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  1. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +3 -0
  2. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +3 -0
  3. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +534 -0
  4. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +67 -0
  5. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +3 -0
  6. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +3 -0
  7. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +534 -0
  8. data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +67 -0
data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt ADDED
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1
+ 2024-11-26,13:26:17 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8.
2
+ 2024-11-26,13:26:17 | INFO | Loading ViT-L-14-336 model config.
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+ 2024-11-26,13:26:20 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt).
4
+ 2024-11-26,13:26:28 | INFO | Model:
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+ 2024-11-26,13:26:28 | INFO | CLIP(
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+ (visual): VisualTransformer(
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+ (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
8
+ (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
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+ (transformer): Transformer(
10
+ (resblocks): ModuleList(
11
+ (0-23): 24 x ResidualAttentionBlock(
12
+ (attn): MultiheadAttention(
13
+ (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
14
+ )
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+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
16
+ (mlp): Sequential(
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+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
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+ (gelu): QuickGELU()
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+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
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+ )
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+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
22
+ )
23
+ )
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+ )
25
+ (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
26
+ )
27
+ (transformer): Transformer(
28
+ (resblocks): ModuleList(
29
+ (0-11): 12 x ResidualAttentionBlock(
30
+ (attn): MultiheadAttention(
31
+ (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
32
+ )
33
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
34
+ (mlp): Sequential(
35
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
36
+ (gelu): QuickGELU()
37
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
38
+ )
39
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
40
+ )
41
+ )
42
+ )
43
+ (token_embedding): Embedding(49408, 768)
44
+ (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
45
+ )
46
+ 2024-11-26,13:26:28 | INFO | Params:
47
+ 2024-11-26,13:26:28 | INFO | batch_size: 64
48
+ 2024-11-26,13:26:28 | INFO | beta1: 0.9
49
+ 2024-11-26,13:26:28 | INFO | beta2: 0.98
50
+ 2024-11-26,13:26:28 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints
51
+ 2024-11-26,13:26:28 | INFO | copy_codebase: False
52
+ 2024-11-26,13:26:28 | INFO | csv_caption_key: caption
53
+ 2024-11-26,13:26:28 | INFO | csv_hard_captions_key: neg_caption
54
+ 2024-11-26,13:26:28 | INFO | csv_img_key: img_path
55
+ 2024-11-26,13:26:28 | INFO | csv_separator: ,
56
+ 2024-11-26,13:26:28 | INFO | dataset_resampled: False
57
+ 2024-11-26,13:26:28 | INFO | dataset_type: csv
58
+ 2024-11-26,13:26:28 | INFO | ddp_static_graph: False
59
+ 2024-11-26,13:26:28 | INFO | debug: False
60
+ 2024-11-26,13:26:28 | INFO | device: cuda:0
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+ 2024-11-26,13:26:28 | INFO | dist_backend: nccl
62
+ 2024-11-26,13:26:28 | INFO | dist_url: env://
63
+ 2024-11-26,13:26:28 | INFO | distributed: True
64
+ 2024-11-26,13:26:28 | INFO | epochs: 2
65
+ 2024-11-26,13:26:28 | INFO | eps: 1e-06
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+ 2024-11-26,13:26:28 | INFO | force_quick_gelu: True
67
+ 2024-11-26,13:26:28 | INFO | gather_with_grad: False
68
+ 2024-11-26,13:26:28 | INFO | grad_checkpointing: False
69
+ 2024-11-26,13:26:28 | INFO | horovod: False
70
+ 2024-11-26,13:26:28 | INFO | imagenet_v2: None
71
+ 2024-11-26,13:26:28 | INFO | imagenet_val: None
72
+ 2024-11-26,13:26:28 | INFO | local_loss: False
73
+ 2024-11-26,13:26:28 | INFO | local_rank: 0
74
+ 2024-11-26,13:26:28 | INFO | lock_image: False
75
+ 2024-11-26,13:26:28 | INFO | lock_image_freeze_bn_stats: False
76
+ 2024-11-26,13:26:28 | INFO | lock_image_unlocked_groups: 0
77
+ 2024-11-26,13:26:28 | INFO | log_level: 20
78
+ 2024-11-26,13:26:28 | INFO | log_local: False
79
+ 2024-11-26,13:26:28 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log
80
+ 2024-11-26,13:26:28 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten
81
+ 2024-11-26,13:26:28 | INFO | lr: 1e-06
82
+ 2024-11-26,13:26:28 | INFO | model: ViT-L-14-336
83
+ 2024-11-26,13:26:28 | INFO | name: 2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp
84
+ 2024-11-26,13:26:28 | INFO | no_set_device_rank: False
85
+ 2024-11-26,13:26:28 | INFO | norm_gradient_clip: None
86
+ 2024-11-26,13:26:28 | INFO | precision: amp
87
+ 2024-11-26,13:26:28 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt
88
+ 2024-11-26,13:26:28 | INFO | pretrained_image: False
89
+ 2024-11-26,13:26:28 | INFO | rank: 0
90
+ 2024-11-26,13:26:28 | INFO | report_to: wandb
91
+ 2024-11-26,13:26:28 | INFO | resume: None
92
+ 2024-11-26,13:26:28 | INFO | save_frequency: 1
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+ 2024-11-26,13:26:28 | INFO | save_most_recent: False
94
+ 2024-11-26,13:26:28 | INFO | seed: 0
95
+ 2024-11-26,13:26:28 | INFO | skip_scheduler: False
96
+ 2024-11-26,13:26:28 | INFO | tensorboard: False
97
+ 2024-11-26,13:26:28 | INFO | tensorboard_path:
98
+ 2024-11-26,13:26:28 | INFO | torchscript: False
99
+ 2024-11-26,13:26:28 | INFO | trace: False
100
+ 2024-11-26,13:26:28 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv
101
+ 2024-11-26,13:26:28 | INFO | train_num_samples: None
102
+ 2024-11-26,13:26:28 | INFO | use_bn_sync: False
103
+ 2024-11-26,13:26:28 | INFO | val_data: None
104
+ 2024-11-26,13:26:28 | INFO | val_frequency: 1
105
+ 2024-11-26,13:26:28 | INFO | val_num_samples: None
106
+ 2024-11-26,13:26:28 | INFO | wandb: True
107
+ 2024-11-26,13:26:28 | INFO | wandb_notes:
108
+ 2024-11-26,13:26:28 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten
109
+ 2024-11-26,13:26:28 | INFO | warmup: 0
110
+ 2024-11-26,13:26:28 | INFO | wd: 0.1
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+ 2024-11-26,13:26:28 | INFO | workers: 4
112
+ 2024-11-26,13:26:28 | INFO | world_size: 8
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+ 2024-11-26,13:26:28 | INFO | zeroshot_frequency: 2
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+ 2024-11-26,13:27:23 | INFO | Init a wandb project!
115
+ 2024-11-26,13:27:29 | INFO | Start epoch 0
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+ 2024-11-26,13:27:36 | INFO | Train Epoch: 0 [ 512/10637090 (0%)] Loss: 5.5496 (5.550) Data (t): 2.793 Batch (t): 6.830, 74.9673/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,13:29:08 | INFO | Train Epoch: 0 [ 51712/10637090 (0%)] Loss: 2.6938 (4.122) Data (t): 0.001 Batch (t): 0.913, 561.888/s LR: 0.000001 Logit Scale: 99.998 - V4
118
+ 2024-11-26,13:30:38 | INFO | Train Epoch: 0 [ 102912/10637090 (1%)] Loss: 2.2569 (3.500) Data (t): 0.001 Batch (t): 0.909, 562.911/s LR: 0.000001 Logit Scale: 99.998 - V4
119
+ 2024-11-26,13:32:10 | INFO | Train Epoch: 0 [ 154112/10637090 (1%)] Loss: 2.0755 (3.144) Data (t): 0.001 Batch (t): 0.915, 563.167/s LR: 0.000001 Logit Scale: 99.998 - V4
120
+ 2024-11-26,13:33:45 | INFO | Train Epoch: 0 [ 205312/10637090 (2%)] Loss: 2.0623 (2.928) Data (t): 0.001 Batch (t): 0.948, 562.861/s LR: 0.000001 Logit Scale: 100.000 - V4
121
+ 2024-11-26,13:35:16 | INFO | Train Epoch: 0 [ 256512/10637090 (2%)] Loss: 1.8247 (2.744) Data (t): 0.001 Batch (t): 0.908, 564.203/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,13:36:46 | INFO | Train Epoch: 0 [ 307712/10637090 (3%)] Loss: 1.7445 (2.601) Data (t): 0.001 Batch (t): 0.908, 566.755/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,13:38:17 | INFO | Train Epoch: 0 [ 358912/10637090 (3%)] Loss: 1.8082 (2.502) Data (t): 0.001 Batch (t): 0.908, 565.794/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,13:39:48 | INFO | Train Epoch: 0 [ 410112/10637090 (4%)] Loss: 1.7109 (2.414) Data (t): 0.001 Batch (t): 0.913, 566.573/s LR: 0.000001 Logit Scale: 100.000 - V4
125
+ 2024-11-26,13:41:24 | INFO | Train Epoch: 0 [ 461312/10637090 (4%)] Loss: 1.5298 (2.326) Data (t): 0.001 Batch (t): 0.955, 567.027/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,13:42:55 | INFO | Train Epoch: 0 [ 512512/10637090 (5%)] Loss: 1.6165 (2.261) Data (t): 0.001 Batch (t): 0.906, 563.278/s LR: 0.000001 Logit Scale: 100.000 - V4
127
+ 2024-11-26,13:44:25 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.5626 (2.203) Data (t): 0.001 Batch (t): 0.907, 565.448/s LR: 0.000001 Logit Scale: 100.000 - V4
128
+ 2024-11-26,13:45:56 | INFO | Train Epoch: 0 [ 614912/10637090 (6%)] Loss: 1.6279 (2.159) Data (t): 0.001 Batch (t): 0.906, 566.904/s LR: 0.000001 Logit Scale: 100.000 - V4
129
+ 2024-11-26,13:47:26 | INFO | Train Epoch: 0 [ 666112/10637090 (6%)] Loss: 1.5003 (2.112) Data (t): 0.001 Batch (t): 0.907, 565.359/s LR: 0.000001 Logit Scale: 100.000 - V4
130
+ 2024-11-26,13:49:01 | INFO | Train Epoch: 0 [ 717312/10637090 (7%)] Loss: 1.4429 (2.067) Data (t): 0.001 Batch (t): 0.946, 567.412/s LR: 0.000001 Logit Scale: 100.000 - V4
131
+ 2024-11-26,13:50:32 | INFO | Train Epoch: 0 [ 768512/10637090 (7%)] Loss: 1.6723 (2.042) Data (t): 0.001 Batch (t): 0.906, 562.600/s LR: 0.000001 Logit Scale: 100.000 - V4
132
+ 2024-11-26,13:52:02 | INFO | Train Epoch: 0 [ 819712/10637090 (8%)] Loss: 1.5646 (2.014) Data (t): 0.001 Batch (t): 0.906, 566.678/s LR: 0.000001 Logit Scale: 100.000 - V4
133
+ 2024-11-26,13:53:33 | INFO | Train Epoch: 0 [ 870912/10637090 (8%)] Loss: 1.4842 (1.985) Data (t): 0.001 Batch (t): 0.906, 563.976/s LR: 0.000001 Logit Scale: 100.000 - V4
134
+ 2024-11-26,13:55:03 | INFO | Train Epoch: 0 [ 922112/10637090 (9%)] Loss: 1.6805 (1.969) Data (t): 0.001 Batch (t): 0.906, 564.824/s LR: 0.000001 Logit Scale: 100.000 - V4
135
+ 2024-11-26,13:56:40 | INFO | Train Epoch: 0 [ 973312/10637090 (9%)] Loss: 1.5527 (1.948) Data (t): 0.001 Batch (t): 0.965, 563.200/s LR: 0.000001 Logit Scale: 100.000 - V4
136
+ 2024-11-26,13:58:11 | INFO | Train Epoch: 0 [ 1024512/10637090 (10%)] Loss: 1.4393 (1.924) Data (t): 0.001 Batch (t): 0.906, 563.626/s LR: 0.000001 Logit Scale: 100.000 - V4
137
+ 2024-11-26,13:59:41 | INFO | Train Epoch: 0 [ 1075712/10637090 (10%)] Loss: 1.5999 (1.909) Data (t): 0.001 Batch (t): 0.906, 564.455/s LR: 0.000001 Logit Scale: 100.000 - V4
138
+ 2024-11-26,14:01:12 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.4600 (1.890) Data (t): 0.001 Batch (t): 0.906, 566.176/s LR: 0.000001 Logit Scale: 100.000 - V4
139
+ 2024-11-26,14:02:42 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.4896 (1.873) Data (t): 0.001 Batch (t): 0.906, 565.815/s LR: 0.000001 Logit Scale: 100.000 - V4
140
+ 2024-11-26,14:04:17 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.5681 (1.861) Data (t): 0.001 Batch (t): 0.946, 565.761/s LR: 0.000001 Logit Scale: 100.000 - V4
141
+ 2024-11-26,14:05:50 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.4004 (1.843) Data (t): 0.001 Batch (t): 0.926, 565.760/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:07:20 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.4362 (1.828) Data (t): 0.001 Batch (t): 0.906, 562.928/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:08:51 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.4948 (1.816) Data (t): 0.001 Batch (t): 0.906, 565.679/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:10:22 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.5365 (1.806) Data (t): 0.001 Batch (t): 0.907, 565.107/s LR: 0.000001 Logit Scale: 100.000 - V4
145
+ 2024-11-26,14:11:53 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.5180 (1.797) Data (t): 0.001 Batch (t): 0.912, 565.333/s LR: 0.000001 Logit Scale: 100.000 - V4
146
+ 2024-11-26,14:13:29 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.6162 (1.791) Data (t): 0.001 Batch (t): 0.961, 565.295/s LR: 0.000001 Logit Scale: 100.000 - V4
147
+ 2024-11-26,14:15:00 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.4370 (1.780) Data (t): 0.001 Batch (t): 0.907, 565.087/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:16:30 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.4059 (1.769) Data (t): 0.001 Batch (t): 0.906, 565.510/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:18:01 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.5552 (1.762) Data (t): 0.001 Batch (t): 0.906, 564.604/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:19:32 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.3878 (1.752) Data (t): 0.001 Batch (t): 0.912, 565.169/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:21:08 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.3488 (1.740) Data (t): 0.001 Batch (t): 0.965, 563.128/s LR: 0.000001 Logit Scale: 100.000 - V4
152
+ 2024-11-26,14:22:39 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.5331 (1.735) Data (t): 0.001 Batch (t): 0.907, 563.040/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:24:10 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.4770 (1.728) Data (t): 0.001 Batch (t): 0.907, 564.176/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:25:40 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.4729 (1.721) Data (t): 0.001 Batch (t): 0.906, 565.956/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:27:11 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.2389 (1.709) Data (t): 0.001 Batch (t): 0.906, 565.150/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:28:48 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.4484 (1.703) Data (t): 0.001 Batch (t): 0.970, 565.558/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:30:19 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.4061 (1.696) Data (t): 0.001 Batch (t): 0.905, 565.588/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:31:49 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.1981 (1.684) Data (t): 0.001 Batch (t): 0.905, 565.478/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:33:20 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.4048 (1.678) Data (t): 0.001 Batch (t): 0.906, 565.190/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:34:50 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.2888 (1.669) Data (t): 0.001 Batch (t): 0.906, 566.264/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:36:26 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.3869 (1.663) Data (t): 0.001 Batch (t): 0.960, 563.551/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:37:58 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.3551 (1.657) Data (t): 0.001 Batch (t): 0.916, 557.168/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:39:28 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.4228 (1.652) Data (t): 0.001 Batch (t): 0.906, 564.947/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:40:59 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.3846 (1.646) Data (t): 0.001 Batch (t): 0.907, 564.711/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:42:30 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.3890 (1.641) Data (t): 0.001 Batch (t): 0.906, 564.274/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:44:04 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.4708 (1.638) Data (t): 0.001 Batch (t): 0.939, 564.029/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:45:37 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.4605 (1.634) Data (t): 0.001 Batch (t): 0.935, 566.684/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:47:08 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.2774 (1.628) Data (t): 0.001 Batch (t): 0.905, 562.427/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:48:38 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.3679 (1.623) Data (t): 0.001 Batch (t): 0.906, 563.969/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:50:09 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.3890 (1.619) Data (t): 0.001 Batch (t): 0.905, 562.776/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:51:41 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.1875 (1.611) Data (t): 0.001 Batch (t): 0.919, 561.616/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:53:16 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.3898 (1.607) Data (t): 0.001 Batch (t): 0.957, 564.746/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:54:47 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.3824 (1.603) Data (t): 0.001 Batch (t): 0.905, 567.549/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:56:17 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.3579 (1.599) Data (t): 0.001 Batch (t): 0.905, 564.705/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:57:48 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.2568 (1.593) Data (t): 0.001 Batch (t): 0.905, 566.225/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,14:59:19 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.3294 (1.589) Data (t): 0.001 Batch (t): 0.913, 566.800/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:00:54 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.2655 (1.584) Data (t): 0.001 Batch (t): 0.953, 564.999/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:02:26 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.1694 (1.577) Data (t): 0.001 Batch (t): 0.916, 565.150/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:03:56 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.3314 (1.573) Data (t): 0.001 Batch (t): 0.906, 565.527/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:05:27 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.3500 (1.570) Data (t): 0.001 Batch (t): 0.907, 562.142/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:06:58 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.3836 (1.567) Data (t): 0.001 Batch (t): 0.906, 563.983/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:08:34 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.3994 (1.565) Data (t): 0.001 Batch (t): 0.960, 564.818/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:10:05 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.3057 (1.561) Data (t): 0.001 Batch (t): 0.916, 565.366/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:11:36 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.4654 (1.559) Data (t): 0.001 Batch (t): 0.906, 564.994/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:13:07 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.1907 (1.554) Data (t): 0.001 Batch (t): 0.906, 566.278/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:14:37 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.1220 (1.548) Data (t): 0.001 Batch (t): 0.905, 566.043/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:16:11 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.3814 (1.546) Data (t): 0.001 Batch (t): 0.939, 564.484/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:17:45 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.3244 (1.543) Data (t): 0.001 Batch (t): 0.937, 566.922/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:19:15 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.4181 (1.541) Data (t): 0.001 Batch (t): 0.905, 566.004/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:20:46 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.1651 (1.536) Data (t): 0.001 Batch (t): 0.906, 565.101/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:22:16 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.3660 (1.534) Data (t): 0.001 Batch (t): 0.905, 566.687/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:23:49 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.4300 (1.532) Data (t): 0.001 Batch (t): 0.925, 564.653/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:25:24 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.3340 (1.530) Data (t): 0.001 Batch (t): 0.950, 566.433/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:26:54 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.3072 (1.527) Data (t): 0.001 Batch (t): 0.905, 565.921/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:28:25 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.2631 (1.524) Data (t): 0.001 Batch (t): 0.906, 563.596/s LR: 0.000001 Logit Scale: 100.000 - V4
