307 [01:59<09:36, 292.09it/s, v_num=line]
Epoch 0: 17%|ββ | 35000/203307 [02:03<09:54, 283.29it/s, v_num=line]
Epoch 0: 17%|ββ | 35000/203307 [02:03<09:54, 283.26it/s, v_num=line]
Epoch 0: 17%|ββ | 35001/203307 [02:07<10:14, 274.07it/s, v_num=line]
Epoch 0: 17%|ββ | 35001/203307 [02:07<10:14, 274.05it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35002/203307 [02:11<10:33, 265.61it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35002/203307 [02:11<10:33, 265.58it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35003/203307 [02:15<10:53, 257.56it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35003/203307 [02:15<10:53, 257.54it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35004/203307 [02:20<11:17, 248.51it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35004/203307 [02:20<11:17, 248.51it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35005/203307 [02:24<11:36, 241.55it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35005/203307 [02:24<11:36, 241.53it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35006/203307 [02:28<11:54, 235.56it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35006/203307 [02:28<11:54, 235.56it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35007/203307 [02:32<12:14, 228.98it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35007/203307 [02:32<12:14, 228.98it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35008/203307 [02:36<12:34, 223.06it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35008/203307 [02:36<12:34, 223.06it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35009/203307 [02:41<12:55, 216.99it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35009/203307 [02:41<12:55, 216.99it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35010/203307 [02:45<13:14, 211.80it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35010/203307 [02:45<13:14, 211.79it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35011/203307 [02:50<13:37, 205.89it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35011/203307 [02:50<13:37, 205.89it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35012/203307 [02:53<13:56, 201.23it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35012/203307 [02:53<13:56, 201.23it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35013/203307 [02:59<14:20, 195.53it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35013/203307 [02:59<14:20, 195.53it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35014/203307 [03:02<14:39, 191.41it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35014/203307 [03:02<14:39, 191.41it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35015/203307 [03:07<14:59, 187.09it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35015/203307 [03:07<14:59, 187.09it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35016/203307 [03:11<15:18, 183.20it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35016/203307 [03:11<15:18, 183.20it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35017/203307 [03:15<15:39, 179.21it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35017/203307 [03:15<15:39, 179.21it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35018/203307 [03:19<15:57, 175.73it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35018/203307 [03:19<15:57, 175.72it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35019/203307 [03:23<16:18, 172.01it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35019/203307 [03:23<16:18, 172.00it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35020/203307 [03:27<16:37, 168.68it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35020/203307 [03:27<16:37, 168.68it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35021/203307 [03:32<17:03, 164.47it/s, v_num=line, training/loss=0.0931]
Epoch 0: 17%|ββ | 35021/203307 [03:32<17:03, 164.46it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35022/203307 [03:36<17:22, 161.50it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35022/203307 [03:36<17:22, 161.50it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35023/203307 [03:41<17:42, 158.41it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35023/203307 [03:41<17:42, 158.41it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35024/203307 [03:45<18:01, 155.58it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35024/203307 [03:45<18:01, 155.58it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35025/203307 [03:49<18:22, 152.62it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35025/203307 [03:49<18:22, 152.62it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35026/203307 [03:53<18:41, 150.01it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35026/203307 [03:53<18:41, 150.01it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35027/203307 [03:57<19:02, 147.27it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35027/203307 [03:57<19:02, 147.26it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35028/203307 [04:01<19:22, 144.79it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35028/203307 [04:01<19:22, 144.79it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35029/203307 [04:07<19:47, 141.76it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35029/203307 [04:07<19:47, 141.76it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35030/203307 [04:11<20:05, 139.54it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35030/203307 [04:11<20:05, 139.53it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35031/203307 [04:15<20:26, 137.18it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35031/203307 [04:15<20:26, 137.18it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35032/203307 [04:19<20:46, 135.01it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35032/203307 [04:19<20:46, 135.01it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35033/203307 [04:23<21:07, 132.78it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35033/203307 [04:23<21:07, 132.78it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35034/203307 [04:27<21:26, 130.77it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35034/203307 [04:27<21:26, 130.77it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35035/203307 [04:32<21:47, 128.66it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35035/203307 [04:32<21:47, 128.66it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35036/203307 [04:36<22:07, 126.77it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35036/203307 [04:36<22:07, 126.77it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35037/203307 [04:40<22:27, 124.85it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35037/203307 [04:40<22:27, 124.85it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35038/203307 [04:45<22:51, 122.71it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35038/203307 [04:45<22:51, 122.71it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35039/203307 [04:49<23:11, 120.90it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35039/203307 [04:49<23:11, 120.90it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35040/203307 [04:53<23:31, 119.21it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35040/203307 [04:53<23:31, 119.21it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35041/203307 [04:58<23:51, 117.54it/s, v_num=line, training/loss=0.0836]
