diff --git "a/log.txt" "b/log.txt"
new file mode 100644--- /dev/null
+++ "b/log.txt"
@@ -0,0 +1,3215 @@
+The following values were not passed to `accelerate launch` and had defaults used instead:
+	`--num_processes` was set to a value of `4`
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+gradient_accumulation_steps:  4
+gradient_accumulation_steps:  4
+Params using prompt template alpaca:
+base_model: baichuan-inc/Baichuan2-7B-Base
+data_path: ../../data/belle_dolphine/p8.jsonl
+output_dir: ../out/lora/p8
+batch_size: 32
+micro_batch_size: 2
+num_epochs: 1
+learning_rate: 0.0004
+cutoff_len: 4096
+val_set_size: 0
+lr_scheduler: cosine
+warmup_steps: 100
+lora_r: 16
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: ['gate_proj', 'down_proj', 'up_proj']
+train_on_inputs: False
+add_eos_token: False
+group_by_length: False
+wandb_project: lora-moe
+wandb_run_name: belle_dolphine-p8
+wandb_watch: 
+wandb_log_model: 
+resume_from_checkpoint: False
+
+gradient_accumulation_steps:  4
+gradient_accumulation_steps:  4
+
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+pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None
+pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None
+pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None
+pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None
+trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183
+trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183
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Map:  98%|█████████▊| 98046/99590 [03:31<00:04, 384.38 examples/s]Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+
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+/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+wandb: Currently logged in as: k-duan (nus_keyu). Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.16.1
+wandb: Run data is saved locally in /home/aiops/duanky/lora-moe/experiments/launch_scripts/wandb/run-20231228_232541-gzzd4w5x
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run belle_dolphine-p8
+wandb: ⭐️ View project at https://wandb.ai/nus_keyu/lora-moe
+wandb: 🚀 View run at https://wandb.ai/nus_keyu/lora-moe/runs/gzzd4w5x
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+  warnings.warn(
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{'loss': 1.0969, 'learning_rate': 0.000124, 'epoch': 0.01}
+
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{'loss': 1.0162, 'learning_rate': 0.00012800000000000002, 'epoch': 0.01}
+
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{'loss': 1.033, 'learning_rate': 0.000132, 'epoch': 0.01}
+
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{'loss': 0.9363, 'learning_rate': 0.00013600000000000003, 'epoch': 0.01}
+
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{'loss': 1.0791, 'learning_rate': 0.00014, 'epoch': 0.01}
+
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{'loss': 0.9156, 'learning_rate': 0.000144, 'epoch': 0.01}
+
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{'loss': 0.9933, 'learning_rate': 0.000148, 'epoch': 0.01}
+
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{'loss': 0.9657, 'learning_rate': 0.000152, 'epoch': 0.01}
+
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{'loss': 1.0068, 'learning_rate': 0.00015600000000000002, 'epoch': 0.01}
+
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{'loss': 0.998, 'learning_rate': 0.00016, 'epoch': 0.01}
+
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{'loss': 0.9114, 'learning_rate': 0.000164, 'epoch': 0.01}
+
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{'loss': 0.9493, 'learning_rate': 0.000168, 'epoch': 0.01}
+
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{'loss': 0.9821, 'learning_rate': 0.000172, 'epoch': 0.01}
+
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{'loss': 1.1043, 'learning_rate': 0.00017600000000000002, 'epoch': 0.01}
+
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{'loss': 0.9675, 'learning_rate': 0.00018, 'epoch': 0.01}
+
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{'loss': 0.9996, 'learning_rate': 0.00018400000000000003, 'epoch': 0.01}
+
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{'loss': 0.9619, 'learning_rate': 0.000188, 'epoch': 0.02}
+
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  2%|▏         | 48/3112 [02:00<2:09:35,  2.54s/it]
                                                   
{'loss': 1.0249, 'learning_rate': 0.000192, 'epoch': 0.02}
+
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{'loss': 0.9913, 'learning_rate': 0.000196, 'epoch': 0.02}
+
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{'loss': 0.7959, 'learning_rate': 0.0002, 'epoch': 0.02}
+
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  2%|▏         | 51/3112 [02:07<2:08:26,  2.52s/it]
                                                   