196
+ 2024-11-26,15:29:55 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.2469 (1.520) Data (t): 0.001 Batch (t): 0.906, 567.034/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:31:27 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.1652 (1.516) Data (t): 0.001 Batch (t): 0.920, 564.787/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:33:02 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.3874 (1.514) Data (t): 0.001 Batch (t): 0.946, 566.911/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:34:34 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.3164 (1.512) Data (t): 0.001 Batch (t): 0.915, 566.698/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:36:04 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.3739 (1.511) Data (t): 0.001 Batch (t): 0.905, 566.523/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:37:35 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.1820 (1.507) Data (t): 0.001 Batch (t): 0.906, 564.028/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:39:05 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.2819 (1.504) Data (t): 0.001 Batch (t): 0.906, 562.150/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:40:41 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.4259 (1.503) Data (t): 0.001 Batch (t): 0.960, 264.610/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:42:13 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.2922 (1.501) Data (t): 0.001 Batch (t): 0.916, 566.770/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:43:43 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.2810 (1.498) Data (t): 0.001 Batch (t): 0.905, 566.095/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:45:14 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.3400 (1.497) Data (t): 0.001 Batch (t): 0.905, 564.121/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:46:44 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.3546 (1.495) Data (t): 0.001 Batch (t): 0.905, 565.485/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:48:18 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.2837 (1.493) Data (t): 0.001 Batch (t): 0.939, 567.428/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:49:52 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.3378 (1.491) Data (t): 0.001 Batch (t): 0.937, 563.893/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:51:23 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.2499 (1.489) Data (t): 0.001 Batch (t): 0.906, 562.105/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:52:53 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.3169 (1.487) Data (t): 0.001 Batch (t): 0.906, 562.888/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:54:24 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.3130 (1.485) Data (t): 0.001 Batch (t): 0.906, 558.000/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:55:58 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.2658 (1.483) Data (t): 0.001 Batch (t): 0.940, 564.414/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:57:32 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.3192 (1.481) Data (t): 0.001 Batch (t): 0.936, 565.290/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,15:59:02 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.1244 (1.478) Data (t): 0.001 Batch (t): 0.906, 565.728/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:00:33 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.1769 (1.475) Data (t): 0.001 Batch (t): 0.906, 566.048/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:02:03 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.2274 (1.472) Data (t): 0.001 Batch (t): 0.906, 563.096/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:03:36 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.2307 (1.470) Data (t): 0.001 Batch (t): 0.926, 567.223/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:05:10 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.1837 (1.467) Data (t): 0.001 Batch (t): 0.940, 566.891/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:06:42 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.3002 (1.466) Data (t): 0.001 Batch (t): 0.916, 565.741/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:08:12 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.1461 (1.463) Data (t): 0.001 Batch (t): 0.905, 565.395/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:09:43 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.3009 (1.461) Data (t): 0.001 Batch (t): 0.905, 565.003/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:11:14 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.3348 (1.460) Data (t): 0.001 Batch (t): 0.912, 562.009/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:12:49 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.3173 (1.459) Data (t): 0.001 Batch (t): 0.954, 566.691/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:14:21 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.1887 (1.456) Data (t): 0.001 Batch (t): 0.916, 566.148/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:15:51 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.2399 (1.454) Data (t): 0.001 Batch (t): 0.905, 564.712/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:17:22 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.1904 (1.452) Data (t): 0.001 Batch (t): 0.906, 565.968/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:18:52 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.1559 (1.449) Data (t): 0.001 Batch (t): 0.905, 566.546/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:20:26 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.2268 (1.447) Data (t): 0.001 Batch (t): 0.940, 563.824/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:22:00 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 1.1154 (1.444) Data (t): 0.001 Batch (t): 0.937, 565.785/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:23:31 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.2780 (1.443) Data (t): 0.001 Batch (t): 0.904, 563.530/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:25:01 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.2995 (1.442) Data (t): 0.001 Batch (t): 0.906, 564.827/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:26:32 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.2820 (1.440) Data (t): 0.001 Batch (t): 0.906, 566.366/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:28:06 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 1.0357 (1.437) Data (t): 0.001 Batch (t): 0.939, 568.102/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:29:39 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.1916 (1.435) Data (t): 0.001 Batch (t): 0.936, 566.852/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:31:10 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.3087 (1.434) Data (t): 0.001 Batch (t): 0.905, 565.900/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:32:40 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.2553 (1.432) Data (t): 0.001 Batch (t): 0.905, 564.135/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:34:11 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.1910 (1.430) Data (t): 0.001 Batch (t): 0.905, 568.550/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:35:45 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.2113 (1.429) Data (t): 0.001 Batch (t): 0.941, 565.892/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:37:18 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.2448 (1.427) Data (t): 0.001 Batch (t): 0.928, 566.225/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:38:49 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.3494 (1.427) Data (t): 0.001 Batch (t): 0.916, 564.945/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:40:20 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.2914 (1.425) Data (t): 0.001 Batch (t): 0.904, 565.027/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:41:50 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.2394 (1.424) Data (t): 0.001 Batch (t): 0.905, 564.575/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:43:23 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.1204 (1.422) Data (t): 0.001 Batch (t): 0.926, 564.557/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:44:57 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.1377 (1.419) Data (t): 0.001 Batch (t): 0.940, 566.221/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:46:28 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.1403 (1.417) Data (t): 0.001 Batch (t): 0.915, 566.595/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:47:59 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.1797 (1.416) Data (t): 0.001 Batch (t): 0.905, 566.042/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:49:29 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.1877 (1.414) Data (t): 0.001 Batch (t): 0.904, 564.831/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:51:00 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.2662 (1.413) Data (t): 0.001 Batch (t): 0.912, 566.063/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:52:33 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.3159 (1.412) Data (t): 0.001 Batch (t): 0.932, 565.862/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:54:07 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.2148 (1.411) Data (t): 0.001 Batch (t): 0.937, 565.621/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:55:38 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.2995 (1.410) Data (t): 0.001 Batch (t): 0.906, 563.823/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:57:08 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 1.1218 (1.408) Data (t): 0.001 Batch (t): 0.906, 565.513/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,16:58:39 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.3295 (1.407) Data (t): 0.001 Batch (t): 0.906, 563.177/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:00:13 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.1448 (1.405) Data (t): 0.001 Batch (t): 0.941, 563.472/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:01:47 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1801 (1.404) Data (t): 0.001 Batch (t): 0.938, 566.473/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:03:18 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.1822 (1.402) Data (t): 0.001 Batch (t): 0.906, 565.609/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:04:48 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.3013 (1.401) Data (t): 0.001 Batch (t): 0.906, 566.407/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:06:19 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 1.2890 (1.401) Data (t): 0.001 Batch (t): 0.906, 565.871/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:07:53 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.2193 (1.399) Data (t): 0.001 Batch (t): 0.942, 564.987/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:09:26 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.2400 (1.398) Data (t): 0.001 Batch (t): 0.927, 565.552/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:10:57 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.2489 (1.397) Data (t): 0.001 Batch (t): 0.917, 566.312/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:12:28 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.3111 (1.397) Data (t): 0.001 Batch (t): 0.906, 566.103/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:13:59 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.1942 (1.395) Data (t): 0.001 Batch (t): 0.906, 566.083/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:15:33 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 1.2478 (1.394) Data (t): 0.001 Batch (t): 0.941, 568.291/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:17:05 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.1523 (1.393) Data (t): 0.001 Batch (t): 0.926, 566.661/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:18:37 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.1149 (1.391) Data (t): 0.001 Batch (t): 0.916, 580.253/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:20:07 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.3505 (1.391) Data (t): 0.001 Batch (t): 0.906, 566.431/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:21:38 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.1597 (1.389) Data (t): 0.001 Batch (t): 0.904, 565.911/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:23:10 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.1724 (1.388) Data (t): 0.001 Batch (t): 0.927, 565.110/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:24:42 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.2565 (1.387) Data (t): 0.001 Batch (t): 0.919, 564.593/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:26:16 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.1642 (1.385) Data (t): 0.001 Batch (t): 0.937, 566.546/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:27:47 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.2992 (1.385) Data (t): 0.001 Batch (t): 0.905, 565.761/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:29:17 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.3238 (1.385) Data (t): 0.001 Batch (t): 0.905, 563.700/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:30:49 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.2713 (1.384) Data (t): 0.001 Batch (t): 0.919, 568.559/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:32:22 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.1442 (1.382) Data (t): 0.001 Batch (t): 0.927, 566.255/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:33:55 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 1.0954 (1.381) Data (t): 0.001 Batch (t): 0.937, 565.181/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:35:26 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.3564 (1.380) Data (t): 0.001 Batch (t): 0.905, 566.639/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:36:56 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.2590 (1.380) Data (t): 0.001 Batch (t): 0.904, 564.117/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:38:27 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.1770 (1.378) Data (t): 0.001 Batch (t): 0.905, 564.134/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:40:01 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.1348 (1.377) Data (t): 0.001 Batch (t): 0.941, 563.761/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:41:33 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.1695 (1.376) Data (t): 0.001 Batch (t): 0.926, 568.251/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:43:05 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.1918 (1.375) Data (t): 0.001 Batch (t): 0.916, 564.239/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:44:36 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.3536 (1.375) Data (t): 0.001 Batch (t): 0.905, 565.739/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:46:06 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.1845 (1.373) Data (t): 0.001 Batch (t): 0.905, 565.182/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:47:40 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.1522 (1.372) Data (t): 0.001 Batch (t): 0.941, 567.618/s LR: 0.000001 Logit Scale: 100.000 - V4
287
+ 2024-11-26,17:49:12 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.2960 (1.372) Data (t): 0.001 Batch (t): 0.915, 564.128/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:50:44 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.1738 (1.371) Data (t): 0.001 Batch (t): 0.927, 563.781/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:52:15 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.2121 (1.370) Data (t): 0.001 Batch (t): 0.905, 566.281/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:53:45 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.2586 (1.369) Data (t): 0.001 Batch (t): 0.906, 566.052/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:55:19 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.3684 (1.369) Data (t): 0.001 Batch (t): 0.934, 567.096/s LR: 0.000001 Logit Scale: 100.000 - V4
292
+ 2024-11-26,17:56:50 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.1778 (1.368) Data (t): 0.001 Batch (t): 0.913, 565.718/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:58:24 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.1981 (1.367) Data (t): 0.001 Batch (t): 0.938, 566.629/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,17:59:55 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.2079 (1.366) Data (t): 0.001 Batch (t): 0.906, 564.911/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:01:25 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.3082 (1.366) Data (t): 0.001 Batch (t): 0.905, 565.323/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:02:58 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.1537 (1.365) Data (t): 0.001 Batch (t): 0.927, 566.460/s LR: 0.000001 Logit Scale: 100.000 - V4
297
+ 2024-11-26,18:04:30 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.2238 (1.364) Data (t): 0.001 Batch (t): 0.919, 566.179/s LR: 0.000001 Logit Scale: 100.000 - V4
298
+ 2024-11-26,18:06:02 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.1675 (1.363) Data (t): 0.001 Batch (t): 0.926, 564.695/s LR: 0.000001 Logit Scale: 100.000 - V4
299
+ 2024-11-26,18:07:34 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.1506 (1.362) Data (t): 0.001 Batch (t): 0.916, 565.429/s LR: 0.000001 Logit Scale: 100.000 - V4
300
+ 2024-11-26,18:09:04 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 1.2045 (1.361) Data (t): 0.001 Batch (t): 0.905, 565.161/s LR: 0.000001 Logit Scale: 100.000 - V4
301