Epoch 0: 17%|ββ | 35041/203307 [04:58<23:51, 117.54it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35042/203307 [05:02<24:11, 115.96it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35042/203307 [05:02<24:11, 115.96it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35043/203307 [05:06<24:31, 114.35it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35043/203307 [05:06<24:31, 114.35it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35044/203307 [05:10<24:51, 112.83it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35044/203307 [05:10<24:51, 112.83it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35045/203307 [05:14<25:11, 111.29it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35045/203307 [05:14<25:11, 111.28it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35046/203307 [05:19<25:34, 109.62it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35046/203307 [05:19<25:35, 109.62it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35047/203307 [05:24<25:57, 108.02it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35047/203307 [05:24<25:57, 108.02it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35048/203307 [05:28<26:16, 106.75it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35048/203307 [05:28<26:16, 106.75it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35049/203307 [05:32<26:36, 105.39it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35049/203307 [05:32<26:36, 105.38it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35050/203307 [05:36<26:55, 104.16it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35050/203307 [05:36<26:55, 104.16it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35051/203307 [05:40<27:16, 102.81it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35051/203307 [05:40<27:16, 102.81it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35052/203307 [05:44<27:35, 101.61it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35052/203307 [05:44<27:35, 101.61it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35053/203307 [05:49<27:56, 100.34it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35053/203307 [05:49<27:56, 100.34it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35054/203307 [05:53<28:15, 99.22it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35054/203307 [05:53<28:15, 99.22it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35055/203307 [05:58<28:39, 97.83it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35055/203307 [05:58<28:39, 97.82it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35056/203307 [06:02<28:59, 96.70it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35056/203307 [06:02<28:59, 96.70it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35057/203307 [06:06<29:20, 95.56it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35057/203307 [06:06<29:20, 95.56it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35058/203307 [06:10<29:39, 94.55it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35058/203307 [06:10<29:39, 94.55it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35059/203307 [06:15<30:00, 93.47it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35059/203307 [06:15<30:00, 93.47it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35060/203307 [06:19<30:18, 92.51it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35060/203307 [06:19<30:18, 92.51it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35061/203307 [06:23<30:40, 91.43it/s, v_num=line, training/loss=0.0741]
Epoch 0: 17%|ββ | 35061/203307 [06:23<30:40, 91.43it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35062/203307 [06:27<30:59, 90.50it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35062/203307 [06:27<30:59, 90.50it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35063/203307 [06:31<31:20, 89.47it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35063/203307 [06:31<31:20, 89.47it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35064/203307 [06:36<31:43, 88.39it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35064/203307 [06:36<31:43, 88.39it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35065/203307 [06:41<32:07, 87.31it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35065/203307 [06:41<32:07, 87.31it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35066/203307 [06:45<32:25, 86.46it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35066/203307 [06:45<32:25, 86.46it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35067/203307 [06:49<32:46, 85.53it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35067/203307 [06:49<32:46, 85.53it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35068/203307 [06:53<33:06, 84.71it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35068/203307 [06:53<33:06, 84.71it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35069/203307 [06:58<33:27, 83.82it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35069/203307 [06:58<33:27, 83.82it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35070/203307 [07:02<33:46, 83.02it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35070/203307 [07:02<33:46, 83.02it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35071/203307 [07:06<34:07, 82.18it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35071/203307 [07:06<34:07, 82.18it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35072/203307 [07:11<34:30, 81.25it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35072/203307 [07:11<34:30, 81.25it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35073/203307 [07:15<34:51, 80.45it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35073/203307 [07:15<34:51, 80.45it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35074/203307 [07:20<35:11, 79.67it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35074/203307 [07:20<35:11, 