{'loss': 1.0425, 'learning_rate': 0.00020400000000000003, 'epoch': 0.02}
+
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  2%|▏         | 52/3112 [02:10<2:19:00,  2.73s/it]
                                                   
{'loss': 1.0633, 'learning_rate': 0.00020800000000000001, 'epoch': 0.02}
+
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  2%|▏         | 53/3112 [02:13<2:17:12,  2.69s/it]
                                                   
{'loss': 0.9647, 'learning_rate': 0.00021200000000000003, 'epoch': 0.02}
+
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  2%|▏         | 54/3112 [02:16<2:16:57,  2.69s/it]
                                                   
{'loss': 0.9763, 'learning_rate': 0.00021600000000000002, 'epoch': 0.02}
+
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  2%|▏         | 55/3112 [02:18<2:11:26,  2.58s/it]
                                                   
{'loss': 1.0168, 'learning_rate': 0.00022000000000000003, 'epoch': 0.02}
+
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  2%|▏         | 56/3112 [02:21<2:21:31,  2.78s/it]
                                                   
{'loss': 0.9852, 'learning_rate': 0.00022400000000000002, 'epoch': 0.02}
+
  2%|▏         | 56/3112 [02:21<2:21:31,  2.78s/it]
  2%|▏         | 57/3112 [02:25<2:31:14,  2.97s/it]
                                                   
{'loss': 1.0007, 'learning_rate': 0.00022799999999999999, 'epoch': 0.02}
+
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  2%|▏         | 58/3112 [02:27<2:25:42,  2.86s/it]
                                                   
{'loss': 1.0332, 'learning_rate': 0.000232, 'epoch': 0.02}
+
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  2%|▏         | 59/3112 [02:30<2:17:20,  2.70s/it]
                                                   
{'loss': 0.9753, 'learning_rate': 0.000236, 'epoch': 0.02}
+
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{'loss': 1.0567, 'learning_rate': 0.00024, 'epoch': 0.02}
+
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  2%|▏         | 61/3112 [02:35<2:12:57,  2.61s/it]
                                                   
{'loss': 0.8954, 'learning_rate': 0.000244, 'epoch': 0.02}
+
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  2%|▏         | 62/3112 [02:37<2:11:08,  2.58s/it]
                                                   
{'loss': 1.0207, 'learning_rate': 0.000248, 'epoch': 0.02}
+
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  2%|▏         | 63/3112 [02:40<2:11:14,  2.58s/it]
                                                   
{'loss': 0.9293, 'learning_rate': 0.000252, 'epoch': 0.02}
+
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  2%|▏         | 64/3112 [02:42<2:04:46,  2.46s/it]
                                                   
{'loss': 0.9731, 'learning_rate': 0.00025600000000000004, 'epoch': 0.02}
+
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  2%|▏         | 65/3112 [02:44<2:01:32,  2.39s/it]
                                                   
{'loss': 0.9343, 'learning_rate': 0.00026000000000000003, 'epoch': 0.02}
+
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  2%|▏         | 66/3112 [02:47<2:06:08,  2.48s/it]
                                                   
{'loss': 0.9203, 'learning_rate': 0.000264, 'epoch': 0.02}
+
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  2%|▏         | 67/3112 [02:49<2:07:09,  2.51s/it]
                                                   
{'loss': 0.8022, 'learning_rate': 0.000268, 'epoch': 0.02}
+
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  2%|▏         | 68/3112 [02:52<2:04:03,  2.45s/it]
                                                   
{'loss': 1.0727, 'learning_rate': 0.00027200000000000005, 'epoch': 0.02}
+
  2%|▏         | 68/3112 [02:52<2:04:03,  2.45s/it]
  2%|▏         | 69/3112 [02:54<2:01:39,  2.40s/it]
                                                   
{'loss': 0.9156, 'learning_rate': 0.000276, 'epoch': 0.02}
+
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{'loss': 1.0, 'learning_rate': 0.00028, 'epoch': 0.02}
+
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  2%|▏         | 71/3112 [02:59<2:04:42,  2.46s/it]
                                                   
{'loss': 0.9119, 'learning_rate': 0.000284, 'epoch': 0.02}
+
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{'loss': 0.9978, 'learning_rate': 0.000288, 'epoch': 0.02}
+
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{'loss': 0.8991, 'learning_rate': 0.000292, 'epoch': 0.02}
+
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  2%|▏         | 74/3112 [03:07<2:07:24,  2.52s/it]
                                                   