+ 2024-11-26,18:10:36 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 1.0813 (1.359) Data (t): 0.001 Batch (t): 0.912, 566.481/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:12:09 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.2197 (1.358) Data (t): 0.001 Batch (t): 0.936, 565.641/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:13:42 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.1496 (1.357) Data (t): 0.001 Batch (t): 0.927, 566.480/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:15:14 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.1646 (1.356) Data (t): 0.001 Batch (t): 0.916, 565.670/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:16:44 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.1273 (1.355) Data (t): 0.001 Batch (t): 0.906, 564.308/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:18:15 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.3357 (1.355) Data (t): 0.001 Batch (t): 0.905, 565.224/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:19:49 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 1.0409 (1.353) Data (t): 0.001 Batch (t): 0.942, 567.549/s LR: 0.000001 Logit Scale: 100.000 - V4
308
+ 2024-11-26,18:21:21 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.2838 (1.353) Data (t): 0.001 Batch (t): 0.926, 565.977/s LR: 0.000001 Logit Scale: 100.000 - V4
309
+ 2024-11-26,18:22:53 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.3626 (1.353) Data (t): 0.001 Batch (t): 0.916, 563.409/s LR: 0.000001 Logit Scale: 100.000 - V4
310
+ 2024-11-26,18:24:24 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.1998 (1.352) Data (t): 0.001 Batch (t): 0.906, 564.122/s LR: 0.000001 Logit Scale: 100.000 - V4
311
+ 2024-11-26,18:25:54 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.2297 (1.352) Data (t): 0.001 Batch (t): 0.906, 565.543/s LR: 0.000001 Logit Scale: 100.000 - V4
312
+ 2024-11-26,18:27:29 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.2715 (1.351) Data (t): 0.001 Batch (t): 0.943, 566.838/s LR: 0.000001 Logit Scale: 100.000 - V4
313
+ 2024-11-26,18:28:59 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.2865 (1.351) Data (t): 0.001 Batch (t): 0.906, 563.765/s LR: 0.000001 Logit Scale: 100.000 - V4
314
+ 2024-11-26,18:30:32 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.2209 (1.350) Data (t): 0.001 Batch (t): 0.928, 561.596/s LR: 0.000001 Logit Scale: 100.000 - V4
315
+ 2024-11-26,18:32:03 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.3355 (1.350) Data (t): 0.001 Batch (t): 0.907, 558.925/s LR: 0.000001 Logit Scale: 100.000 - V4
316
+ 2024-11-26,18:33:33 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.1038 (1.349) Data (t): 0.001 Batch (t): 0.906, 564.300/s LR: 0.000001 Logit Scale: 100.000 - V4
317
+ 2024-11-26,18:35:06 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.2405 (1.348) Data (t): 0.001 Batch (t): 0.928, 567.856/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:36:38 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 1.0198 (1.347) Data (t): 0.001 Batch (t): 0.919, 563.372/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:38:12 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.1710 (1.346) Data (t): 0.001 Batch (t): 0.938, 249.357/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:39:42 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.3286 (1.346) Data (t): 0.001 Batch (t): 0.905, 565.746/s LR: 0.000001 Logit Scale: 100.000 - V4
321
+ 2024-11-26,18:41:13 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 1.0912 (1.345) Data (t): 0.001 Batch (t): 0.904, 567.087/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:42:45 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.4040 (1.345) Data (t): 0.001 Batch (t): 0.928, 567.309/s LR: 0.000001 Logit Scale: 100.000 - V4
323
+ 2024-11-26,18:44:17 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.2265 (1.344) Data (t): 0.001 Batch (t): 0.920, 565.180/s LR: 0.000001 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:45:26 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.3101 (1.344) Data (t): 0.001 Batch (t): 0.933, 568.099/s LR: 0.000001 Logit Scale: 100.000 - V4
325
+ 2024-11-26,18:45:33 | INFO | Start epoch 1
326
+ 2024-11-26,18:45:36 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 1.1113 (1.111) Data (t): 2.678 Batch (t): 3.614, 141.674/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:47:08 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.1790 (1.145) Data (t): 0.001 Batch (t): 0.918, 565.967/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:48:39 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.2082 (1.166) Data (t): 0.001 Batch (t): 0.906, 567.243/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:50:10 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.2178 (1.179) Data (t): 0.001 Batch (t): 0.913, 564.863/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:51:44 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 1.0870 (1.161) Data (t): 0.001 Batch (t): 0.935, 566.280/s LR: 0.000000 Logit Scale: 100.000 - V4
331
+ 2024-11-26,18:53:14 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 1.2527 (1.176) Data (t): 0.001 Batch (t): 0.903, 564.633/s LR: 0.000000 Logit Scale: 100.000 - V4
332
+ 2024-11-26,18:54:47 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.1543 (1.173) Data (t): 0.001 Batch (t): 0.935, 566.305/s LR: 0.000000 Logit Scale: 100.000 - V4
333
+ 2024-11-26,18:56:18 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.2308 (1.180) Data (t): 0.001 Batch (t): 0.903, 568.845/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,18:57:49 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.2369 (1.186) Data (t): 0.001 Batch (t): 0.911, 567.065/s LR: 0.000000 Logit Scale: 100.000 - V4
335
+ 2024-11-26,18:59:22 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 1.0303 (1.171) Data (t): 0.001 Batch (t): 0.930, 567.518/s LR: 0.000000 Logit Scale: 100.000 - V4
336
+ 2024-11-26,19:00:52 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.2102 (1.174) Data (t): 0.001 Batch (t): 0.903, 564.848/s LR: 0.000000 Logit Scale: 100.000 - V4
337
+ 2024-11-26,19:02:25 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.2996 (1.185) Data (t): 0.001 Batch (t): 0.934, 566.655/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,19:03:56 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.1657 (1.183) Data (t): 0.001 Batch (t): 0.905, 568.032/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-26,19:05:27 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 1.1367 (1.180) Data (t): 0.001 Batch (t): 0.912, 567.773/s LR: 0.000000 Logit Scale: 100.000 - V4
340
+ 2024-11-26,19:06:59 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.2879 (1.187) Data (t): 0.001 Batch (t): 0.918, 567.224/s LR: 0.000000 Logit Scale: 100.000 - V4
341
+ 2024-11-26,19:08:31 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 1.2183 (1.189) Data (t): 0.001 Batch (t): 0.918, 567.219/s LR: 0.000000 Logit Scale: 100.000 - V4
342
+ 2024-11-26,19:10:03 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.2487 (1.193) Data (t): 0.001 Batch (t): 0.923, 564.209/s LR: 0.000000 Logit Scale: 100.000 - V4
343
+ 2024-11-26,19:11:34 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 1.0493 (1.185) Data (t): 0.001 Batch (t): 0.913, 566.582/s LR: 0.000000 Logit Scale: 100.000 - V4
344
+ 2024-11-26,19:13:05 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.1146 (1.181) Data (t): 0.001 Batch (t): 0.904, 566.208/s LR: 0.000000 Logit Scale: 100.000 - V4
345
+ 2024-11-26,19:14:37 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.2195 (1.183) Data (t): 0.001 Batch (t): 0.918, 563.963/s LR: 0.000000 Logit Scale: 100.000 - V4
346
+ 2024-11-26,19:16:09 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 1.1162 (1.180) Data (t): 0.001 Batch (t): 0.925, 566.122/s LR: 0.000000 Logit Scale: 100.000 - V4
347
+ 2024-11-26,19:17:41 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.1723 (1.179) Data (t): 0.001 Batch (t): 0.923, 566.487/s LR: 0.000000 Logit Scale: 100.000 - V4
348
+ 2024-11-26,19:19:13 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 1.0903 (1.176) Data (t): 0.001 Batch (t): 0.913, 569.544/s LR: 0.000000 Logit Scale: 100.000 - V4
349
+ 2024-11-26,19:20:43 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.1557 (1.175) Data (t): 0.001 Batch (t): 0.904, 564.434/s LR: 0.000000 Logit Scale: 100.000 - V4
350
+ 2024-11-26,19:22:14 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.2936 (1.179) Data (t): 0.001 Batch (t): 0.910, 568.474/s LR: 0.000000 Logit Scale: 100.000 - V4
351
+ 2024-11-26,19:23:47 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 1.0049 (1.173) Data (t): 0.001 Batch (t): 0.930, 566.808/s LR: 0.000000 Logit Scale: 100.000 - V4
352
+ 2024-11-26,19:25:18 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.1353 (1.171) Data (t): 0.001 Batch (t): 0.913, 566.849/s LR: 0.000000 Logit Scale: 100.000 - V4
353
+ 2024-11-26,19:26:51 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.1447 (1.170) Data (t): 0.001 Batch (t): 0.922, 564.822/s LR: 0.000000 Logit Scale: 100.000 - V4
354
+ 2024-11-26,19:28:21 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 1.0842 (1.167) Data (t): 0.001 Batch (t): 0.904, 564.034/s LR: 0.000000 Logit Scale: 100.000 - V4
355
+ 2024-11-26,19:29:52 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 1.1166 (1.166) Data (t): 0.001 Batch (t): 0.910, 568.063/s LR: 0.000000 Logit Scale: 100.000 - V4
356
+ 2024-11-26,19:31:25 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 1.0902 (1.163) Data (t): 0.001 Batch (t): 0.930, 565.891/s LR: 0.000000 Logit Scale: 100.000 - V4
357
+ 2024-11-26,19:32:55 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.1390 (1.163) Data (t): 0.001 Batch (t): 0.903, 565.759/s LR: 0.000000 Logit Scale: 100.000 - V4
358
+ 2024-11-26,19:34:28 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 1.0111 (1.158) Data (t): 0.001 Batch (t): 0.931, 567.499/s LR: 0.000000 Logit Scale: 100.000 - V4
359
+ 2024-11-26,19:35:59 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 1.1096 (1.157) Data (t): 0.001 Batch (t): 0.904, 566.242/s LR: 0.000000 Logit Scale: 100.000 - V4
360
+ 2024-11-26,19:37:30 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 1.0811 (1.154) Data (t): 0.001 Batch (t): 0.911, 563.914/s LR: 0.000000 Logit Scale: 100.000 - V4
361
+ 2024-11-26,19:39:03 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.2335 (1.157) Data (t): 0.001 Batch (t): 0.931, 567.587/s LR: 0.000000 Logit Scale: 100.000 - V4
362
+ 2024-11-26,19:40:33 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 0.96379 (1.151) Data (t): 0.001 Batch (t): 0.904, 568.363/s LR: 0.000000 Logit Scale: 100.000 - V4
363
+ 2024-11-26,19:42:07 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 1.2238 (1.153) Data (t): 0.001 Batch (t): 0.934, 565.869/s LR: 0.000000 Logit Scale: 100.000 - V4
364
+ 2024-11-26,19:43:37 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.1333 (1.153) Data (t): 0.001 Batch (t): 0.905, 563.048/s LR: 0.000000 Logit Scale: 100.000 - V4
365
+ 2024-11-26,19:45:08 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.1600 (1.153) Data (t): 0.001 Batch (t): 0.904, 565.614/s LR: 0.000000 Logit Scale: 100.000 - V4
366
+ 2024-11-26,19:46:40 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 1.0534 (1.151) Data (t): 0.001 Batch (t): 0.924, 568.257/s LR: 0.000000 Logit Scale: 100.000 - V4
367
+ 2024-11-26,19:48:12 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 1.1201 (1.150) Data (t): 0.001 Batch (t): 0.917, 566.229/s LR: 0.000000 Logit Scale: 100.000 - V4
368
+ 2024-11-26,19:49:44 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 1.1335 (1.149) Data (t): 0.001 Batch (t): 0.924, 565.974/s LR: 0.000000 Logit Scale: 100.000 - V4
369
+ 2024-11-26,19:51:16 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 1.1657 (1.150) Data (t): 0.001 Batch (t): 0.915, 561.098/s LR: 0.000000 Logit Scale: 100.000 - V4
370
+ 2024-11-26,19:52:46 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 1.1044 (1.149) Data (t): 0.001 Batch (t): 0.906, 565.411/s LR: 0.000000 Logit Scale: 100.000 - V4
371
+ 2024-11-26,19:54:18 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.2146 (1.150) Data (t): 0.001 Batch (t): 0.918, 568.226/s LR: 0.000000 Logit Scale: 100.000 - V4
372
+ 2024-11-26,19:55:51 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.2726 (1.153) Data (t): 0.001 Batch (t): 0.925, 566.263/s LR: 0.000000 Logit Scale: 100.000 - V4
373
+ 2024-11-26,19:57:23 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.1442 (1.153) Data (t): 0.001 Batch (t): 0.923, 564.662/s LR: 0.000000 Logit Scale: 100.000 - V4
374
+ 2024-11-26,19:58:54 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.2504 (1.155) Data (t): 0.001 Batch (t): 0.914, 565.119/s LR: 0.000000 Logit Scale: 100.000 - V4
375
+ 2024-11-26,20:00:25 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 1.1312 (1.154) Data (t): 0.001 Batch (t): 0.904, 565.673/s LR: 0.000000 Logit Scale: 100.000 - V4
376
+ 2024-11-26,20:01:56 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.1920 (1.155) Data (t): 0.001 Batch (t): 0.911, 567.681/s LR: 0.000000 Logit Scale: 100.000 - V4
377
+ 2024-11-26,20:03:29 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.2368 (1.156) Data (t): 0.001 Batch (t): 0.931, 567.142/s LR: 0.000000 Logit Scale: 100.000 - V4
378
+ 2024-11-26,20:05:00 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.1776 (1.157) Data (t): 0.001 Batch (t): 0.914, 564.779/s LR: 0.000000 Logit Scale: 100.000 - V4
379
+ 2024-11-26,20:06:33 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 1.1162 (1.156) Data (t): 0.001 Batch (t): 0.924, 565.048/s LR: 0.000000 Logit Scale: 100.000 - V4
380
+ 2024-11-26,20:08:03 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.1965 (1.157) Data (t): 0.001 Batch (t): 0.905, 565.182/s LR: 0.000000 Logit Scale: 100.000 - V4
381
+ 2024-11-26,20:09:34 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 1.0867 (1.156) Data (t): 0.001 Batch (t): 0.912, 569.781/s LR: 0.000000 Logit Scale: 100.000 - V4
382
+ 2024-11-26,20:11:07 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.2475 (1.157) Data (t): 0.001 Batch (t): 0.931, 568.291/s LR: 0.000000 Logit Scale: 100.000 - V4
383
+ 2024-11-26,20:12:38 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.1480 (1.157) Data (t): 0.001 Batch (t): 0.904, 568.279/s LR: 0.000000 Logit Scale: 100.000 - V4
384
+ 2024-11-26,20:14:11 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.1874 (1.158) Data (t): 0.001 Batch (t): 0.935, 567.359/s LR: 0.000000 Logit Scale: 100.000 - V4
385
+ 2024-11-26,20:15:42 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.2032 (1.158) Data (t): 0.001 Batch (t): 0.904, 567.935/s LR: 0.000000 Logit Scale: 100.000 - V4
386
+ 2024-11-26,20:17:12 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 1.1499 (1.158) Data (t): 0.001 Batch (t): 0.904, 567.167/s LR: 0.000000 Logit Scale: 100.000 - V4
387
+ 2024-11-26,20:18:45 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.2424 (1.160) Data (t): 0.001 Batch (t): 0.924, 564.097/s LR: 0.000000 Logit Scale: 100.000 - V4
388
+ 2024-11-26,20:20:16 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 1.0890 (1.158) Data (t): 0.001 Batch (t): 0.917, 567.259/s LR: 0.000000 Logit Scale: 100.000 - V4
389
+ 2024-11-26,20:21:50 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.2888 (1.160) Data (t): 0.001 Batch (t): 0.934, 565.948/s LR: 0.000000 Logit Scale: 100.000 - V4
390
+ 2024-11-26,20:23:20 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.1547 (1.160) Data (t): 0.001 Batch (t): 0.905, 565.787/s LR: 0.000000 Logit Scale: 100.000 - V4
391
+ 2024-11-26,20:24:51 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.3574 (1.163) Data (t): 0.001 Batch (t): 0.904, 565.443/s LR: 0.000000 Logit Scale: 100.000 - V4
392
+ 2024-11-26,20:26:23 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 1.1291 (1.163) Data (t): 0.001 Batch (t): 0.924, 566.729/s LR: 0.000000 Logit Scale: 100.000 - V4
393
+ 2024-11-26,20:27:55 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.2364 (1.164) Data (t): 0.001 Batch (t): 0.918, 566.960/s LR: 0.000000 Logit Scale: 100.000 - V4
394
+ 2024-11-26,20:29:27 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 1.0870 (1.163) Data (t): 0.001 Batch (t): 0.925, 564.544/s LR: 0.000000 Logit Scale: 100.000 - V4
395
+ 2024-11-26,20:30:59 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.1682 (1.163) Data (t): 0.001 Batch (t): 0.915, 567.330/s LR: 0.000000 Logit Scale: 100.000 - V4
396
+ 2024-11-26,20:32:29 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 1.0792 (1.162) Data (t): 0.001 Batch (t): 0.905, 564.027/s LR: 0.000000 Logit Scale: 100.000 - V4
397
+ 2024-11-26,20:34:01 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.1628 (1.162) Data (t): 0.001 Batch (t): 0.919, 565.897/s LR: 0.000000 Logit Scale: 100.000 - V4
398
+ 2024-11-26,20:35:34 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 0.98935 (1.159) Data (t): 0.001 Batch (t): 0.924, 565.175/s LR: 0.000000 Logit Scale: 100.000 - V4
399
+ 2024-11-26,20:37:05 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 1.2257 (1.160) Data (t): 0.001 Batch (t): 0.913, 566.154/s LR: 0.000000 Logit Scale: 100.000 - V4
400
+ 2024-11-26,20:38:37 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.2173 (1.161) Data (t): 0.001 Batch (t): 0.924, 566.209/s LR: 0.000000 Logit Scale: 100.000 - V4
401
+ 2024-11-26,20:40:08 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.2186 (1.162) Data (t): 0.001 Batch (t): 0.904, 567.116/s LR: 0.000000 Logit Scale: 100.000 - V4
402
+ 2024-11-26,20:41:38 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 1.0532 (1.160) Data (t): 0.001 Batch (t): 0.905, 566.398/s LR: 0.000000 Logit Scale: 100.000 - V4
403
+ 2024-11-26,20:43:12 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 1.0454 (1.159) Data (t): 0.001 Batch (t): 0.939, 564.783/s LR: 0.000000 Logit Scale: 100.000 - V4
404
+ 2024-11-26,20:44:44 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 1.0210 (1.157) Data (t): 0.001 Batch (t): 0.914, 566.413/s LR: 0.000000 Logit Scale: 100.000 - V4
405
+ 2024-11-26,20:46:16 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 1.1267 (1.157) Data (t): 0.001 Batch (t): 0.922, 567.655/s LR: 0.000000 Logit Scale: 100.000 - V4
406
+ 2024-11-26,20:47:46 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.1625 (1.157) Data (t): 0.001 Batch (t): 0.904, 567.239/s LR: 0.000000 Logit Scale: 100.000 - V4
407
+ 2024-11-26,20:49:17 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 1.0676 (1.156) Data (t): 0.001 Batch (t): 0.905, 569.392/s LR: 0.000000 Logit Scale: 100.000 - V4
408
+ 2024-11-26,20:50:51 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.1740 (1.156) Data (t): 0.001 Batch (t): 0.937, 567.198/s LR: 0.000000 Logit Scale: 100.000 - V4
409
+ 2024-11-26,20:52:21 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.2480 (1.157) Data (t): 0.001 Batch (t): 0.904, 566.632/s LR: 0.000000 Logit Scale: 100.000 - V4
410
+ 2024-11-26,20:53:54 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 1.0699 (1.156) Data (t): 0.001 Batch (t): 0.933, 565.796/s LR: 0.000000 Logit Scale: 100.000 - V4
411
+ 2024-11-26,20:55:25 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 1.1192 (1.156) Data (t): 0.001 Batch (t): 0.905, 562.706/s LR: 0.000000 Logit Scale: 100.000 - V4
412
+ 2024-11-26,20:56:55 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 1.1440 (1.155) Data (t): 0.001 Batch (t): 0.904, 565.289/s LR: 0.000000 Logit Scale: 100.000 - V4
413
+ 2024-11-26,20:58:28 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.2507 (1.157) Data (t): 0.001 Batch (t): 0.925, 566.284/s LR: 0.000000 Logit Scale: 100.000 - V4
414
+ 2024-11-26,20:59:59 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 1.0559 (1.155) Data (t): 0.001 Batch (t): 0.917, 567.174/s LR: 0.000000 Logit Scale: 100.000 - V4
415
+ 2024-11-26,21:01:32 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 1.1118 (1.155) Data (t): 0.001 Batch (t): 0.924, 566.448/s LR: 0.000000 Logit Scale: 100.000 - V4
416
+ 2024-11-26,21:03:03 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.1348 (1.155) Data (t): 0.001 Batch (t): 0.914, 565.992/s LR: 0.000000 Logit Scale: 100.000 - V4