79.67it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35075/203307 [07:24<35:31, 78.91it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35075/203307 [07:24<35:31, 78.91it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35076/203307 [07:28<35:51, 78.21it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35076/203307 [07:28<35:51, 78.20it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35077/203307 [07:32<36:12, 77.45it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35077/203307 [07:32<36:12, 77.45it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35078/203307 [07:36<36:31, 76.76it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35078/203307 [07:36<36:31, 76.76it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35079/203307 [07:41<36:52, 76.05it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35079/203307 [07:41<36:52, 76.05it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35080/203307 [07:45<37:11, 75.38it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35080/203307 [07:45<37:11, 75.38it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35081/203307 [07:50<37:35, 74.58it/s, v_num=line, training/loss=0.0837]
Epoch 0: 17%|ββ | 35081/203307 [07:50<37:35, 74.58it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35082/203307 [07:54<37:54, 73.96it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35082/203307 [07:54<37:54, 73.96it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35083/203307 [07:59<38:18, 73.20it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35083/203307 [07:59<38:18, 73.20it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35084/203307 [08:03<38:37, 72.58it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35084/203307 [08:03<38:37, 72.58it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35085/203307 [08:07<38:57, 71.97it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35085/203307 [08:07<38:57, 71.97it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35086/203307 [08:11<39:16, 71.38it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35086/203307 [08:11<39:16, 71.37it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35087/203307 [08:15<39:37, 70.77it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35087/203307 [08:15<39:37, 70.77it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35088/203307 [08:19<39:56, 70.19it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35088/203307 [08:19<39:56, 70.19it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35089/203307 [08:24<40:16, 69.61it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35089/203307 [08:24<40:16, 69.61it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35090/203307 [08:28<40:39, 68.95it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35090/203307 [08:28<40:39, 68.95it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35091/203307 [08:33<40:59, 68.40it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35091/203307 [08:33<40:59, 68.40it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35092/203307 [08:37<41:20, 67.83it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35092/203307 [08:37<41:20, 67.83it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35093/203307 [08:41<41:39, 67.29it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35093/203307 [08:41<41:40, 67.28it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35094/203307 [08:45<41:59, 66.77it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35094/203307 [08:45<41:59, 66.77it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35095/203307 [08:49<42:19, 66.24it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35095/203307 [08:49<42:19, 66.24it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35096/203307 [08:53<42:39, 65.73it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35096/203307 [08:53<42:39, 65.73it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35097/203307 [08:58<42:58, 65.23it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35097/203307 [08:58<42:58, 65.23it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35098/203307 [09:02<43:21, 64.65it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35098/203307 [09:02<43:21, 64.65it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35099/203307 [09:06<43:41, 64.17it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35099/203307 [09:06<43:41, 64.17it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35100/203307 [09:11<44:00, 63.70it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35100/203307 [09:11<44:00, 63.69it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35101/203307 [09:16<44:24, 63.13it/s, v_num=line, training/loss=0.072]
Epoch 0: 17%|ββ | 35101/203307 [09:16<44:24, 63.13it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35102/203307 [09:20<44:44, 62.66it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35102/203307 [09:20<44:44, 62.66it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35103/203307 [09:24<45:04, 62.20it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35103/203307 [09:24<45:04, 62.20it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35104/203307 [09:28<45:23, 61.76it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35104/203307 [09:28<45:23, 61.76it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35105/203307 [09:32<45:42, 61.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35105/203307 [09:32<45:42, 61.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35106/203307 [09:36<46:01, 60.91it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35106/203307 [09:36<46:01, 60.90it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35107/203307 [09:41<46:25, 60.39it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35107/203307 [09:41<46:25, 60.39it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35108/203307 [09:45<46:44, 59.97it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35108/203307 [09:45<46:44, 59.97it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35109/203307 [09:49<47:04, 59.55it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35109/203307 [09:49<47:04, 59.55it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35110/203307 [09:53<47:24, 59.12it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35110/203307 [09:53<47:24, 59.12it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35111/203307 [09:57<47:44, 58.72it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35111/203307 [09:57<47:44, 58.72it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35112/203307 [10:01<48:03, 58.