{'loss': 0.9723, 'learning_rate': 0.000296, 'epoch': 0.02}
+
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  2%|▏         | 75/3112 [03:10<2:08:36,  2.54s/it]
                                                   
{'loss': 1.0487, 'learning_rate': 0.00030000000000000003, 'epoch': 0.02}
+
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{'loss': 1.022, 'learning_rate': 0.000304, 'epoch': 0.02}
+
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  2%|▏         | 77/3112 [03:15<2:07:02,  2.51s/it]
                                                   
{'loss': 1.1011, 'learning_rate': 0.000308, 'epoch': 0.02}
+
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  3%|▎         | 78/3112 [03:17<2:08:09,  2.53s/it]
                                                   
{'loss': 0.9025, 'learning_rate': 0.00031200000000000005, 'epoch': 0.03}
+
  3%|▎         | 78/3112 [03:17<2:08:09,  2.53s/it]
  3%|▎         | 79/3112 [03:20<2:12:15,  2.62s/it]
                                                   
{'loss': 0.9647, 'learning_rate': 0.00031600000000000004, 'epoch': 0.03}
+
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  3%|▎         | 80/3112 [03:22<2:06:15,  2.50s/it]
                                                   
{'loss': 1.0676, 'learning_rate': 0.00032, 'epoch': 0.03}
+
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  3%|▎         | 81/3112 [03:25<2:07:20,  2.52s/it]
                                                   
{'loss': 0.9855, 'learning_rate': 0.000324, 'epoch': 0.03}
+
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  3%|▎         | 82/3112 [03:27<2:06:54,  2.51s/it]
                                                   
{'loss': 0.9698, 'learning_rate': 0.000328, 'epoch': 0.03}
+
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{'loss': 1.0027, 'learning_rate': 0.000332, 'epoch': 0.03}
+
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  3%|▎         | 84/3112 [03:32<2:03:06,  2.44s/it]
                                                   
{'loss': 0.9025, 'learning_rate': 0.000336, 'epoch': 0.03}
+
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{'loss': 0.8639, 'learning_rate': 0.00034, 'epoch': 0.03}
+
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{'loss': 0.8467, 'learning_rate': 0.000344, 'epoch': 0.03}
+
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  3%|▎         | 87/3112 [03:40<2:07:39,  2.53s/it]
                                                   
{'loss': 0.9356, 'learning_rate': 0.000348, 'epoch': 0.03}
+
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{'loss': 0.9875, 'learning_rate': 0.00035200000000000005, 'epoch': 0.03}
+
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  3%|▎         | 89/3112 [03:45<2:08:23,  2.55s/it]
                                                   
{'loss': 0.848, 'learning_rate': 0.00035600000000000003, 'epoch': 0.03}
+
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  3%|▎         | 90/3112 [03:47<2:07:19,  2.53s/it]
                                                   
{'loss': 0.936, 'learning_rate': 0.00036, 'epoch': 0.03}
+
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  3%|▎         | 91/3112 [03:50<2:14:25,  2.67s/it]
                                                   
{'loss': 1.0434, 'learning_rate': 0.000364, 'epoch': 0.03}
+
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  3%|▎         | 92/3112 [03:53<2:07:44,  2.54s/it]
                                                   