417
+ 2024-11-26,21:04:34 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.1899 (1.155) Data (t): 0.001 Batch (t): 0.905, 563.363/s LR: 0.000000 Logit Scale: 100.000 - V4
418
+ 2024-11-26,21:06:06 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.2856 (1.157) Data (t): 0.001 Batch (t): 0.925, 567.289/s LR: 0.000000 Logit Scale: 100.000 - V4
419
+ 2024-11-26,21:07:38 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 1.1253 (1.156) Data (t): 0.001 Batch (t): 0.918, 564.719/s LR: 0.000000 Logit Scale: 100.000 - V4
420
+ 2024-11-26,21:09:09 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 1.0894 (1.155) Data (t): 0.001 Batch (t): 0.915, 565.447/s LR: 0.000000 Logit Scale: 100.000 - V4
421
+ 2024-11-26,21:10:42 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.1255 (1.155) Data (t): 0.001 Batch (t): 0.925, 566.633/s LR: 0.000000 Logit Scale: 100.000 - V4
422
+ 2024-11-26,21:12:12 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 1.0128 (1.154) Data (t): 0.001 Batch (t): 0.905, 566.988/s LR: 0.000000 Logit Scale: 100.000 - V4
423
+ 2024-11-26,21:13:43 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.2716 (1.155) Data (t): 0.001 Batch (t): 0.905, 568.637/s LR: 0.000000 Logit Scale: 100.000 - V4
424
+ 2024-11-26,21:15:17 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 1.1035 (1.154) Data (t): 0.001 Batch (t): 0.939, 566.968/s LR: 0.000000 Logit Scale: 100.000 - V4
425
+ 2024-11-26,21:16:48 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 1.0628 (1.153) Data (t): 0.001 Batch (t): 0.915, 566.736/s LR: 0.000000 Logit Scale: 100.000 - V4
426
+ 2024-11-26,21:18:21 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 1.1938 (1.154) Data (t): 0.001 Batch (t): 0.925, 564.839/s LR: 0.000000 Logit Scale: 100.000 - V4
427
+ 2024-11-26,21:19:51 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.1683 (1.154) Data (t): 0.001 Batch (t): 0.905, 566.970/s LR: 0.000000 Logit Scale: 100.000 - V4
428
+ 2024-11-26,21:21:22 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 1.1101 (1.154) Data (t): 0.001 Batch (t): 0.904, 564.382/s LR: 0.000000 Logit Scale: 100.000 - V4
429
+ 2024-11-26,21:22:56 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.2139 (1.154) Data (t): 0.001 Batch (t): 0.939, 565.488/s LR: 0.000000 Logit Scale: 100.000 - V4
430
+ 2024-11-26,21:24:26 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 1.0669 (1.153) Data (t): 0.001 Batch (t): 0.905, 567.869/s LR: 0.000000 Logit Scale: 100.000 - V4
431
+ 2024-11-26,21:26:00 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.1615 (1.153) Data (t): 0.001 Batch (t): 0.935, 568.790/s LR: 0.000000 Logit Scale: 100.000 - V4
432
+ 2024-11-26,21:27:30 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 1.1216 (1.153) Data (t): 0.001 Batch (t): 0.905, 569.802/s LR: 0.000000 Logit Scale: 100.000 - V4
433
+ 2024-11-26,21:29:01 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.2386 (1.154) Data (t): 0.001 Batch (t): 0.905, 565.213/s LR: 0.000000 Logit Scale: 100.000 - V4
434
+ 2024-11-26,21:30:34 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 1.0780 (1.153) Data (t): 0.001 Batch (t): 0.939, 566.992/s LR: 0.000000 Logit Scale: 100.000 - V4
435
+ 2024-11-26,21:32:05 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.1594 (1.153) Data (t): 0.001 Batch (t): 0.904, 568.733/s LR: 0.000000 Logit Scale: 100.000 - V4
436
+ 2024-11-26,21:33:38 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 1.0383 (1.152) Data (t): 0.001 Batch (t): 0.934, 566.493/s LR: 0.000000 Logit Scale: 100.000 - V4
437
+ 2024-11-26,21:35:09 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.2032 (1.153) Data (t): 0.001 Batch (t): 0.905, 564.756/s LR: 0.000000 Logit Scale: 100.000 - V4
438
+ 2024-11-26,21:36:39 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 1.1998 (1.153) Data (t): 0.001 Batch (t): 0.905, 562.056/s LR: 0.000000 Logit Scale: 100.000 - V4
439
+ 2024-11-26,21:38:12 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 1.0639 (1.152) Data (t): 0.001 Batch (t): 0.926, 566.306/s LR: 0.000000 Logit Scale: 100.000 - V4
440
+ 2024-11-26,21:39:44 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.2116 (1.153) Data (t): 0.001 Batch (t): 0.919, 566.372/s LR: 0.000000 Logit Scale: 100.000 - V4
441
+ 2024-11-26,21:41:15 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 1.0662 (1.152) Data (t): 0.001 Batch (t): 0.915, 565.906/s LR: 0.000000 Logit Scale: 100.000 - V4
442
+ 2024-11-26,21:42:48 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.2228 (1.153) Data (t): 0.001 Batch (t): 0.925, 565.118/s LR: 0.000000 Logit Scale: 100.000 - V4
443
+ 2024-11-26,21:44:18 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 1.0105 (1.151) Data (t): 0.001 Batch (t): 0.905, 566.870/s LR: 0.000000 Logit Scale: 100.000 - V4
444
+ 2024-11-26,21:45:50 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 1.1212 (1.151) Data (t): 0.001 Batch (t): 0.919, 567.042/s LR: 0.000000 Logit Scale: 100.000 - V4
445
+ 2024-11-26,21:47:22 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.2714 (1.152) Data (t): 0.001 Batch (t): 0.919, 563.793/s LR: 0.000000 Logit Scale: 100.000 - V4
446
+ 2024-11-26,21:48:53 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 1.0927 (1.152) Data (t): 0.001 Batch (t): 0.915, 565.260/s LR: 0.000000 Logit Scale: 100.000 - V4
447
+ 2024-11-26,21:50:26 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.2607 (1.153) Data (t): 0.001 Batch (t): 0.924, 564.631/s LR: 0.000000 Logit Scale: 100.000 - V4
448
+ 2024-11-26,21:51:56 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 1.1110 (1.152) Data (t): 0.001 Batch (t): 0.905, 564.706/s LR: 0.000000 Logit Scale: 100.000 - V4
449
+ 2024-11-26,21:53:28 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 1.0302 (1.151) Data (t): 0.001 Batch (t): 0.913, 565.761/s LR: 0.000000 Logit Scale: 100.000 - V4
450
+ 2024-11-26,21:55:00 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 1.0109 (1.150) Data (t): 0.001 Batch (t): 0.927, 566.584/s LR: 0.000000 Logit Scale: 100.000 - V4
451
+ 2024-11-26,21:56:32 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 1.1750 (1.150) Data (t): 0.001 Batch (t): 0.916, 564.912/s LR: 0.000000 Logit Scale: 100.000 - V4
452
+ 2024-11-26,21:58:05 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 1.2053 (1.151) Data (t): 0.001 Batch (t): 0.926, 562.800/s LR: 0.000000 Logit Scale: 100.000 - V4
453
+ 2024-11-26,21:59:35 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.2756 (1.152) Data (t): 0.001 Batch (t): 0.906, 565.667/s LR: 0.000000 Logit Scale: 100.000 - V4
454
+ 2024-11-26,22:01:06 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 1.2139 (1.152) Data (t): 0.001 Batch (t): 0.906, 566.985/s LR: 0.000000 Logit Scale: 100.000 - V4
455
+ 2024-11-26,22:02:39 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.1667 (1.152) Data (t): 0.001 Batch (t): 0.935, 564.301/s LR: 0.000000 Logit Scale: 100.000 - V4
456
+ 2024-11-26,22:04:10 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 0.99248 (1.151) Data (t): 0.001 Batch (t): 0.906, 566.735/s LR: 0.000000 Logit Scale: 100.000 - V4
457
+ 2024-11-26,22:05:43 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 1.0848 (1.151) Data (t): 0.001 Batch (t): 0.936, 567.274/s LR: 0.000000 Logit Scale: 100.000 - V4
458
+ 2024-11-26,22:07:14 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.2516 (1.151) Data (t): 0.001 Batch (t): 0.906, 565.804/s LR: 0.000000 Logit Scale: 100.000 - V4
459
+ 2024-11-26,22:08:45 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 1.1139 (1.151) Data (t): 0.001 Batch (t): 0.905, 564.323/s LR: 0.000000 Logit Scale: 100.000 - V4
460
+ 2024-11-26,22:10:17 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 0.98787 (1.150) Data (t): 0.001 Batch (t): 0.927, 563.882/s LR: 0.000000 Logit Scale: 100.000 - V4
461
+ 2024-11-26,22:11:49 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.1831 (1.150) Data (t): 0.001 Batch (t): 0.920, 565.372/s LR: 0.000000 Logit Scale: 100.000 - V4
462
+ 2024-11-26,22:13:22 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.2515 (1.151) Data (t): 0.001 Batch (t): 0.925, 565.697/s LR: 0.000000 Logit Scale: 100.000 - V4
463
+ 2024-11-26,22:14:53 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 1.1531 (1.151) Data (t): 0.001 Batch (t): 0.915, 562.846/s LR: 0.000000 Logit Scale: 100.000 - V4
464
+ 2024-11-26,22:16:24 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.1604 (1.151) Data (t): 0.001 Batch (t): 0.904, 565.719/s LR: 0.000000 Logit Scale: 100.000 - V4
465
+ 2024-11-26,22:17:56 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 1.1141 (1.151) Data (t): 0.001 Batch (t): 0.919, 563.724/s LR: 0.000000 Logit Scale: 100.000 - V4
466
+ 2024-11-26,22:19:27 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 1.0183 (1.150) Data (t): 0.001 Batch (t): 0.918, 567.763/s LR: 0.000000 Logit Scale: 100.000 - V4
467
+ 2024-11-26,22:20:59 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 1.2316 (1.150) Data (t): 0.001 Batch (t): 0.914, 565.280/s LR: 0.000000 Logit Scale: 100.000 - V4
468
+ 2024-11-26,22:22:31 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 1.1976 (1.151) Data (t): 0.001 Batch (t): 0.925, 565.785/s LR: 0.000000 Logit Scale: 100.000 - V4
469
+ 2024-11-26,22:24:02 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.1537 (1.151) Data (t): 0.001 Batch (t): 0.905, 567.193/s LR: 0.000000 Logit Scale: 100.000 - V4
470
+ 2024-11-26,22:25:34 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.1750 (1.151) Data (t): 0.001 Batch (t): 0.920, 565.102/s LR: 0.000000 Logit Scale: 100.000 - V4
471
+ 2024-11-26,22:27:06 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 1.1223 (1.151) Data (t): 0.001 Batch (t): 0.920, 567.425/s LR: 0.000000 Logit Scale: 100.000 - V4
472
+ 2024-11-26,22:28:37 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.2066 (1.151) Data (t): 0.001 Batch (t): 0.915, 565.040/s LR: 0.000000 Logit Scale: 100.000 - V4
473
+ 2024-11-26,22:30:10 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 1.0742 (1.151) Data (t): 0.001 Batch (t): 0.925, 564.063/s LR: 0.000000 Logit Scale: 100.000 - V4
474
+ 2024-11-26,22:31:40 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 1.2356 (1.151) Data (t): 0.001 Batch (t): 0.905, 565.331/s LR: 0.000000 Logit Scale: 100.000 - V4
475
+ 2024-11-26,22:33:11 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 1.0804 (1.151) Data (t): 0.001 Batch (t): 0.912, 564.789/s LR: 0.000000 Logit Scale: 100.000 - V4
476
+ 2024-11-26,22:34:44 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 1.2377 (1.151) Data (t): 0.001 Batch (t): 0.926, 565.187/s LR: 0.000000 Logit Scale: 100.000 - V4
477
+ 2024-11-26,22:36:16 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.2121 (1.152) Data (t): 0.001 Batch (t): 0.915, 564.164/s LR: 0.000000 Logit Scale: 100.000 - V4
478
+ 2024-11-26,22:37:48 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 1.0722 (1.151) Data (t): 0.001 Batch (t): 0.926, 565.085/s LR: 0.000000 Logit Scale: 100.000 - V4
479
+ 2024-11-26,22:39:19 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.1729 (1.151) Data (t): 0.001 Batch (t): 0.906, 567.682/s LR: 0.000000 Logit Scale: 100.000 - V4
480
+ 2024-11-26,22:40:49 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.1288 (1.151) Data (t): 0.001 Batch (t): 0.905, 564.341/s LR: 0.000000 Logit Scale: 100.000 - V4
481
+ 2024-11-26,22:42:23 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 1.1042 (1.151) Data (t): 0.001 Batch (t): 0.941, 567.728/s LR: 0.000000 Logit Scale: 100.000 - V4
482
+ 2024-11-26,22:43:54 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 1.0765 (1.150) Data (t): 0.001 Batch (t): 0.905, 565.947/s LR: 0.000000 Logit Scale: 100.000 - V4
483
+ 2024-11-26,22:45:26 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 0.98328 (1.149) Data (t): 0.001 Batch (t): 0.925, 565.552/s LR: 0.000000 Logit Scale: 100.000 - V4
484
+ 2024-11-26,22:46:58 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 0.99957 (1.148) Data (t): 0.001 Batch (t): 0.915, 565.838/s LR: 0.000000 Logit Scale: 100.000 - V4
485
+ 2024-11-26,22:48:28 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.2803 (1.149) Data (t): 0.001 Batch (t): 0.905, 566.837/s LR: 0.000000 Logit Scale: 100.000 - V4
486
+ 2024-11-26,22:50:00 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 1.0993 (1.149) Data (t): 0.001 Batch (t): 0.919, 567.254/s LR: 0.000000 Logit Scale: 100.000 - V4
487
+ 2024-11-26,22:51:33 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.2008 (1.149) Data (t): 0.001 Batch (t): 0.926, 566.216/s LR: 0.000000 Logit Scale: 100.000 - V4
488
+ 2024-11-26,22:53:04 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.1265 (1.149) Data (t): 0.001 Batch (t): 0.915, 565.011/s LR: 0.000000 Logit Scale: 100.000 - V4
489
+ 2024-11-26,22:54:37 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 1.0999 (1.149) Data (t): 0.001 Batch (t): 0.925, 564.941/s LR: 0.000000 Logit Scale: 100.000 - V4
490
+ 2024-11-26,22:56:07 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 1.1695 (1.149) Data (t): 0.001 Batch (t): 0.905, 567.078/s LR: 0.000000 Logit Scale: 100.000 - V4
491
+ 2024-11-26,22:57:39 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 1.1207 (1.149) Data (t): 0.001 Batch (t): 0.920, 565.229/s LR: 0.000000 Logit Scale: 100.000 - V4
492
+ 2024-11-26,22:59:12 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.2345 (1.149) Data (t): 0.001 Batch (t): 0.927, 564.107/s LR: 0.000000 Logit Scale: 100.000 - V4
493
+ 2024-11-26,23:00:44 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.1573 (1.149) Data (t): 0.001 Batch (t): 0.915, 568.099/s LR: 0.000000 Logit Scale: 100.000 - V4
494
+ 2024-11-26,23:02:16 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 0.89660 (1.148) Data (t): 0.001 Batch (t): 0.926, 563.038/s LR: 0.000000 Logit Scale: 100.000 - V4
495
+ 2024-11-26,23:03:47 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 1.0196 (1.147) Data (t): 0.001 Batch (t): 0.906, 566.984/s LR: 0.000000 Logit Scale: 100.000 - V4
496
+ 2024-11-26,23:05:19 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.1959 (1.147) Data (t): 0.001 Batch (t): 0.919, 564.807/s LR: 0.000000 Logit Scale: 100.000 - V4
497
+ 2024-11-26,23:06:51 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.2581 (1.148) Data (t): 0.001 Batch (t): 0.926, 566.843/s LR: 0.000000 Logit Scale: 100.000 - V4
498
+ 2024-11-26,23:08:23 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.2642 (1.149) Data (t): 0.001 Batch (t): 0.915, 564.401/s LR: 0.000000 Logit Scale: 100.000 - V4
499
+ 2024-11-26,23:09:55 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 1.0202 (1.148) Data (t): 0.001 Batch (t): 0.925, 565.253/s LR: 0.000000 Logit Scale: 100.000 - V4
500
+ 2024-11-26,23:11:26 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.1631 (1.148) Data (t): 0.001 Batch (t): 0.906, 567.493/s LR: 0.000000 Logit Scale: 100.000 - V4
501
+ 2024-11-26,23:12:57 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.0979 (1.148) Data (t): 0.001 Batch (t): 0.913, 310.349/s LR: 0.000000 Logit Scale: 100.000 - V4
502
+ 2024-11-26,23:14:31 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.2026 (1.148) Data (t): 0.001 Batch (t): 0.935, 564.573/s LR: 0.000000 Logit Scale: 100.000 - V4
503
+ 2024-11-26,23:16:02 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 1.1609 (1.148) Data (t): 0.001 Batch (t): 0.915, 264.616/s LR: 0.000000 Logit Scale: 100.000 - V4
504
+ 2024-11-26,23:17:35 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.2517 (1.149) Data (t): 0.001 Batch (t): 0.925, 564.945/s LR: 0.000000 Logit Scale: 100.000 - V4
505
+ 2024-11-26,23:19:05 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 1.1683 (1.149) Data (t): 0.001 Batch (t): 0.905, 565.435/s LR: 0.000000 Logit Scale: 100.000 - V4
506
+ 2024-11-26,23:20:36 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 1.1305 (1.149) Data (t): 0.001 Batch (t): 0.905, 565.797/s LR: 0.000000 Logit Scale: 100.000 - V4
507
+ 2024-11-26,23:22:09 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 0.97606 (1.148) Data (t): 0.001 Batch (t): 0.936, 567.587/s LR: 0.000000 Logit Scale: 100.000 - V4
508
+ 2024-11-26,23:23:40 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 1.0670 (1.147) Data (t): 0.001 Batch (t): 0.913, 567.888/s LR: 0.000000 Logit Scale: 100.000 - V4
509
+ 2024-11-26,23:25:13 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 1.1213 (1.147) Data (t): 0.001 Batch (t): 0.927, 565.866/s LR: 0.000000 Logit Scale: 100.000 - V4
510
+ 2024-11-26,23:26:45 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 1.0958 (1.147) Data (t): 0.001 Batch (t): 0.916, 561.937/s LR: 0.000000 Logit Scale: 100.000 - V4
511
+ 2024-11-26,23:28:15 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 1.0313 (1.146) Data (t): 0.001 Batch (t): 0.905, 566.182/s LR: 0.000000 Logit Scale: 100.000 - V4
512
+ 2024-11-26,23:29:47 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 1.0909 (1.146) Data (t): 0.001 Batch (t): 0.920, 566.038/s LR: 0.000000 Logit Scale: 100.000 - V4
513
+ 2024-11-26,23:31:20 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.2037 (1.146) Data (t): 0.001 Batch (t): 0.928, 562.930/s LR: 0.000000 Logit Scale: 100.000 - V4
514
+ 2024-11-26,23:32:52 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 1.0937 (1.146) Data (t): 0.001 Batch (t): 0.916, 564.861/s LR: 0.000000 Logit Scale: 100.000 - V4
515
+ 2024-11-26,23:34:24 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 1.0984 (1.146) Data (t): 0.001 Batch (t): 0.926, 566.187/s LR: 0.000000 Logit Scale: 100.000 - V4
516
+ 2024-11-26,23:35:55 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.1745 (1.146) Data (t): 0.001 Batch (t): 0.905, 565.219/s LR: 0.000000 Logit Scale: 100.000 - V4
517
+ 2024-11-26,23:37:27 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 1.0503 (1.145) Data (t): 0.001 Batch (t): 0.920, 564.235/s LR: 0.000000 Logit Scale: 100.000 - V4
518
+ 2024-11-26,23:38:59 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.1714 (1.145) Data (t): 0.001 Batch (t): 0.928, 567.173/s LR: 0.000000 Logit Scale: 100.000 - V4
519
+ 2024-11-26,23:40:31 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 1.1987 (1.146) Data (t): 0.001 Batch (t): 0.915, 567.815/s LR: 0.000000 Logit Scale: 100.000 - V4
520
+ 2024-11-26,23:42:04 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 1.1630 (1.146) Data (t): 0.001 Batch (t): 0.926, 565.133/s LR: 0.000000 Logit Scale: 100.000 - V4
521
+ 2024-11-26,23:43:34 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.1249 (1.146) Data (t): 0.001 Batch (t): 0.905, 565.255/s LR: 0.000000 Logit Scale: 100.000 - V4
522
+ 2024-11-26,23:45:06 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 1.0842 (1.145) Data (t): 0.001 Batch (t): 0.920, 564.436/s LR: 0.000000 Logit Scale: 100.000 - V4
523
+ 2024-11-26,23:46:39 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 1.1441 (1.145) Data (t): 0.001 Batch (t): 0.928, 566.782/s LR: 0.000000 Logit Scale: 100.000 - V4
524
+ 2024-11-26,23:48:10 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 1.1079 (1.145) Data (t): 0.001 Batch (t): 0.916, 566.827/s LR: 0.000000 Logit Scale: 100.000 - V4
525
+ 2024-11-26,23:49:43 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.2371 (1.146) Data (t): 0.001 Batch (t): 0.925, 566.392/s LR: 0.000000 Logit Scale: 100.000 - V4
526
+ 2024-11-26,23:51:13 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 1.0206 (1.145) Data (t): 0.001 Batch (t): 0.905, 564.624/s LR: 0.000000 Logit Scale: 100.000 - V4
527
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+ 2024-11-27,00:03:54 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8.