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35112/203307 [10:01<48:03, 58.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35113/203307 [10:06<48:23, 57.93it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35113/203307 [10:06<48:23, 57.93it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35114/203307 [10:10<48:42, 57.55it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35114/203307 [10:10<48:42, 57.55it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35115/203307 [10:14<49:02, 57.17it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35115/203307 [10:14<49:02, 57.17it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35116/203307 [10:19<49:26, 56.70it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35116/203307 [10:19<49:26, 56.70it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35117/203307 [10:23<49:45, 56.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35117/203307 [10:23<49:45, 56.33it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35118/203307 [10:27<50:05, 55.95it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35118/203307 [10:27<50:05, 55.95it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35119/203307 [10:32<50:29, 55.52it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35119/203307 [10:32<50:29, 55.52it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35120/203307 [10:36<50:48, 55.17it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35120/203307 [10:36<50:48, 55.17it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35121/203307 [10:40<51:08, 54.82it/s, v_num=line, training/loss=0.0877]
Epoch 0: 17%|ββ | 35121/203307 [10:40<51:08, 54.82it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35122/203307 [10:44<51:27, 54.47it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35122/203307 [10:44<51:27, 54.47it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35123/203307 [10:48<51:47, 54.12it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35123/203307 [10:48<51:47, 54.12it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35124/203307 [10:53<52:10, 53.72it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35124/203307 [10:53<52:10, 53.72it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35125/203307 [10:58<52:30, 53.38it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35125/203307 [10:58<52:30, 53.38it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35126/203307 [11:01<52:49, 53.07it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35126/203307 [11:01<52:49, 53.07it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35127/203307 [11:06<53:08, 52.74it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35127/203307 [11:06<53:08, 52.74it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35128/203307 [11:10<53:29, 52.40it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35128/203307 [11:10<53:29, 52.40it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35129/203307 [11:14<53:49, 52.07it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35129/203307 [11:14<53:49, 52.07it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35130/203307 [11:18<54:08, 51.77it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35130/203307 [11:18<54:08, 51.76it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35131/203307 [11:22<54:28, 51.46it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35131/203307 [11:22<54:28, 51.46it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35132/203307 [11:26<54:47, 51.15it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35132/203307 [11:26<54:47, 51.15it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35133/203307 [11:31<55:12, 50.77it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35133/203307 [11:31<55:12, 50.77it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35134/203307 [11:36<55:31, 50.48it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35134/203307 [11:36<55:31, 50.48it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35135/203307 [11:40<55:51, 50.18it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35135/203307 [11:40<55:51, 50.18it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35136/203307 [11:44<56:11, 49.88it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35136/203307 [11:44<56:11, 49.88it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35137/203307 [11:49<56:34, 49.54it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35137/203307 [11:49<56:34, 49.54it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35138/203307 [11:53<56:53, 49.26it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35138/203307 [11:53<56:53, 49.26it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35139/203307 [11:57<57:13, 48.97it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35139/203307 [11:57<57:13, 48.97it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35140/203307 [12:01<57:33, 48.70it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35140/203307 [12:01<57:33, 48.70it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35141/203307 [12:05<57:53, 48.42it/s, v_num=line, training/loss=0.0845]
Epoch 0: 17%|ββ | 35141/203307 [12:05<57:53, 48.42it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35142/203307 [12:10<58:16, 48.10it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35142/203307 [12:10<58:16, 48.10it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35143/203307 [12:14<58:36, 47.83it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35143/203307 [12:14<58:36, 47.83it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35144/203307 [12:18<58:55, 47.56it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35144/203307 [12:18<58:55, 47.56it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35145/203307 [12:23<59:15, 47.29it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35145/203307 [12:23<59:15, 47.29it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35146/203307 [12:27<59:36, 47.02it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35146/203307 [12:27<59:36, 47.02it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35147/203307 [12:31<59:57, 46.75it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35147/203307 [12:31<59:57, 46.75it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35148/203307 [12:35<1:00:16, 46.50it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35148/203307 [12:35<1:00:16, 46.50it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35149/203307 [12:40<1:00:36, 46.25it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35149/203307 [12:40<1:00:36, 46.25it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35150/203307 [12:44<1:00:58, 45.97it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35150/203307 [12:44<1:00:58, 45.97it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35151/203307 [12:48<1:01:18, 45.71it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35151/203307 [12:48<1:01:18, 45.71it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35152/203307 [12:53<1:01:38, 45.47it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35152/203307 [12:53<1:01:38, 45.47it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35153/203307 [12:57<1:01:58, 45.22it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35153/203307 [12:57<1:01:58, 45.22it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35154/203307 [13:01<1:02:17, 44.99it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35154/203307 [13:01<1:02:17, 44.99it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35155/203307 [13:05<1:02:38, 44.73it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35155/203307 [13:05<1:02:39, 44.73it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35156/203307 [13:09<1:02:58, 44.50it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35156/203307 [13:10<1:02:58, 44.50it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35157/203307 [13:14<1:03:18, 44.27it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35157/203307 [13:14<1:03:18, 44.27it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35158/203307 [13:18<1:03:37, 44.04it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35158/203307 [13:18<1:03:37, 44.04it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35159/203307 [13:23<1:04:00, 43.78it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35159/203307 [13:23<1:04:00, 43.78it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35160/203307 [13:27<1:04:20, 43.55it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35160/203307 [13:27<1:04:20, 43.55it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35161/203307 [13:31<1:04:40, 43.34it/s, v_num=line, training/loss=0.0778]
Epoch 0: 17%|ββ | 35161/203307 [13:31<1:04:40, 43.34it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35162/203307 [13:35<1:04:59, 43.12it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35162/203307 [13:35<1:04:59, 43.12it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35163/203307 [13:39<1:05:18, 42.91it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35163/203307 [13:39<1:05:18, 42.91it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35164/203307 [13:43<1:05:39, 42.68it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35164/203307 [13:43<1:05:39, 42.68it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35165/203307 [13:48<1:05:59, 42.47it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35165/203307 [13:48<1:05:59, 42.47it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35166/203307 [13:51<1:06:18, 42.27it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35166/203307 [13:52<1:06:18, 42.27it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35167/203307 [13:56<1:06:37, 42.06it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35167/203307 [13:56<1:06:37, 42.06it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35168/203307 [14:01<1:07:01, 41.81it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35168/203307 [14:01<1:07:02, 41.80it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35169/203307 [14:05<1:07:21, 41.60it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35169/203307 [14:05<1:07:21, 41.60it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35170/203307 [14:09<1:07:41, 41.40it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35170/203307 [14:09<1:07:41, 41.40it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35171/203307 [14:13<1:08:00, 41.20it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35171/203307 [14:13<1:08:00, 41.20it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35172/203307 [14:17<1:08:21, 41.00it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35172/203307 [14:17<1:08:21, 41.00it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35173/203307 [14:22<1:08:42, 40.78it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35173/203307 [14:22<1:08:42, 40.78it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35174/203307 [14:26<1:09:02, 40.59it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35174/203307 [14:26<1:09:02, 40.59it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35175/203307 [14:30<1:09:21, 40.40it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35175/203307 [14:30<1:09:21, 40.40it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35176/203307 [14:35<1:09:45, 40.17it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35176/203307 [14:35<1:09:45, 40.17it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35177/203307 [14:39<1:10:04, 39.99it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35177/203307 [14:39<1:10:04, 39.99it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35178/203307 [14:43<1:10:23, 39.80it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35178/203307 [14:43<1:10:24, 39.80it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35179/203307 [14:47<1:10:43, 39.62it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35179/203307 [14:47<1:10:43, 39.62it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35180/203307 [14:52<1:11:04, 39.43it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35180/203307 [14:52<1:11:04, 39.43it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35181/203307 [14:56<1:11:22, 39.26it/s, v_num=line, training/loss=0.0879]