{'loss': 0.9038, 'learning_rate': 0.00036800000000000005, 'epoch': 0.03}
+
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{'loss': 0.956, 'learning_rate': 0.00037200000000000004, 'epoch': 0.03}
+
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{'loss': 1.0062, 'learning_rate': 0.000376, 'epoch': 0.03}
+
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{'loss': 1.0327, 'learning_rate': 0.00038, 'epoch': 0.03}
+
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{'loss': 0.9512, 'learning_rate': 0.000384, 'epoch': 0.03}
+
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{'loss': 0.8612, 'learning_rate': 0.000388, 'epoch': 0.03}
+
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{'loss': 0.9073, 'learning_rate': 0.000392, 'epoch': 0.03}
+
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{'loss': 0.8613, 'learning_rate': 0.00039600000000000003, 'epoch': 0.03}
+
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{'loss': 0.9321, 'learning_rate': 0.0004, 'epoch': 0.03}
+
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{'loss': 0.9222, 'learning_rate': 0.00039999989120980106, 'epoch': 0.03}
+
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{'loss': 0.9403, 'learning_rate': 0.00039999956483932256, 'epoch': 0.03}
+
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{'loss': 0.8481, 'learning_rate': 0.0003999990208889196, 'epoch': 0.03}
+
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{'loss': 0.9945, 'learning_rate': 0.0003999982593591839, 'epoch': 0.03}
+
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{'loss': 1.025, 'learning_rate': 0.000399997280250944, 'epoch': 0.03}
+
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{'loss': 0.95, 'learning_rate': 0.000399996083565265, 'epoch': 0.03}
+
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{'loss': 0.9418, 'learning_rate': 0.0003999946693034488, 'epoch': 0.03}
+
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{'loss': 0.9057, 'learning_rate': 0.000399993037467034, 'epoch': 0.03}
+
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{'loss': 1.0409, 'learning_rate': 0.00039999118805779583, 'epoch': 0.04}
+
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{'loss': 0.9333, 'learning_rate': 0.0003999891210777463, 'epoch': 0.04}
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{'loss': 0.9392, 'learning_rate': 0.00039998683652913406, 'epoch': 0.04}
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{'loss': 0.9495, 'learning_rate': 0.0003999843344144445, 'epoch': 0.04}
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{'loss': 0.8723, 'learning_rate': 0.00039998161473639974, 'epoch': 0.04}
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{'loss': 0.8309, 'learning_rate': 0.0003999786774979584, 'epoch': 0.04}
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{'loss': 0.919, 'learning_rate': 0.00039997552270231595, 'epoch': 0.04}
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{'loss': 0.8769, 'learning_rate': 0.00039997215035290456, 'epoch': 0.04}
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{'loss': 0.9288, 'learning_rate': 0.0003999685604533929, 'epoch': 0.04}
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{'loss': 0.8767, 'learning_rate': 0.00039996475300768647, 'epoch': 0.04}
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{'loss': 0.7836, 'learning_rate': 0.0003999607280199275, 'epoch': 0.04}
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{'loss': 0.9204, 'learning_rate': 0.0003999564854944946, 'epoch': 0.04}
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{'loss': 0.9527, 'learning_rate': 0.0003999520254360034, 'epoch': 0.04}
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{'loss': 0.9527, 'learning_rate': 0.00039994734784930594, 'epoch': 0.04}
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{'loss': 0.8504, 'learning_rate': 0.00039994245273949094, 'epoch': 0.04}
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{'loss': 0.945, 'learning_rate': 0.0003999373401118838, 'epoch': 0.04}
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{'loss': 1.025, 'learning_rate': 0.00039993200997204666, 'epoch': 0.04}
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{'loss': 0.9359, 'learning_rate': 0.0003999264623257781, 'epoch': 0.04}
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{'loss': 1.0323, 'learning_rate': 0.0003999206971791134, 'epoch': 0.04}
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{'loss': 0.9431, 'learning_rate': 0.00039991471453832447, 'epoch': 0.04}
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{'loss': 0.8771, 'learning_rate': 0.00039990851440991986, 'epoch': 0.04}
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{'loss': 0.8938, 'learning_rate': 0.00039990209680064475, 'epoch': 0.04}
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{'loss': 0.8502, 'learning_rate': 0.00039989546171748086, 'epoch': 0.04}
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{'loss': 0.8991, 'learning_rate': 0.00039988860916764644, 'epoch': 0.04}
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{'loss': 0.8344, 'learning_rate': 0.00039988153915859645, 'epoch': 0.04}
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{'loss': 0.9809, 'learning_rate': 0.00039987425169802233, 'epoch': 0.04}
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{'loss': 0.9492, 'learning_rate': 0.0003998667467938522, 'epoch': 0.04}