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+ 2024-11-27,00:03:54 | INFO | Loading ViT-L-14-336 model config.
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+ 2024-11-27,00:04:03 | INFO | Params:
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+ 2024-11-27,00:04:03 | INFO | batch_size: 64
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+ 2024-11-27,00:04:03 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints
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+ 2024-11-27,00:04:03 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten
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+ 2024-11-27,00:04:03 | INFO | lr: 5e-06
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124
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+ 2024-11-27,00:22:06 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.3850 (1.888) Data (t): 0.001 Batch (t): 0.908, 562.223/s LR: 0.000005 Logit Scale: 99.975 - V4
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+ 2024-11-27,00:38:56 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.2620 (1.630) Data (t): 0.001 Batch (t): 0.906, 566.337/s LR: 0.000005 Logit Scale: 99.948 - V4
139
+ 2024-11-27,00:40:27 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.3336 (1.618) Data (t): 0.001 Batch (t): 0.908, 564.522/s LR: 0.000005 Logit Scale: 99.947 - V4
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+ 2024-11-27,00:42:03 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.3807 (1.608) Data (t): 0.001 Batch (t): 0.963, 565.424/s LR: 0.000005 Logit Scale: 99.944 - V4
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+ 2024-11-27,00:43:35 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.2279 (1.594) Data (t): 0.001 Batch (t): 0.917, 563.331/s LR: 0.000005 Logit Scale: 99.944 - V4
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+ 2024-11-27,00:45:06 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.2738 (1.582) Data (t): 0.001 Batch (t): 0.907, 561.282/s LR: 0.000005 Logit Scale: 99.942 - V4
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+ 2024-11-27,00:46:37 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.3069 (1.572) Data (t): 0.001 Batch (t): 0.908, 566.991/s LR: 0.000005 Logit Scale: 99.939 - V4
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+ 2024-11-27,00:48:07 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.3902 (1.566) Data (t): 0.001 Batch (t): 0.907, 565.220/s LR: 0.000005 Logit Scale: 99.936 - V4
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+ 2024-11-27,00:49:39 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.3253 (1.558) Data (t): 0.001 Batch (t): 0.917, 560.577/s LR: 0.000005 Logit Scale: 99.935 - V4
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+ 2024-11-27,00:51:14 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.4161 (1.553) Data (t): 0.001 Batch (t): 0.948, 565.131/s LR: 0.000005 Logit Scale: 99.931 - V4
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+ 2024-11-27,00:52:45 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.2922 (1.545) Data (t): 0.001 Batch (t): 0.908, 562.395/s LR: 0.000005 Logit Scale: 99.928 - V4
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+ 2024-11-27,00:54:15 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.2860 (1.537) Data (t): 0.001 Batch (t): 0.907, 564.746/s LR: 0.000005 Logit Scale: 99.928 - V4
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+ 2024-11-27,00:55:46 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.3566 (1.532) Data (t): 0.001 Batch (t): 0.907, 563.971/s LR: 0.000005 Logit Scale: 99.926 - V4
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+ 2024-11-27,00:57:17 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.2082 (1.522) Data (t): 0.001 Batch (t): 0.906, 563.194/s LR: 0.000005 Logit Scale: 99.925 - V4
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+ 2024-11-27,00:58:53 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.1899 (1.513) Data (t): 0.001 Batch (t): 0.965, 563.591/s LR: 0.000005 Logit Scale: 99.921 - V4
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+ 2024-11-27,01:00:24 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.3821 (1.510) Data (t): 0.001 Batch (t): 0.905, 565.801/s LR: 0.000005 Logit Scale: 99.921 - V4
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+ 2024-11-27,01:01:54 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.3560 (1.506) Data (t): 0.001 Batch (t): 0.907, 563.000/s LR: 0.000005 Logit Scale: 99.920 - V4
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+ 2024-11-27,01:03:25 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.3263 (1.501) Data (t): 0.001 Batch (t): 0.906, 563.843/s LR: 0.000005 Logit Scale: 99.919 - V4
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+ 2024-11-27,01:04:56 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.1140 (1.491) Data (t): 0.001 Batch (t): 0.906, 561.312/s LR: 0.000005 Logit Scale: 99.918 - V4
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+ 2024-11-27,01:06:32 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.2637 (1.486) Data (t): 0.001 Batch (t): 0.965, 565.332/s LR: 0.000005 Logit Scale: 99.916 - V4
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+ 2024-11-27,01:08:03 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.2427 (1.480) Data (t): 0.001 Batch (t): 0.907, 564.089/s LR: 0.000005 Logit Scale: 99.914 - V4
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+ 2024-11-27,01:09:33 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.0425 (1.470) Data (t): 0.001 Batch (t): 0.908, 563.341/s LR: 0.000005 Logit Scale: 99.911 - V4
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+ 2024-11-27,01:11:04 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.2554 (1.465) Data (t): 0.001 Batch (t): 0.909, 566.173/s LR: 0.000005 Logit Scale: 99.912 - V4
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+ 2024-11-27,01:12:35 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.1487 (1.458) Data (t): 0.001 Batch (t): 0.909, 562.763/s LR: 0.000005 Logit Scale: 99.910 - V4
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+ 2024-11-27,01:14:12 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.2780 (1.454) Data (t): 0.001 Batch (t): 0.967, 563.359/s LR: 0.000005 Logit Scale: 99.909 - V4
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+ 2024-11-27,01:15:43 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.1924 (1.448) Data (t): 0.001 Batch (t): 0.909, 564.695/s LR: 0.000005 Logit Scale: 99.907 - V4
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+ 2024-11-27,01:17:14 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.2804 (1.445) Data (t): 0.001 Batch (t): 0.908, 564.077/s LR: 0.000005 Logit Scale: 99.904 - V4
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+ 2024-11-27,01:18:44 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.2421 (1.441) Data (t): 0.001 Batch (t): 0.908, 562.105/s LR: 0.000005 Logit Scale: 99.905 - V4
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+ 2024-11-27,01:20:15 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.2321 (1.437) Data (t): 0.001 Batch (t): 0.907, 564.190/s LR: 0.000005 Logit Scale: 99.904 - V4
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+ 2024-11-27,01:21:50 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.3552 (1.435) Data (t): 0.001 Batch (t): 0.948, 564.421/s LR: 0.000005 Logit Scale: 99.902 - V4
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+ 2024-11-27,01:23:22 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.2933 (1.432) Data (t): 0.001 Batch (t): 0.925, 565.797/s LR: 0.000005 Logit Scale: 99.901 - V4
168
+ 2024-11-27,01:24:53 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.1608 (1.427) Data (t): 0.001 Batch (t): 0.907, 565.335/s LR: 0.000005 Logit Scale: 99.898 - V4
169
+ 2024-11-27,01:26:24 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.2296 (1.424) Data (t): 0.001 Batch (t): 0.907, 563.085/s LR: 0.000005 Logit Scale: 99.898 - V4
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+ 2024-11-27,01:27:54 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.2679 (1.421) Data (t): 0.001 Batch (t): 0.906, 564.493/s LR: 0.000005 Logit Scale: 99.895 - V4
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+ 2024-11-27,01:29:25 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.0745 (1.415) Data (t): 0.001 Batch (t): 0.907, 563.675/s LR: 0.000005 Logit Scale: 99.896 - V4
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+ 2024-11-27,01:31:02 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.2342 (1.411) Data (t): 0.001 Batch (t): 0.967, 562.898/s LR: 0.000005 Logit Scale: 99.893 - V4
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+ 2024-11-27,01:32:33 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.2334 (1.408) Data (t): 0.001 Batch (t): 0.907, 564.139/s LR: 0.000005 Logit Scale: 99.891 - V4
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+ 2024-11-27,01:34:03 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.2475 (1.406) Data (t): 0.001 Batch (t): 0.906, 566.407/s LR: 0.000005 Logit Scale: 99.889 - V4
175
+ 2024-11-27,01:35:34 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.1165 (1.401) Data (t): 0.001 Batch (t): 0.907, 564.933/s LR: 0.000005 Logit Scale: 99.890 - V4
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+ 2024-11-27,01:37:04 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.2125 (1.398) Data (t): 0.001 Batch (t): 0.906, 564.736/s LR: 0.000005 Logit Scale: 99.890 - V4
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+ 2024-11-27,01:38:42 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.1519 (1.394) Data (t): 0.001 Batch (t): 0.972, 562.768/s LR: 0.000005 Logit Scale: 99.889 - V4
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+ 2024-11-27,01:40:12 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.0469 (1.388) Data (t): 0.001 Batch (t): 0.906, 564.360/s LR: 0.000005 Logit Scale: 99.887 - V4
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+ 2024-11-27,01:41:43 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.2218 (1.386) Data (t): 0.001 Batch (t): 0.906, 564.814/s LR: 0.000005 Logit Scale: 99.883 - V4
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+ 2024-11-27,01:43:13 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.2339 (1.383) Data (t): 0.001 Batch (t): 0.906, 564.536/s LR: 0.000005 Logit Scale: 99.882 - V4
181
+ 2024-11-27,01:44:44 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.2602 (1.381) Data (t): 0.001 Batch (t): 0.906, 565.416/s LR: 0.000005 Logit Scale: 99.883 - V4
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+ 2024-11-27,01:46:20 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.2756 (1.380) Data (t): 0.001 Batch (t): 0.957, 569.027/s LR: 0.000005 Logit Scale: 99.883 - V4
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+ 2024-11-27,01:47:50 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.1577 (1.377) Data (t): 0.001 Batch (t): 0.906, 565.673/s LR: 0.000005 Logit Scale: 99.883 - V4
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+ 2024-11-27,01:49:21 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.3642 (1.376) Data (t): 0.001 Batch (t): 0.907, 564.255/s LR: 0.000005 Logit Scale: 99.883 - V4
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+ 2024-11-27,01:50:52 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.0692 (1.372) Data (t): 0.001 Batch (t): 0.907, 562.650/s LR: 0.000005 Logit Scale: 99.883 - V4
186
+ 2024-11-27,01:52:22 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.0010 (1.367) Data (t): 0.001 Batch (t): 0.905, 567.810/s LR: 0.000005 Logit Scale: 99.881 - V4
187
+ 2024-11-27,01:53:57 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.2472 (1.365) Data (t): 0.001 Batch (t): 0.953, 567.326/s LR: 0.000005 Logit Scale: 99.879 - V4
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+ 2024-11-27,01:55:29 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.1852 (1.363) Data (t): 0.001 Batch (t): 0.915, 562.093/s LR: 0.000005 Logit Scale: 99.879 - V4
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+ 2024-11-27,01:57:00 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.2980 (1.362) Data (t): 0.001 Batch (t): 0.906, 564.317/s LR: 0.000005 Logit Scale: 99.880 - V4
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+ 2024-11-27,01:58:30 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.0737 (1.358) Data (t): 0.001 Batch (t): 0.907, 563.488/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:00:01 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.2507 (1.357) Data (t): 0.001 Batch (t): 0.906, 564.094/s LR: 0.000005 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:01:35 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.2840 (1.356) Data (t): 0.001 Batch (t): 0.937, 566.199/s LR: 0.000005 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:03:09 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.2218 (1.354) Data (t): 0.001 Batch (t): 0.942, 563.305/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:04:40 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.1864 (1.352) Data (t): 0.001 Batch (t): 0.908, 561.669/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:06:10 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.1524 (1.349) Data (t): 0.001 Batch (t): 0.908, 561.194/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:07:41 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.0936 (1.346) Data (t): 0.001 Batch (t): 0.907, 563.653/s LR: 0.000005 Logit Scale: 99.880 - V4
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+ 2024-11-27,02:09:12 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.0619 (1.343) Data (t): 0.001 Batch (t): 0.907, 567.106/s LR: 0.000005 Logit Scale: 99.876 - V4
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+ 2024-11-27,02:10:49 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.2763 (1.342) Data (t): 0.001 Batch (t): 0.973, 566.887/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:12:20 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.2116 (1.340) Data (t): 0.001 Batch (t): 0.906, 565.165/s LR: 0.000005 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:13:50 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.2519 (1.339) Data (t): 0.001 Batch (t): 0.907, 565.488/s LR: 0.000005 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:15:21 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.0579 (1.336) Data (t): 0.001 Batch (t): 0.907, 565.474/s LR: 0.000005 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:16:52 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.1732 (1.334) Data (t): 0.001 Batch (t): 0.905, 566.659/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:18:29 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.3028 (1.334) Data (t): 0.001 Batch (t): 0.973, 564.583/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:20:00 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.1581 (1.332) Data (t): 0.001 Batch (t): 0.907, 564.599/s LR: 0.000004 Logit Scale: 99.876 - V4
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+ 2024-11-27,02:21:30 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.1656 (1.330) Data (t): 0.001 Batch (t): 0.908, 565.709/s LR: 0.000004 Logit Scale: 99.876 - V4
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+ 2024-11-27,02:23:01 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.2345 (1.329) Data (t): 0.001 Batch (t): 0.908, 567.399/s LR: 0.000004 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:24:32 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.2057 (1.328) Data (t): 0.001 Batch (t): 0.907, 565.329/s LR: 0.000004 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:26:08 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.1793 (1.326) Data (t): 0.001 Batch (t): 0.961, 567.611/s LR: 0.000004 Logit Scale: 99.875 - V4
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+ 2024-11-27,02:27:40 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.1822 (1.324) Data (t): 0.001 Batch (t): 0.916, 564.163/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:29:10 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.1163 (1.322) Data (t): 0.001 Batch (t): 0.906, 565.708/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:30:41 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.2176 (1.321) Data (t): 0.001 Batch (t): 0.907, 566.702/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:32:12 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.1790 (1.320) Data (t): 0.001 Batch (t): 0.906, 565.709/s LR: 0.000004 Logit Scale: 99.876 - V4
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+ 2024-11-27,02:33:48 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.1488 (1.318) Data (t): 0.001 Batch (t): 0.962, 565.630/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:35:20 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.2160 (1.317) Data (t): 0.001 Batch (t): 0.917, 564.620/s LR: 0.000004 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:36:50 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.0144 (1.314) Data (t): 0.001 Batch (t): 0.907, 565.310/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:38:21 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.0713 (1.311) Data (t): 0.001 Batch (t): 0.906, 564.709/s LR: 0.000004 Logit Scale: 99.880 - V4
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+ 2024-11-27,02:39:52 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.1245 (1.310) Data (t): 0.001 Batch (t): 0.907, 564.576/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:41:27 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.1152 (1.308) Data (t): 0.001 Batch (t): 0.952, 323.467/s LR: 0.000004 Logit Scale: 99.877 - V4
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+ 2024-11-27,02:42:59 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.0976 (1.306) Data (t): 0.001 Batch (t): 0.926, 566.396/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:44:30 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.1582 (1.304) Data (t): 0.001 Batch (t): 0.906, 565.759/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:46:01 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.0433 (1.302) Data (t): 0.001 Batch (t): 0.905, 563.090/s LR: 0.000004 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:47:31 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.1670 (1.301) Data (t): 0.001 Batch (t): 0.907, 565.995/s LR: 0.000004 Logit Scale: 99.878 - V4
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+ 2024-11-27,02:49:02 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.2039 (1.300) Data (t): 0.001 Batch (t): 0.907, 565.876/s LR: 0.000004 Logit Scale: 99.879 - V4
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+ 2024-11-27,02:50:38 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.1855 (1.299) Data (t): 0.001 Batch (t): 0.964, 565.582/s LR: 0.000004 Logit Scale: 99.881 - V4
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+ 2024-11-27,02:52:09 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.0581 (1.296) Data (t): 0.001 Batch (t): 0.905, 563.976/s LR: 0.000004 Logit Scale: 99.883 - V4
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+ 2024-11-27,02:53:40 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.1543 (1.295) Data (t): 0.001 Batch (t): 0.907, 565.803/s LR: 0.000004 Logit Scale: 99.884 - V4
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+ 2024-11-27,02:55:10 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.0941 (1.293) Data (t): 0.001 Batch (t): 0.906, 565.987/s LR: 0.000004 Logit Scale: 99.883 - V4