Epoch 0: 17%|ββ | 35181/203307 [14:56<1:11:22, 39.26it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35182/203307 [15:00<1:11:44, 39.06it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35182/203307 [15:00<1:11:44, 39.06it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35183/203307 [15:04<1:12:02, 38.89it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35183/203307 [15:04<1:12:02, 38.89it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35184/203307 [15:08<1:12:23, 38.71it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35184/203307 [15:08<1:12:23, 38.71it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35185/203307 [15:13<1:12:45, 38.51it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35185/203307 [15:13<1:12:45, 38.51it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35186/203307 [15:17<1:13:05, 38.34it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35186/203307 [15:17<1:13:05, 38.34it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35187/203307 [15:21<1:13:24, 38.17it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35187/203307 [15:21<1:13:24, 38.17it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35188/203307 [15:26<1:13:44, 37.99it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35188/203307 [15:26<1:13:44, 37.99it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35189/203307 [15:30<1:14:03, 37.83it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35189/203307 [15:30<1:14:03, 37.83it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35190/203307 [15:34<1:14:23, 37.67it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35190/203307 [15:34<1:14:23, 37.67it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35191/203307 [15:38<1:14:45, 37.48it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35191/203307 [15:38<1:14:45, 37.48it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35192/203307 [15:43<1:15:05, 37.31it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35192/203307 [15:43<1:15:05, 37.31it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35193/203307 [15:47<1:15:24, 37.16it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35193/203307 [15:47<1:15:24, 37.16it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35194/203307 [15:52<1:15:48, 36.96it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35194/203307 [15:52<1:15:48, 36.96it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35195/203307 [15:56<1:16:06, 36.81it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35195/203307 [15:56<1:16:06, 36.81it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35196/203307 [16:00<1:16:27, 36.65it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35196/203307 [16:00<1:16:27, 36.65it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35197/203307 [16:04<1:16:45, 36.50it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35197/203307 [16:04<1:16:46, 36.50it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35198/203307 [16:08<1:17:05, 36.34it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35198/203307 [16:08<1:17:05, 36.34it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35199/203307 [16:12<1:17:24, 36.19it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35199/203307 [16:12<1:17:24, 36.19it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35200/203307 [16:17<1:17:46, 36.02it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35200/203307 [16:17<1:17:46, 36.02it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35201/203307 [16:21<1:18:05, 35.88it/s, v_num=line, training/loss=0.0844]
Epoch 0: 17%|ββ | 35201/203307 [16:21<1:18:05, 35.88it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35202/203307 [16:25<1:18:28, 35.71it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35202/203307 [16:25<1:18:28, 35.71it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35203/203307 [16:29<1:18:46, 35.56it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35203/203307 [16:29<1:18:46, 35.56it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35204/203307 [16:34<1:19:08, 35.40it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35204/203307 [16:34<1:19:08, 35.40it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35205/203307 [16:38<1:19:26, 35.26it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35205/203307 [16:38<1:19:27, 35.26it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35206/203307 [16:42<1:19:47, 35.11it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35206/203307 [16:42<1:19:47, 35.11it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35207/203307 [16:46<1:20:06, 34.98it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35207/203307 [16:46<1:20:06, 34.98it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35208/203307 [16:50<1:20:26, 34.83it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35208/203307 [16:51<1:20:26, 34.82it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35209/203307 [16:55<1:20:49, 34.66it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35209/203307 [16:55<1:20:49, 34.66it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35210/203307 [16:59<1:21:08, 34.52it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35210/203307 [16:59<1:21:09, 34.52it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35211/203307 [17:04<1:21:31, 34.37it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35211/203307 [17:04<1:21:31, 34.37it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35212/203307 [17:08<1:21:51, 34.22it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35212/203307 [17:08<1:21:51, 34.22it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35213/203307 [17:12<1:22:10, 34.09it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35213/203307 [17:12<1:22:10, 34.09it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35214/203307 [17:17<1:22:30, 33.96it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35214/203307 [17:17<1:22:30, 33.96it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35215/203307 [17:21<1:22:49, 33.82it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35215/203307 [17:21<1:22:49, 33.82it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35216/203307 [17:25<1:23:09, 33.69it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35216/203307 [17:25<1:23:09, 33.69it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35217/203307 [17:29<1:23:28, 33.56it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35217/203307 [17:29<1:23:28, 33.56it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35218/203307 [17:33<1:23:50, 33.41it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35218/203307 [17:34<1:23:50, 33.41it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35219/203307 [17:38<1:24:09, 33.29it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35219/203307 [17:38<1:24:09, 33.29it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35220/203307 [17:43<1:24:33, 33.13it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35220/203307 [17:43<1:24:33, 33.13it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35221/203307 [17:47<1:24:52, 33.01it/s, v_num=line, training/loss=0.0834]
Epoch 0: 17%|ββ | 35221/203307 [17:47<1:24:52, 33.00it/s, v_num=line, training/loss=0.0896]
Epoch 0: 17%|ββ | 35222/203307 [17:51<1:25:12, 32.88it/s, v_num=line, training/loss=0.0896]
Epoch 0: 17%|ββ | 35222/203307 [17:51<1:25:12, 32.88it/s, v_num=line, training/loss=0.0896]
Epoch 0: 17%|ββ | 35223/203307 [17:55<1:25:31, 32.76it/s, v_num=line, training/loss=0.0896]
Epoch 0: 17%|β |