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+ 2024-11-27,02:56:41 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.0296 (1.291) Data (t): 0.001 Batch (t): 0.908, 563.915/s LR: 0.000004 Logit Scale: 99.887 - V4
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+ 2024-11-27,02:58:17 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.0786 (1.289) Data (t): 0.001 Batch (t): 0.962, 560.123/s LR: 0.000004 Logit Scale: 99.887 - V4
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+ 2024-11-27,02:59:49 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 0.97867 (1.287) Data (t): 0.001 Batch (t): 0.917, 559.717/s LR: 0.000004 Logit Scale: 99.887 - V4
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+ 2024-11-27,03:01:20 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.1695 (1.285) Data (t): 0.001 Batch (t): 0.907, 564.841/s LR: 0.000004 Logit Scale: 99.889 - V4
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+ 2024-11-27,03:02:50 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.2086 (1.285) Data (t): 0.001 Batch (t): 0.907, 565.609/s LR: 0.000004 Logit Scale: 99.891 - V4
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+ 2024-11-27,03:04:21 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.1527 (1.284) Data (t): 0.001 Batch (t): 0.907, 564.843/s LR: 0.000004 Logit Scale: 99.894 - V4
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+ 2024-11-27,03:05:57 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 0.91726 (1.281) Data (t): 0.001 Batch (t): 0.963, 567.789/s LR: 0.000004 Logit Scale: 99.894 - V4
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+ 2024-11-27,03:07:29 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.0480 (1.279) Data (t): 0.001 Batch (t): 0.914, 566.422/s LR: 0.000004 Logit Scale: 99.894 - V4
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+ 2024-11-27,03:08:59 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.2131 (1.278) Data (t): 0.001 Batch (t): 0.906, 564.047/s LR: 0.000004 Logit Scale: 99.897 - V4
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+ 2024-11-27,03:10:30 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.1538 (1.277) Data (t): 0.001 Batch (t): 0.907, 564.773/s LR: 0.000004 Logit Scale: 99.899 - V4
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+ 2024-11-27,03:12:00 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.0573 (1.275) Data (t): 0.001 Batch (t): 0.904, 565.742/s LR: 0.000004 Logit Scale: 99.898 - V4
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+ 2024-11-27,03:13:36 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.0688 (1.274) Data (t): 0.001 Batch (t): 0.953, 565.402/s LR: 0.000004 Logit Scale: 99.900 - V4
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+ 2024-11-27,03:15:08 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.1105 (1.272) Data (t): 0.001 Batch (t): 0.927, 565.196/s LR: 0.000004 Logit Scale: 99.902 - V4
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+ 2024-11-27,03:16:39 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.2064 (1.272) Data (t): 0.001 Batch (t): 0.906, 565.563/s LR: 0.000004 Logit Scale: 99.903 - V4
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+ 2024-11-27,03:18:10 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.1697 (1.271) Data (t): 0.001 Batch (t): 0.906, 565.669/s LR: 0.000004 Logit Scale: 99.904 - V4
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+ 2024-11-27,03:19:40 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.1387 (1.270) Data (t): 0.001 Batch (t): 0.906, 567.191/s LR: 0.000004 Logit Scale: 99.905 - V4
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+ 2024-11-27,03:21:14 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.0262 (1.268) Data (t): 0.001 Batch (t): 0.938, 319.866/s LR: 0.000004 Logit Scale: 99.907 - V4
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+ 2024-11-27,03:22:48 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.0539 (1.266) Data (t): 0.001 Batch (t): 0.941, 565.541/s LR: 0.000004 Logit Scale: 99.909 - V4
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+ 2024-11-27,03:24:19 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.0189 (1.265) Data (t): 0.001 Batch (t): 0.905, 562.521/s LR: 0.000004 Logit Scale: 99.912 - V4
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+ 2024-11-27,03:25:49 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.0560 (1.263) Data (t): 0.001 Batch (t): 0.906, 566.932/s LR: 0.000004 Logit Scale: 99.911 - V4
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+ 2024-11-27,03:27:20 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.0828 (1.262) Data (t): 0.001 Batch (t): 0.906, 564.973/s LR: 0.000004 Logit Scale: 99.911 - V4
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+ 2024-11-27,03:28:50 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.1878 (1.261) Data (t): 0.001 Batch (t): 0.905, 566.106/s LR: 0.000004 Logit Scale: 99.914 - V4
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+ 2024-11-27,03:30:27 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.1957 (1.261) Data (t): 0.001 Batch (t): 0.961, 566.985/s LR: 0.000004 Logit Scale: 99.915 - V4
251
+ 2024-11-27,03:31:58 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.0947 (1.259) Data (t): 0.001 Batch (t): 0.915, 565.294/s LR: 0.000004 Logit Scale: 99.918 - V4
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+ 2024-11-27,03:33:29 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.1910 (1.259) Data (t): 0.001 Batch (t): 0.905, 564.840/s LR: 0.000004 Logit Scale: 99.920 - V4
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+ 2024-11-27,03:34:59 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 0.98965 (1.257) Data (t): 0.001 Batch (t): 0.905, 566.808/s LR: 0.000004 Logit Scale: 99.919 - V4
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+ 2024-11-27,03:36:30 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.2333 (1.257) Data (t): 0.001 Batch (t): 0.906, 563.277/s LR: 0.000004 Logit Scale: 99.922 - V4
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+ 2024-11-27,03:38:06 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.0522 (1.255) Data (t): 0.001 Batch (t): 0.964, 261.884/s LR: 0.000004 Logit Scale: 99.923 - V4
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+ 2024-11-27,03:39:38 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1185 (1.254) Data (t): 0.001 Batch (t): 0.917, 563.931/s LR: 0.000004 Logit Scale: 99.926 - V4
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+ 2024-11-27,03:41:08 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.0537 (1.253) Data (t): 0.001 Batch (t): 0.905, 567.143/s LR: 0.000004 Logit Scale: 99.928 - V4
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+ 2024-11-27,03:42:39 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.2028 (1.253) Data (t): 0.001 Batch (t): 0.907, 565.542/s LR: 0.000004 Logit Scale: 99.932 - V4
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+ 2024-11-27,03:44:10 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 1.1686 (1.252) Data (t): 0.001 Batch (t): 0.908, 566.566/s LR: 0.000004 Logit Scale: 99.932 - V4
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+ 2024-11-27,03:45:45 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.0920 (1.251) Data (t): 0.001 Batch (t): 0.953, 565.327/s LR: 0.000004 Logit Scale: 99.935 - V4
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+ 2024-11-27,03:47:18 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.1358 (1.250) Data (t): 0.001 Batch (t): 0.928, 567.053/s LR: 0.000004 Logit Scale: 99.937 - V4
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+ 2024-11-27,03:48:48 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.1103 (1.249) Data (t): 0.001 Batch (t): 0.907, 564.318/s LR: 0.000004 Logit Scale: 99.939 - V4
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+ 2024-11-27,03:50:19 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.1976 (1.249) Data (t): 0.001 Batch (t): 0.906, 566.610/s LR: 0.000004 Logit Scale: 99.941 - V4
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+ 2024-11-27,03:51:50 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.0606 (1.248) Data (t): 0.001 Batch (t): 0.906, 566.326/s LR: 0.000004 Logit Scale: 99.941 - V4
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+ 2024-11-27,03:53:25 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 1.1570 (1.247) Data (t): 0.001 Batch (t): 0.953, 566.576/s LR: 0.000004 Logit Scale: 99.946 - V4
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+ 2024-11-27,03:54:57 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.0365 (1.246) Data (t): 0.001 Batch (t): 0.926, 565.160/s LR: 0.000004 Logit Scale: 99.947 - V4
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+ 2024-11-27,03:56:28 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.0135 (1.244) Data (t): 0.001 Batch (t): 0.904, 564.774/s LR: 0.000004 Logit Scale: 99.950 - V4
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+ 2024-11-27,03:57:58 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.2436 (1.244) Data (t): 0.001 Batch (t): 0.905, 565.661/s LR: 0.000004 Logit Scale: 99.953 - V4
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+ 2024-11-27,03:59:29 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.0197 (1.243) Data (t): 0.001 Batch (t): 0.905, 565.650/s LR: 0.000004 Logit Scale: 99.957 - V4
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+ 2024-11-27,04:01:03 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.0551 (1.241) Data (t): 0.001 Batch (t): 0.938, 566.026/s LR: 0.000003 Logit Scale: 99.958 - V4
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+ 2024-11-27,04:02:36 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.1634 (1.241) Data (t): 0.001 Batch (t): 0.932, 565.840/s LR: 0.000003 Logit Scale: 99.960 - V4
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+ 2024-11-27,04:04:07 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.0412 (1.240) Data (t): 0.001 Batch (t): 0.916, 565.631/s LR: 0.000003 Logit Scale: 99.963 - V4
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+ 2024-11-27,04:05:38 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.1871 (1.239) Data (t): 0.001 Batch (t): 0.907, 564.041/s LR: 0.000003 Logit Scale: 99.965 - V4
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+ 2024-11-27,04:07:09 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.2274 (1.239) Data (t): 0.001 Batch (t): 0.908, 562.570/s LR: 0.000003 Logit Scale: 99.967 - V4
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+ 2024-11-27,04:08:40 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.1849 (1.239) Data (t): 0.001 Batch (t): 0.907, 563.275/s LR: 0.000003 Logit Scale: 99.969 - V4
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+ 2024-11-27,04:10:16 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.0565 (1.238) Data (t): 0.001 Batch (t): 0.965, 566.708/s LR: 0.000003 Logit Scale: 99.972 - V4
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+ 2024-11-27,04:11:48 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 0.96787 (1.236) Data (t): 0.001 Batch (t): 0.916, 566.115/s LR: 0.000003 Logit Scale: 99.975 - V4
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+ 2024-11-27,04:13:18 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.2400 (1.236) Data (t): 0.001 Batch (t): 0.905, 564.940/s LR: 0.000003 Logit Scale: 99.978 - V4
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+ 2024-11-27,04:14:49 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.1404 (1.235) Data (t): 0.001 Batch (t): 0.907, 567.661/s LR: 0.000003 Logit Scale: 99.981 - V4
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+ 2024-11-27,04:16:20 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.0614 (1.234) Data (t): 0.001 Batch (t): 0.907, 561.883/s LR: 0.000003 Logit Scale: 99.984 - V4
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+ 2024-11-27,04:17:55 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.0117 (1.233) Data (t): 0.001 Batch (t): 0.956, 564.351/s LR: 0.000003 Logit Scale: 99.990 - V4
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+ 2024-11-27,04:19:28 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.0210 (1.232) Data (t): 0.001 Batch (t): 0.929, 565.590/s LR: 0.000003 Logit Scale: 99.990 - V4
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+ 2024-11-27,04:20:59 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.0776 (1.231) Data (t): 0.001 Batch (t): 0.907, 561.515/s LR: 0.000003 Logit Scale: 99.993 - V4
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+ 2024-11-27,04:22:30 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.2603 (1.231) Data (t): 0.001 Batch (t): 0.908, 565.205/s LR: 0.000003 Logit Scale: 99.995 - V4
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+ 2024-11-27,04:24:00 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.0624 (1.230) Data (t): 0.001 Batch (t): 0.907, 567.606/s LR: 0.000003 Logit Scale: 99.997 - V4
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+ 2024-11-27,04:25:36 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.0406 (1.229) Data (t): 0.001 Batch (t): 0.954, 563.472/s LR: 0.000003 Logit Scale: 99.999 - V4
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+ 2024-11-27,04:27:09 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.1790 (1.229) Data (t): 0.001 Batch (t): 0.929, 564.248/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:28:39 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.0720 (1.228) Data (t): 0.001 Batch (t): 0.907, 565.555/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:30:10 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.1143 (1.227) Data (t): 0.001 Batch (t): 0.908, 562.775/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:31:41 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.1544 (1.227) Data (t): 0.001 Batch (t): 0.906, 567.008/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:33:16 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.2570 (1.227) Data (t): 0.001 Batch (t): 0.954, 566.439/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:34:48 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.0736 (1.226) Data (t): 0.001 Batch (t): 0.917, 565.595/s LR: 0.000003 Logit Scale: 99.999 - V4
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+ 2024-11-27,04:36:20 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.0660 (1.225) Data (t): 0.001 Batch (t): 0.918, 566.646/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:37:50 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.0883 (1.224) Data (t): 0.001 Batch (t): 0.907, 563.627/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:39:21 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.1771 (1.224) Data (t): 0.001 Batch (t): 0.907, 568.406/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:40:54 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.0513 (1.223) Data (t): 0.001 Batch (t): 0.930, 564.746/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:42:28 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.1358 (1.223) Data (t): 0.001 Batch (t): 0.942, 565.882/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:44:00 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.0467 (1.222) Data (t): 0.001 Batch (t): 0.915, 566.400/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:45:30 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.0472 (1.221) Data (t): 0.001 Batch (t): 0.905, 567.472/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:47:01 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 1.1237 (1.220) Data (t): 0.001 Batch (t): 0.905, 562.914/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:48:31 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 0.98222 (1.219) Data (t): 0.001 Batch (t): 0.905, 563.595/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:50:07 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.0848 (1.218) Data (t): 0.001 Batch (t): 0.955, 560.502/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:51:40 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.0220 (1.217) Data (t): 0.001 Batch (t): 0.928, 562.770/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:53:10 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.0301 (1.216) Data (t): 0.001 Batch (t): 0.906, 564.688/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:54:41 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.0248 (1.215) Data (t): 0.001 Batch (t): 0.906, 565.551/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:56:12 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.2502 (1.215) Data (t): 0.001 Batch (t): 0.906, 566.045/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:57:47 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 0.91515 (1.214) Data (t): 0.001 Batch (t): 0.956, 563.774/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,04:59:20 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.1894 (1.214) Data (t): 0.001 Batch (t): 0.928, 561.993/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:00:51 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.2648 (1.214) Data (t): 0.001 Batch (t): 0.906, 564.816/s LR: 0.000003 Logit Scale: 99.999 - V4
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+ 2024-11-27,05:02:21 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.0943 (1.213) Data (t): 0.001 Batch (t): 0.907, 565.354/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:03:52 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.1343 (1.213) Data (t): 0.001 Batch (t): 0.907, 564.173/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:05:28 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.1223 (1.212) Data (t): 0.001 Batch (t): 0.956, 565.636/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:07:00 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.1591 (1.212) Data (t): 0.001 Batch (t): 0.928, 565.406/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:08:31 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.0973 (1.212) Data (t): 0.001 Batch (t): 0.905, 565.222/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:10:02 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.2007 (1.212) Data (t): 0.001 Batch (t): 0.905, 567.536/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:11:32 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.0202 (1.211) Data (t): 0.001 Batch (t): 0.905, 563.347/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:13:07 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.1201 (1.210) Data (t): 0.001 Batch (t): 0.949, 567.357/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:14:39 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 0.90820 (1.209) Data (t): 0.001 Batch (t): 0.923, 564.457/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:16:11 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.0708 (1.208) Data (t): 0.001 Batch (t): 0.917, 561.375/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:17:42 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.2011 (1.208) Data (t): 0.001 Batch (t): 0.906, 565.315/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:19:12 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 0.97144 (1.207) Data (t): 0.001 Batch (t): 0.904, 565.353/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:20:43 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.2910 (1.207) Data (t): 0.001 Batch (t): 0.914, 290.590/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:22:17 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.1027 (1.207) Data (t): 0.001 Batch (t): 0.938, 565.447/s LR: 0.000003 Logit Scale: 100.000 - V4
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+ 2024-11-27,05:23:26 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.1504 (1.206) Data (t): 0.001 Batch (t): 0.934, 569.359/s LR: 0.000003 Logit Scale: 100.000 - V4
325
+ 2024-11-27,05:23:34 | INFO | Start epoch 1
326
+ 2024-11-27,05:23:38 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 0.99605 (0.9960) Data (t): 3.047 Batch (t): 3.970, 128.961/s LR: 0.000003 Logit Scale: 100.000 - V4
327
+ 2024-11-27,05:25:08 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.0653 (1.031) Data (t): 0.001 Batch (t): 0.908, 565.990/s LR: 0.000002 Logit Scale: 100.000 - V4
328
+ 2024-11-27,05:26:39 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.0920 (1.051) Data (t): 0.001 Batch (t): 0.906, 566.813/s LR: 0.000002 Logit Scale: 100.000 - V4
329
+ 2024-11-27,05:28:10 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.1251 (1.070) Data (t): 0.001 Batch (t): 0.906, 563.222/s LR: 0.000002 Logit Scale: 100.000 - V4
330
+ 2024-11-27,05:29:46 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 0.96004 (1.048) Data (t): 0.001 Batch (t): 0.961, 567.453/s LR: 0.000002 Logit Scale: 100.000 - V4
331
+ 2024-11-27,05:31:17 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 1.1357 (1.062) Data (t): 0.001 Batch (t): 0.914, 566.725/s LR: 0.000002 Logit Scale: 100.000 - V4
332
+ 2024-11-27,05:32:47 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.0054 (1.054) Data (t): 0.001 Batch (t): 0.904, 567.400/s LR: 0.000002 Logit Scale: 100.000 - V4
333
+ 2024-11-27,05:34:18 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.1098 (1.061) Data (t): 0.001 Batch (t): 0.905, 566.172/s LR: 0.000002 Logit Scale: 100.000 - V4
334
+ 2024-11-27,05:35:48 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.1216 (1.068) Data (t): 0.001 Batch (t): 0.904, 566.204/s LR: 0.000002 Logit Scale: 100.000 - V4
335
+ 2024-11-27,05:37:23 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 0.89254 (1.050) Data (t): 0.001 Batch (t): 0.942, 566.342/s LR: 0.000002 Logit Scale: 100.000 - V4
336
+ 2024-11-27,05:38:55 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.0946 (1.054) Data (t): 0.001 Batch (t): 0.922, 275.669/s LR: 0.000002 Logit Scale: 100.000 - V4
337
+ 2024-11-27,05:40:25 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.1492 (1.062) Data (t): 0.001 Batch (t): 0.905, 564.122/s LR: 0.000002 Logit Scale: 100.000 - V4
338
+ 2024-11-27,05:41:56 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.0533 (1.062) Data (t): 0.001 Batch (t): 0.906, 567.595/s LR: 0.000002 Logit Scale: 100.000 - V4
339
+ 2024-11-27,05:43:26 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 1.0244 (1.059) Data (t): 0.001 Batch (t): 0.904, 568.043/s LR: 0.000002 Logit Scale: 100.000 - V4
340
+ 2024-11-27,05:45:00 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.1731 (1.067) Data (t): 0.001 Batch (t): 0.935, 567.687/s LR: 0.000002 Logit Scale: 100.000 - V4
341
+ 2024-11-27,05:46:32 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 1.0956 (1.068) Data (t): 0.001 Batch (t): 0.926, 562.400/s LR: 0.000002 Logit Scale: 100.000 - V4
342
+ 2024-11-27,05:48:04 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.1266 (1.072) Data (t): 0.001 Batch (t): 0.915, 565.419/s LR: 0.000002 Logit Scale: 100.000 - V4
343
+ 2024-11-27,05:49:34 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 0.92508 (1.064) Data (t): 0.001 Batch (t): 0.905, 564.122/s LR: 0.000002 Logit Scale: 100.000 - V4
344
+ 2024-11-27,05:51:05 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.0077 (1.061) Data (t): 0.001 Batch (t): 0.907, 563.798/s LR: 0.000002 Logit Scale: 100.000 - V4
345
+ 2024-11-27,05:52:36 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.1039 (1.063) Data (t): 0.001 Batch (t): 0.912, 325.849/s LR: 0.000002 Logit Scale: 100.000 - V4
346
+ 2024-11-27,05:54:11 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 1.0021 (1.060) Data (t): 0.001 Batch (t): 0.951, 565.388/s LR: 0.000002 Logit Scale: 100.000 - V4
347
+ 2024-11-27,05:55:43 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.0619 (1.060) Data (t): 0.001 Batch (t): 0.917, 563.605/s LR: 0.000002 Logit Scale: 100.000 - V4
348
+ 2024-11-27,05:57:14 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 0.97982 (1.057) Data (t): 0.001 Batch (t): 0.906, 565.205/s LR: 0.000002 Logit Scale: 100.000 - V4
349
+ 2024-11-27,05:58:44 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.0500 (1.056) Data (t): 0.001 Batch (t): 0.906, 564.697/s LR: 0.000002 Logit Scale: 100.000 - V4
350
+ 2024-11-27,06:00:15 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.1384 (1.060) Data (t): 0.001 Batch (t): 0.907, 565.260/s LR: 0.000002 Logit Scale: 100.000 - V4
351
+ 2024-11-27,06:01:51 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 0.89766 (1.053) Data (t): 0.001 Batch (t): 0.958, 561.988/s LR: 0.000002 Logit Scale: 100.000 - V4
352
+ 2024-11-27,06:03:22 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.0120 (1.052) Data (t): 0.001 Batch (t): 0.915, 565.976/s LR: 0.000002 Logit Scale: 100.000 - V4
353
+ 2024-11-27,06:04:53 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.0200 (1.051) Data (t): 0.001 Batch (t): 0.906, 560.877/s LR: 0.000002 Logit Scale: 100.000 - V4
354
+ 2024-11-27,06:06:24 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 0.97388 (1.048) Data (t): 0.001 Batch (t): 0.906, 566.830/s LR: 0.000002 Logit Scale: 100.000 - V4
355
+ 2024-11-27,06:07:54 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 0.99186 (1.046) Data (t): 0.001 Batch (t): 0.905, 567.056/s LR: 0.000002 Logit Scale: 100.000 - V4
356
+ 2024-11-27,06:09:29 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 0.96438 (1.044) Data (t): 0.001 Batch (t): 0.948, 567.650/s LR: 0.000002 Logit Scale: 100.000 - V4
357
+ 2024-11-27,06:11:01 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.0170 (1.043) Data (t): 0.001 Batch (t): 0.922, 564.996/s LR: 0.000002 Logit Scale: 100.000 - V4
358
+ 2024-11-27,06:12:32 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 0.91122 (1.039) Data (t): 0.001 Batch (t): 0.906, 563.498/s LR: 0.000002 Logit Scale: 100.000 - V4
359
+ 2024-11-27,06:14:02 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 0.98500 (1.037) Data (t): 0.001 Batch (t): 0.907, 565.624/s LR: 0.000002 Logit Scale: 100.000 - V4
360
+ 2024-11-27,06:15:33 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 0.94624 (1.035) Data (t): 0.001 Batch (t): 0.904, 566.717/s LR: 0.000002 Logit Scale: 100.000 - V4
361
+ 2024-11-27,06:17:08 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.0650 (1.035) Data (t): 0.001 Batch (t): 0.950, 562.945/s LR: 0.000002 Logit Scale: 100.000 - V4
362
+ 2024-11-27,06:18:39 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 0.85102 (1.030) Data (t): 0.001 Batch (t): 0.915, 564.332/s LR: 0.000002 Logit Scale: 100.000 - V4
363
+ 2024-11-27,06:20:11 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 1.1179 (1.033) Data (t): 0.001 Batch (t): 0.917, 567.175/s LR: 0.000002 Logit Scale: 100.000 - V4
364
+ 2024-11-27,06:21:42 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.0313 (1.033) Data (t): 0.001 Batch (t): 0.907, 562.038/s LR: 0.000002 Logit Scale: 100.000 - V4
365
+ 2024-11-27,06:23:12 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.0216 (1.032) Data (t): 0.001 Batch (t): 0.905, 564.146/s LR: 0.000002 Logit Scale: 100.000 - V4
366
+ 2024-11-27,06:24:46 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 0.93176 (1.030) Data (t): 0.001 Batch (t): 0.937, 567.844/s LR: 0.000002 Logit Scale: 100.000 - V4
367
+ 2024-11-27,06:26:19 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 1.0021 (1.029) Data (t): 0.001 Batch (t): 0.927, 563.530/s LR: 0.000002 Logit Scale: 100.000 - V4
368
+ 2024-11-27,06:27:50 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 1.0069 (1.029) Data (t): 0.001 Batch (t): 0.915, 564.485/s LR: 0.000002 Logit Scale: 100.000 - V4
369
+ 2024-11-27,06:29:21 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 1.0092 (1.028) Data (t): 0.001 Batch (t): 0.905, 564.221/s LR: 0.000002 Logit Scale: 100.000 - V4
370
+ 2024-11-27,06:30:51 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 0.99873 (1.028) Data (t): 0.001 Batch (t): 0.905, 564.241/s LR: 0.000002 Logit Scale: 100.000 - V4
371
+ 2024-11-27,06:32:22 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.0552 (1.028) Data (t): 0.001 Batch (t): 0.914, 566.532/s LR: 0.000002 Logit Scale: 100.000 - V4
372
+ 2024-11-27,06:33:58 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.1399 (1.031) Data (t): 0.001 Batch (t): 0.952, 565.086/s LR: 0.000002 Logit Scale: 100.000 - V4
373
+ 2024-11-27,06:35:29 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.0182 (1.030) Data (t): 0.001 Batch (t): 0.917, 564.931/s LR: 0.000002 Logit Scale: 100.000 - V4
374
+ 2024-11-27,06:37:00 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.1023 (1.032) Data (t): 0.001 Batch (t): 0.906, 567.323/s LR: 0.000002 Logit Scale: 100.000 - V4
375
+ 2024-11-27,06:38:30 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 0.98974 (1.031) Data (t): 0.001 Batch (t): 0.905, 566.239/s LR: 0.000002 Logit Scale: 100.000 - V4
376
+ 2024-11-27,06:40:01 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.0722 (1.032) Data (t): 0.001 Batch (t): 0.907, 564.504/s LR: 0.000002 Logit Scale: 100.000 - V4
377
+ 2024-11-27,06:41:36 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.1386 (1.034) Data (t): 0.001 Batch (t): 0.951, 562.775/s LR: 0.000002 Logit Scale: 100.000 - V4
378
+ 2024-11-27,06:43:08 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.0205 (1.034) Data (t): 0.001 Batch (t): 0.922, 561.643/s LR: 0.000002 Logit Scale: 100.000 - V4
379
+ 2024-11-27,06:44:39 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 0.96769 (1.032) Data (t): 0.001 Batch (t): 0.904, 566.540/s LR: 0.000002 Logit Scale: 100.000 - V4
380
+ 2024-11-27,06:46:09 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.0600 (1.033) Data (t): 0.001 Batch (t): 0.907, 561.604/s LR: 0.000002 Logit Scale: 100.000 - V4
381
+ 2024-11-27,06:47:40 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 0.95154 (1.031) Data (t): 0.001 Batch (t): 0.907, 563.200/s LR: 0.000001 Logit Scale: 100.000 - V4
382
+ 2024-11-27,06:49:15 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.0999 (1.033) Data (t): 0.001 Batch (t): 0.951, 565.942/s LR: 0.000001 Logit Scale: 100.000 - V4
383
+ 2024-11-27,06:50:48 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.0344 (1.033) Data (t): 0.001 Batch (t): 0.923, 566.765/s LR: 0.000001 Logit Scale: 100.000 - V4
384
+ 2024-11-27,06:52:18 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.0589 (1.033) Data (t): 0.001 Batch (t): 0.907, 567.177/s LR: 0.000001 Logit Scale: 100.000 - V4
385
+ 2024-11-27,06:53:49 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.0874 (1.034) Data (t): 0.001 Batch (t): 0.906, 564.328/s LR: 0.000001 Logit Scale: 100.000 - V4
386
+ 2024-11-27,06:55:20 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 1.0388 (1.034) Data (t): 0.001 Batch (t): 0.907, 564.209/s LR: 0.000001 Logit Scale: 100.000 - V4
387
+ 2024-11-27,06:56:54 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.1097 (1.035) Data (t): 0.001 Batch (t): 0.944, 565.862/s LR: 0.000001 Logit Scale: 100.000 - V4
388
+ 2024-11-27,06:58:25 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 0.95967 (1.034) Data (t): 0.001 Batch (t): 0.912, 561.042/s LR: 0.000001 Logit Scale: 100.000 - V4
389
+ 2024-11-27,06:59:57 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.1461 (1.036) Data (t): 0.001 Batch (t): 0.915, 562.685/s LR: 0.000001 Logit Scale: 100.000 - V4
390
+ 2024-11-27,07:01:27 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.0143 (1.036) Data (t): 0.001 Batch (t): 0.906, 563.140/s LR: 0.000001 Logit Scale: 100.000 - V4
391
+ 2024-11-27,07:02:58 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.2315 (1.038) Data (t): 0.001 Batch (t): 0.905, 565.750/s LR: 0.000001 Logit Scale: 100.000 - V4
392
+ 2024-11-27,07:04:30 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 0.99611 (1.038) Data (t): 0.001 Batch (t): 0.918, 566.282/s LR: 0.000001 Logit Scale: 100.000 - V4
393
+ 2024-11-27,07:06:04 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.1552 (1.040) Data (t): 0.001 Batch (t): 0.941, 566.611/s LR: 0.000001 Logit Scale: 100.000 - V4
394
+ 2024-11-27,07:07:35 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 0.95877 (1.038) Data (t): 0.001 Batch (t): 0.916, 566.782/s LR: 0.000001 Logit Scale: 100.000 - V4
395
+ 2024-11-27,07:09:06 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.0217 (1.038) Data (t): 0.001 Batch (t): 0.904, 566.456/s LR: 0.000001 Logit Scale: 100.000 - V4
396
+ 2024-11-27,07:10:36 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 0.96306 (1.037) Data (t): 0.001 Batch (t): 0.906, 566.385/s LR: 0.000001 Logit Scale: 100.000 - V4
397
+ 2024-11-27,07:12:07 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.0495 (1.037) Data (t): 0.001 Batch (t): 0.905, 564.076/s LR: 0.000001 Logit Scale: 100.000 - V4
398
+ 2024-11-27,07:13:41 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 0.86775 (1.035) Data (t): 0.001 Batch (t): 0.942, 565.481/s LR: 0.000001 Logit Scale: 100.000 - V4
399
+ 2024-11-27,07:15:13 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 1.1263 (1.036) Data (t): 0.001 Batch (t): 0.921, 564.546/s LR: 0.000001 Logit Scale: 100.000 - V4
400
+ 2024-11-27,07:16:44 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.0803 (1.037) Data (t): 0.001 Batch (t): 0.904, 566.396/s LR: 0.000001 Logit Scale: 100.000 - V4
401
+ 2024-11-27,07:18:14 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.0569 (1.037) Data (t): 0.001 Batch (t): 0.904, 567.187/s LR: 0.000001 Logit Scale: 100.000 - V4
402
+ 2024-11-27,07:19:44 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 0.92729 (1.036) Data (t): 0.001 Batch (t): 0.904, 565.604/s LR: 0.000001 Logit Scale: 100.000 - V4
403
+ 2024-11-27,07:21:19 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 0.91242 (1.034) Data (t): 0.001 Batch (t): 0.942, 566.131/s LR: 0.000001 Logit Scale: 100.000 - V4
404
+ 2024-11-27,07:22:51 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 0.89468 (1.032) Data (t): 0.001 Batch (t): 0.921, 564.739/s LR: 0.000001 Logit Scale: 100.000 - V4
405
+ 2024-11-27,07:24:21 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 0.99255 (1.032) Data (t): 0.001 Batch (t): 0.905, 565.871/s LR: 0.000001 Logit Scale: 100.000 - V4
406
+ 2024-11-27,07:25:52 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.0470 (1.032) Data (t): 0.001 Batch (t): 0.904, 566.582/s LR: 0.000001 Logit Scale: 100.000 - V4
407
+ 2024-11-27,07:27:22 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 0.93214 (1.031) Data (t): 0.001 Batch (t): 0.904, 565.994/s LR: 0.000001 Logit Scale: 100.000 - V4
408
+ 2024-11-27,07:28:56 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.0591 (1.031) Data (t): 0.001 Batch (t): 0.942, 566.044/s LR: 0.000001 Logit Scale: 100.000 - V4
409
+ 2024-11-27,07:30:28 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.1059 (1.032) Data (t): 0.001 Batch (t): 0.921, 565.716/s LR: 0.000001 Logit Scale: 100.000 - V4
410
+ 2024-11-27,07:31:59 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 0.91878 (1.031) Data (t): 0.001 Batch (t): 0.906, 564.831/s LR: 0.000001 Logit Scale: 100.000 - V4
411
+ 2024-11-27,07:33:30 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 0.99103 (1.030) Data (t): 0.001 Batch (t): 0.907, 563.558/s LR: 0.000001 Logit Scale: 100.000 - V4
412
+ 2024-11-27,07:35:00 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 1.0128 (1.030) Data (t): 0.001 Batch (t): 0.907, 564.319/s LR: 0.000001 Logit Scale: 100.000 - V4
413
+ 2024-11-27,07:36:35 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.1169 (1.031) Data (t): 0.001 Batch (t): 0.945, 565.396/s LR: 0.000001 Logit Scale: 100.000 - V4
414
+ 2024-11-27,07:38:06 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 0.93320 (1.030) Data (t): 0.001 Batch (t): 0.913, 563.863/s LR: 0.000001 Logit Scale: 100.000 - V4
415
+ 2024-11-27,07:39:38 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 0.96299 (1.029) Data (t): 0.001 Batch (t): 0.915, 565.037/s LR: 0.000001 Logit Scale: 100.000 - V4
416
+ 2024-11-27,07:41:08 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.0005 (1.029) Data (t): 0.001 Batch (t): 0.905, 563.905/s LR: 0.000001 Logit Scale: 100.000 - V4
417
+ 2024-11-27,07:42:39 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.0549 (1.029) Data (t): 0.001 Batch (t): 0.905, 567.201/s LR: 0.000001 Logit Scale: 100.000 - V4
418
+ 2024-11-27,07:44:11 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.1337 (1.030) Data (t): 0.001 Batch (t): 0.920, 565.926/s LR: 0.000001 Logit Scale: 100.000 - V4
419
+ 2024-11-27,07:45:44 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 0.97907 (1.030) Data (t): 0.001 Batch (t): 0.929, 564.644/s LR: 0.000001 Logit Scale: 100.000 - V4
420
+ 2024-11-27,07:47:16 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 0.94102 (1.029) Data (t): 0.001 Batch (t): 0.924, 564.756/s LR: 0.000001 Logit Scale: 100.000 - V4
421
+ 2024-11-27,07:48:47 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.0158 (1.029) Data (t): 0.001 Batch (t): 0.906, 566.274/s LR: 0.000001 Logit Scale: 100.000 - V4
422
+ 2024-11-27,07:50:17 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 0.89690 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.019/s LR: 0.000001 Logit Scale: 100.000 - V4
423
+ 2024-11-27,07:51:48 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.1335 (1.028) Data (t): 0.001 Batch (t): 0.905, 565.623/s LR: 0.000001 Logit Scale: 100.000 - V4
424
+ 2024-11-27,07:53:23 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 0.94765 (1.028) Data (t): 0.001 Batch (t): 0.950, 567.271/s LR: 0.000001 Logit Scale: 100.000 - V4
425
+ 2024-11-27,07:54:55 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 0.93306 (1.027) Data (t): 0.001 Batch (t): 0.922, 566.493/s LR: 0.000001 Logit Scale: 100.000 - V4
426
+ 2024-11-27,07:56:25 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 1.0547 (1.027) Data (t): 0.001 Batch (t): 0.906, 563.600/s LR: 0.000001 Logit Scale: 100.000 - V4
427
+ 2024-11-27,07:57:56 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.0597 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.034/s LR: 0.000001 Logit Scale: 100.000 - V4
428
+ 2024-11-27,07:59:27 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 0.97989 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.593/s LR: 0.000001 Logit Scale: 100.000 - V4
429
+ 2024-11-27,08:01:02 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.0548 (1.027) Data (t): 0.001 Batch (t): 0.951, 566.319/s LR: 0.000001 Logit Scale: 100.000 - V4
430
+ 2024-11-27,08:02:34 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 0.94453 (1.026) Data (t): 0.001 Batch (t): 0.923, 564.214/s LR: 0.000001 Logit Scale: 100.000 - V4
431
+ 2024-11-27,08:04:04 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.0222 (1.026) Data (t): 0.001 Batch (t): 0.906, 565.438/s LR: 0.000001 Logit Scale: 100.000 - V4
432
+ 2024-11-27,08:05:35 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 0.99847 (1.026) Data (t): 0.001 Batch (t): 0.907, 564.791/s LR: 0.000001 Logit Scale: 100.000 - V4
433
+ 2024-11-27,08:07:06 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.1068 (1.027) Data (t): 0.001 Batch (t): 0.906, 566.754/s LR: 0.000001 Logit Scale: 100.000 - V4
434
+ 2024-11-27,08:08:41 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 0.95260 (1.026) Data (t): 0.001 Batch (t): 0.951, 566.922/s LR: 0.000001 Logit Scale: 100.000 - V4
435
+ 2024-11-27,08:10:12 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.0146 (1.026) Data (t): 0.001 Batch (t): 0.916, 267.261/s LR: 0.000001 Logit Scale: 100.000 - V4
436
+ 2024-11-27,08:11:44 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 0.88795 (1.025) Data (t): 0.001 Batch (t): 0.913, 566.896/s LR: 0.000001 Logit Scale: 100.000 - V4
437
+ 2024-11-27,08:13:14 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.0358 (1.025) Data (t): 0.001 Batch (t): 0.905, 567.783/s LR: 0.000001 Logit Scale: 100.000 - V4
438
+ 2024-11-27,08:14:45 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 1.0684 (1.025) Data (t): 0.001 Batch (t): 0.905, 566.366/s LR: 0.000001 Logit Scale: 100.000 - V4
439
+ 2024-11-27,08:16:18 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 0.94225 (1.024) Data (t): 0.001 Batch (t): 0.936, 568.469/s LR: 0.000001 Logit Scale: 100.000 - V4
440
+ 2024-11-27,08:17:50 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.0869 (1.025) Data (t): 0.001 Batch (t): 0.919, 566.358/s LR: 0.000001 Logit Scale: 100.000 - V4
441
+ 2024-11-27,08:19:22 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 0.93740 (1.024) Data (t): 0.001 Batch (t): 0.923, 564.485/s LR: 0.000001 Logit Scale: 100.000 - V4
442
+ 2024-11-27,08:20:53 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.0763 (1.025) Data (t): 0.001 Batch (t): 0.906, 563.449/s LR: 0.000001 Logit Scale: 100.000 - V4
443
+ 2024-11-27,08:22:24 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 0.90599 (1.024) Data (t): 0.001 Batch (t): 0.906, 562.998/s LR: 0.000001 Logit Scale: 100.000 - V4
444
+ 2024-11-27,08:23:56 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 0.99699 (1.023) Data (t): 0.001 Batch (t): 0.922, 565.232/s LR: 0.000001 Logit Scale: 100.000 - V4
445
+ 2024-11-27,08:25:30 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.1403 (1.024) Data (t): 0.001 Batch (t): 0.936, 563.738/s LR: 0.000001 Logit Scale: 100.000 - V4
446
+ 2024-11-27,08:27:02 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 0.92787 (1.024) Data (t): 0.001 Batch (t): 0.923, 565.007/s LR: 0.000001 Logit Scale: 100.000 - V4
447
+ 2024-11-27,08:28:33 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.1130 (1.024) Data (t): 0.001 Batch (t): 0.906, 564.766/s LR: 0.000001 Logit Scale: 100.000 - V4
448
+ 2024-11-27,08:30:03 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 0.94117 (1.024) Data (t): 0.001 Batch (t): 0.907, 562.227/s LR: 0.000001 Logit Scale: 100.000 - V4
449
+ 2024-11-27,08:31:34 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 0.92106 (1.023) Data (t): 0.001 Batch (t): 0.907, 567.182/s LR: 0.000000 Logit Scale: 100.000 - V4
450
+ 2024-11-27,08:33:09 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 0.89584 (1.022) Data (t): 0.001 Batch (t): 0.952, 566.305/s LR: 0.000000 Logit Scale: 100.000 - V4
451
+ 2024-11-27,08:34:41 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 0.98642 (1.022) Data (t): 0.001 Batch (t): 0.922, 564.370/s LR: 0.000000 Logit Scale: 100.000 - V4
452
+ 2024-11-27,08:36:12 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 1.0965 (1.022) Data (t): 0.001 Batch (t): 0.905, 565.747/s LR: 0.000000 Logit Scale: 100.000 - V4
453
+ 2024-11-27,08:37:42 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.1340 (1.023) Data (t): 0.001 Batch (t): 0.906, 564.926/s LR: 0.000000 Logit Scale: 100.000 - V4
454
+ 2024-11-27,08:39:13 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 1.0770 (1.023) Data (t): 0.001 Batch (t): 0.907, 563.914/s LR: 0.000000 Logit Scale: 100.000 - V4
455
+ 2024-11-27,08:40:49 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.0448 (1.024) Data (t): 0.001 Batch (t): 0.955, 561.226/s LR: 0.000000 Logit Scale: 100.000 - V4
456
+ 2024-11-27,08:42:21 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 0.86651 (1.022) Data (t): 0.001 Batch (t): 0.926, 565.842/s LR: 0.000000 Logit Scale: 100.000 - V4
457
+ 2024-11-27,08:43:52 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 0.97040 (1.022) Data (t): 0.001 Batch (t): 0.907, 563.521/s LR: 0.000000 Logit Scale: 100.000 - V4
458
+ 2024-11-27,08:45:22 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.1047 (1.023) Data (t): 0.001 Batch (t): 0.906, 566.229/s LR: 0.000000 Logit Scale: 100.000 - V4
459
+ 2024-11-27,08:46:53 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 0.97643 (1.022) Data (t): 0.001 Batch (t): 0.906, 563.671/s LR: 0.000000 Logit Scale: 100.000 - V4
460
+ 2024-11-27,08:48:28 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 0.86220 (1.021) Data (t): 0.001 Batch (t): 0.952, 567.008/s LR: 0.000000 Logit Scale: 100.000 - V4
461
+ 2024-11-27,08:49:59 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.0355 (1.021) Data (t): 0.001 Batch (t): 0.906, 565.695/s LR: 0.000000 Logit Scale: 100.000 - V4
462
+ 2024-11-27,08:51:31 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.1112 (1.022) Data (t): 0.001 Batch (t): 0.923, 562.959/s LR: 0.000000 Logit Scale: 100.000 - V4
463
+ 2024-11-27,08:53:02 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 1.0347 (1.022) Data (t): 0.001 Batch (t): 0.906, 565.479/s LR: 0.000000 Logit Scale: 100.000 - V4
464
+ 2024-11-27,08:54:33 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.0209 (1.022) Data (t): 0.001 Batch (t): 0.908, 563.315/s LR: 0.000000 Logit Scale: 100.000 - V4
465
+ 2024-11-27,08:56:06 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 0.98528 (1.022) Data (t): 0.001 Batch (t): 0.939, 311.203/s LR: 0.000000 Logit Scale: 100.000 - V4
466
+ 2024-11-27,08:57:39 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 0.90495 (1.021) Data (t): 0.001 Batch (t): 0.925, 562.615/s LR: 0.000000 Logit Scale: 100.000 - V4
467
+ 2024-11-27,08:59:11 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 1.1059 (1.021) Data (t): 0.001 Batch (t): 0.924, 564.994/s LR: 0.000000 Logit Scale: 100.000 - V4
468
+ 2024-11-27,09:00:42 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 1.0582 (1.022) Data (t): 0.001 Batch (t): 0.908, 565.052/s LR: 0.000000 Logit Scale: 100.000 - V4
469
+ 2024-11-27,09:02:13 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.0287 (1.022) Data (t): 0.001 Batch (t): 0.906, 562.875/s LR: 0.000000 Logit Scale: 100.000 - V4
470
+ 2024-11-27,09:03:45 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.0140 (1.022) Data (t): 0.001 Batch (t): 0.923, 561.382/s LR: 0.000000 Logit Scale: 100.000 - V4
471
+ 2024-11-27,09:05:19 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 0.97930 (1.021) Data (t): 0.001 Batch (t): 0.939, 563.123/s LR: 0.000000 Logit Scale: 100.000 - V4
472
+ 2024-11-27,09:06:52 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.0888 (1.022) Data (t): 0.001 Batch (t): 0.926, 564.008/s LR: 0.000000 Logit Scale: 100.000 - V4
473
+ 2024-11-27,09:08:22 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 0.97723 (1.022) Data (t): 0.001 Batch (t): 0.907, 565.380/s LR: 0.000000 Logit Scale: 100.000 - V4
474
+ 2024-11-27,09:09:53 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 1.0862 (1.022) Data (t): 0.001 Batch (t): 0.906, 565.697/s LR: 0.000000 Logit Scale: 100.000 - V4
475
+ 2024-11-27,09:11:24 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 0.95046 (1.022) Data (t): 0.001 Batch (t): 0.908, 566.509/s LR: 0.000000 Logit Scale: 100.000 - V4
476
+ 2024-11-27,09:12:59 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 1.0851 (1.022) Data (t): 0.001 Batch (t): 0.953, 565.420/s LR: 0.000000 Logit Scale: 100.000 - V4
477
+ 2024-11-27,09:14:31 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.0705 (1.022) Data (t): 0.001 Batch (t): 0.923, 564.119/s LR: 0.000000 Logit Scale: 100.000 - V4
478
+ 2024-11-27,09:16:02 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 0.91027 (1.022) Data (t): 0.001 Batch (t): 0.906, 563.653/s LR: 0.000000 Logit Scale: 100.000 - V4
479
+ 2024-11-27,09:17:33 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.0479 (1.022) Data (t): 0.001 Batch (t): 0.908, 564.853/s LR: 0.000000 Logit Scale: 100.000 - V4
480
+ 2024-11-27,09:19:03 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.0007 (1.022) Data (t): 0.001 Batch (t): 0.908, 562.832/s LR: 0.000000 Logit Scale: 100.000 - V4
481
+ 2024-11-27,09:20:38 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 0.97559 (1.021) Data (t): 0.001 Batch (t): 0.948, 564.404/s LR: 0.000000 Logit Scale: 100.000 - V4
482
+ 2024-11-27,09:22:10 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 0.92651 (1.021) Data (t): 0.001 Batch (t): 0.916, 562.082/s LR: 0.000000 Logit Scale: 100.000 - V4
483
+ 2024-11-27,09:23:41 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 0.85818 (1.020) Data (t): 0.001 Batch (t): 0.914, 566.555/s LR: 0.000000 Logit Scale: 100.000 - V4
484
+ 2024-11-27,09:25:12 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 0.86241 (1.019) Data (t): 0.001 Batch (t): 0.906, 567.106/s LR: 0.000000 Logit Scale: 100.000 - V4
485
+ 2024-11-27,09:26:42 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.1077 (1.019) Data (t): 0.001 Batch (t): 0.907, 563.186/s LR: 0.000000 Logit Scale: 100.000 - V4
486
+ 2024-11-27,09:28:17 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 0.97823 (1.019) Data (t): 0.001 Batch (t): 0.947, 566.863/s LR: 0.000000 Logit Scale: 100.000 - V4
487
+ 2024-11-27,09:29:48 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.0374 (1.019) Data (t): 0.001 Batch (t): 0.914, 565.784/s LR: 0.000000 Logit Scale: 100.000 - V4
488
+ 2024-11-27,09:31:21 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.0225 (1.019) Data (t): 0.001 Batch (t): 0.925, 563.876/s LR: 0.000000 Logit Scale: 100.000 - V4
489
+ 2024-11-27,09:32:51 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 0.97318 (1.019) Data (t): 0.001 Batch (t): 0.906, 566.196/s LR: 0.000000 Logit Scale: 100.000 - V4
490
+ 2024-11-27,09:34:22 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 1.0428 (1.019) Data (t): 0.001 Batch (t): 0.905, 566.293/s LR: 0.000000 Logit Scale: 100.000 - V4
491
+ 2024-11-27,09:35:55 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 0.96848 (1.019) Data (t): 0.001 Batch (t): 0.929, 564.782/s LR: 0.000000 Logit Scale: 100.000 - V4
492
+ 2024-11-27,09:37:28 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.1015 (1.019) Data (t): 0.001 Batch (t): 0.931, 567.245/s LR: 0.000000 Logit Scale: 100.000 - V4
493
+ 2024-11-27,09:39:00 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.0210 (1.019) Data (t): 0.001 Batch (t): 0.924, 565.455/s LR: 0.000000 Logit Scale: 100.000 - V4
494
+ 2024-11-27,09:40:31 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 0.76609 (1.018) Data (t): 0.001 Batch (t): 0.906, 566.002/s LR: 0.000000 Logit Scale: 100.000 - V4
495
+ 2024-11-27,09:42:01 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 0.91934 (1.017) Data (t): 0.001 Batch (t): 0.905, 566.385/s LR: 0.000000 Logit Scale: 100.000 - V4
496
+ 2024-11-27,09:43:33 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.0500 (1.017) Data (t): 0.001 Batch (t): 0.913, 566.963/s LR: 0.000000 Logit Scale: 100.000 - V4
497
+ 2024-11-27,09:45:07 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.0902 (1.018) Data (t): 0.001 Batch (t): 0.945, 562.549/s LR: 0.000000 Logit Scale: 100.000 - V4
498
+ 2024-11-27,09:46:39 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.1124 (1.018) Data (t): 0.001 Batch (t): 0.918, 564.213/s LR: 0.000000 Logit Scale: 100.000 - V4
499
+ 2024-11-27,09:48:10 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 0.87710 (1.017) Data (t): 0.001 Batch (t): 0.914, 567.206/s LR: 0.000000 Logit Scale: 100.000 - V4
500
+ 2024-11-27,09:49:41 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.0338 (1.018) Data (t): 0.001 Batch (t): 0.906, 565.747/s LR: 0.000000 Logit Scale: 100.000 - V4
501
+ 2024-11-27,09:51:12 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.0121 (1.017) Data (t): 0.001 Batch (t): 0.907, 564.501/s LR: 0.000000 Logit Scale: 100.000 - V4
502
+ 2024-11-27,09:52:47 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.0576 (1.018) Data (t): 0.001 Batch (t): 0.955, 565.691/s LR: 0.000000 Logit Scale: 100.000 - V4
503
+ 2024-11-27,09:54:19 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 1.0241 (1.018) Data (t): 0.001 Batch (t): 0.916, 567.483/s LR: 0.000000 Logit Scale: 100.000 - V4
504
+ 2024-11-27,09:55:50 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.0915 (1.018) Data (t): 0.001 Batch (t): 0.913, 565.679/s LR: 0.000000 Logit Scale: 100.000 - V4
505
+ 2024-11-27,09:57:21 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 1.0455 (1.018) Data (t): 0.001 Batch (t): 0.905, 564.263/s LR: 0.000000 Logit Scale: 100.000 - V4
506
+ 2024-11-27,09:58:51 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 0.98658 (1.018) Data (t): 0.001 Batch (t): 0.907, 565.168/s LR: 0.000000 Logit Scale: 100.000 - V4
507
+ 2024-11-27,10:00:27 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 0.83012 (1.017) Data (t): 0.001 Batch (t): 0.954, 565.891/s LR: 0.000000 Logit Scale: 100.000 - V4
508
+ 2024-11-27,10:01:58 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 0.91993 (1.017) Data (t): 0.001 Batch (t): 0.916, 566.650/s LR: 0.000000 Logit Scale: 100.000 - V4
509
+ 2024-11-27,10:03:30 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 0.95294 (1.016) Data (t): 0.001 Batch (t): 0.914, 566.302/s LR: 0.000000 Logit Scale: 100.000 - V4
510
+ 2024-11-27,10:05:00 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 0.99583 (1.016) Data (t): 0.001 Batch (t): 0.906, 565.011/s LR: 0.000000 Logit Scale: 100.000 - V4
511
+ 2024-11-27,10:06:31 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 0.91248 (1.016) Data (t): 0.001 Batch (t): 0.905, 565.156/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:08:06 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 0.94513 (1.015) Data (t): 0.001 Batch (t): 0.947, 302.579/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:09:37 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.0810 (1.016) Data (t): 0.001 Batch (t): 0.913, 564.455/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:11:09 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 0.95236 (1.015) Data (t): 0.001 Batch (t): 0.926, 561.952/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:12:40 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 0.94826 (1.015) Data (t): 0.001 Batch (t): 0.907, 563.711/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:14:11 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.0354 (1.015) Data (t): 0.001 Batch (t): 0.907, 566.087/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:15:44 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 0.90536 (1.014) Data (t): 0.001 Batch (t): 0.930, 562.897/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:17:17 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.0303 (1.014) Data (t): 0.001 Batch (t): 0.932, 563.609/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:18:49 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 1.0716 (1.015) Data (t): 0.001 Batch (t): 0.917, 563.220/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:20:20 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 1.0059 (1.015) Data (t): 0.001 Batch (t): 0.914, 564.446/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:21:51 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.0010 (1.015) Data (t): 0.001 Batch (t): 0.906, 567.366/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:23:22 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 0.93830 (1.014) Data (t): 0.001 Batch (t): 0.915, 566.496/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:24:56 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 0.98885 (1.014) Data (t): 0.001 Batch (t): 0.940, 563.388/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:26:28 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 0.97667 (1.014) Data (t): 0.001 Batch (t): 0.916, 567.157/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:27:59 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.0805 (1.014) Data (t): 0.001 Batch (t): 0.914, 563.164/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:29:30 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 0.84258 (1.013) Data (t): 0.001 Batch (t): 0.907, 566.468/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:31:01 | INFO | Train Epoch: 1 [10291712/10637090 (97%)] Loss: 0.95244 (1.013) Data (t): 0.001 Batch (t): 0.907, 563.463/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:32:36 | INFO | Train Epoch: 1 [10342912/10637090 (97%)] Loss: 1.0020 (1.013) Data (t): 0.001 Batch (t): 0.955, 564.334/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:34:08 | INFO | Train Epoch: 1 [10394112/10637090 (98%)] Loss: 1.0205 (1.013) Data (t): 0.001 Batch (t): 0.916, 565.652/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:35:39 | INFO | Train Epoch: 1 [10445312/10637090 (98%)] Loss: 1.0271 (1.013) Data (t): 0.001 Batch (t): 0.914, 564.856/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:37:10 | INFO | Train Epoch: 1 [10496512/10637090 (99%)] Loss: 0.99658 (1.013) Data (t): 0.001 Batch (t): 0.907, 562.422/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:38:41 | INFO | Train Epoch: 1 [10547712/10637090 (99%)] Loss: 1.0557 (1.013) Data (t): 0.001 Batch (t): 0.908, 565.340/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:40:16 | INFO | Train Epoch: 1 [10598912/10637090 (100%)] Loss: 1.1365 (1.014) Data (t): 0.001 Batch (t): 0.956, 566.695/s LR: 0.000000 Logit Scale: 100.000 - V4
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+ 2024-11-27,10:41:23 | INFO | Train Epoch: 1 [10636800/10637090 (100%)] Loss: 0.96837 (1.014) Data (t): 0.002 Batch (t): 0.907, 566.356/s LR: 0.000000 Logit Scale: 100.000 - V4
data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ batch_size: 64
2
+ beta1: 0.9
3
+ beta2: 0.98
4
+ checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints
5
+ copy_codebase: False
6
+ csv_caption_key: caption
7
+ csv_hard_captions_key: neg_caption
8
+ csv_img_key: img_path
9
+ csv_separator: ,
10
+ dataset_resampled: False
11
+ dataset_type: csv
12
+ ddp_static_graph: False
13
+ debug: False
14
+ device: cuda:0
15
+ dist_backend: nccl
16
+ dist_url: env://
17
+ distributed: True
18
+ epochs: 2
19
+ eps: 1e-06
20
+ force_quick_gelu: True
21
+ gather_with_grad: False
22
+ grad_checkpointing: False
23
+ horovod: False
24
+ imagenet_v2: None
25
+ imagenet_val: None
26
+ local_loss: False
27
+ local_rank: 0
28
+ lock_image: False
29
+ lock_image_freeze_bn_stats: False
30
+ lock_image_unlocked_groups: 0
31
+ log_level: 20
32
+ log_local: False
33
+ log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log
34
+ logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten
35
+ lr: 5e-06
36
+ model: ViT-L-14-336
37
+ name: 2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp
38
+ no_set_device_rank: False
39
+ norm_gradient_clip: None
40
+ precision: amp
41
+ pretrained: data/openclip-vit-14-336/openclip_model.pt
42
+ pretrained_image: False
43
+ rank: 0
44
+ report_to: wandb
45
+ resume: None
46
+ save_frequency: 1
47
+ save_most_recent: False
48
+ seed: 0
49
+ skip_scheduler: False
50
+ tensorboard: False
51
+ tensorboard_path:
52
+ torchscript: False
53
+ trace: False
54
+ train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv
55
+ train_num_samples: None
56
+ use_bn_sync: False
57
+ val_data: None
58
+ val_frequency: 1
59
+ val_num_samples: None
60
+ wandb: True
61
+ wandb_notes:
62
+ wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten
63
+ warmup: 0
64
+ wd: 0.1
65
+ workers: 4
66
+ world_size: 8
67
+ zeroshot_frequency: 2