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{'loss': 0.9401, 'learning_rate': 0.00039985902445425055, 'epoch': 0.04}
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{'loss': 0.9753, 'learning_rate': 0.00039985108468761863, 'epoch': 0.04}
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{'loss': 0.9621, 'learning_rate': 0.00039984292750259407, 'epoch': 0.04}
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{'loss': 0.9769, 'learning_rate': 0.00039983455290805106, 'epoch': 0.04}
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{'loss': 0.9319, 'learning_rate': 0.0003998259609131004, 'epoch': 0.04}
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{'loss': 0.82, 'learning_rate': 0.0003998171515270893, 'epoch': 0.05}
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{'loss': 0.9276, 'learning_rate': 0.0003998081247596016, 'epoch': 0.05}
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{'loss': 0.8548, 'learning_rate': 0.0003997988806204574, 'epoch': 0.05}
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{'loss': 0.8974, 'learning_rate': 0.00039978941911971344, 'epoch': 0.05}
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{'loss': 0.9384, 'learning_rate': 0.00039977974026766304, 'epoch': 0.05}
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{'loss': 0.9008, 'learning_rate': 0.00039976984407483565, 'epoch': 0.05}
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{'loss': 0.8802, 'learning_rate': 0.0003997597305519975, 'epoch': 0.05}
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{'loss': 0.8109, 'learning_rate': 0.00039974939971015107, 'epoch': 0.05}
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{'loss': 0.9472, 'learning_rate': 0.0003997388515605353, 'epoch': 0.05}
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{'loss': 0.8003, 'learning_rate': 0.0003997280861146256, 'epoch': 0.05}
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{'loss': 0.8599, 'learning_rate': 0.00039971710338413357, 'epoch': 0.05}
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{'loss': 0.9683, 'learning_rate': 0.00039970590338100746, 'epoch': 0.05}
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{'loss': 0.9864, 'learning_rate': 0.00039969448611743176, 'epoch': 0.05}
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{'loss': 0.9497, 'learning_rate': 0.00039968285160582736, 'epoch': 0.05}
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{'loss': 0.9325, 'learning_rate': 0.0003996709998588514, 'epoch': 0.05}
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{'loss': 0.9167, 'learning_rate': 0.00039965893088939747, 'epoch': 0.05}
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{'loss': 0.9643, 'learning_rate': 0.00039964664471059543, 'epoch': 0.05}
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{'loss': 0.9718, 'learning_rate': 0.00039963414133581133, 'epoch': 0.05}
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{'loss': 0.8662, 'learning_rate': 0.00039962142077864783, 'epoch': 0.05}
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{'loss': 0.9711, 'learning_rate': 0.00039960848305294343, 'epoch': 0.05}
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{'loss': 1.0001, 'learning_rate': 0.00039959532817277323, 'epoch': 0.05}
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{'loss': 0.8947, 'learning_rate': 0.0003995819561524485, 'epoch': 0.05}
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{'loss': 0.8309, 'learning_rate': 0.0003995683670065165, 'epoch': 0.05}
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{'loss': 0.852, 'learning_rate': 0.00039955456074976107, 'epoch': 0.05}
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{'loss': 0.9198, 'learning_rate': 0.000399540537397202, 'epoch': 0.05}
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{'loss': 0.7994, 'learning_rate': 0.0003995262969640952, 'epoch': 0.05}
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{'loss': 0.9199, 'learning_rate': 0.0003995118394659331, 'epoch': 0.05}
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{'loss': 0.9513, 'learning_rate': 0.0003994971649184438, 'epoch': 0.05}
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{'loss': 0.8567, 'learning_rate': 0.00039948227333759203, 'epoch': 0.05}
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{'loss': 0.8006, 'learning_rate': 0.0003994671647395782, 'epoch': 0.05}
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{'loss': 0.8287, 'learning_rate': 0.00039945183914083895, 'epoch': 0.05}
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{'loss': 0.8466, 'learning_rate': 0.0003994362965580472, 'epoch': 0.06}
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{'loss': 0.8329, 'learning_rate': 0.0003994205370081116, 'epoch': 0.06}
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{'loss': 1.0046, 'learning_rate': 0.00039940456050817705, 'epoch': 0.06}
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{'loss': 0.8684, 'learning_rate': 0.0003993883670756244, 'epoch': 0.06}
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{'loss': 0.8472, 'learning_rate': 0.00039937195672807055, 'epoch': 0.06}
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{'loss': 0.9505, 'learning_rate': 0.00039935532948336827, 'epoch': 0.06}
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{'loss': 0.9022, 'learning_rate': 0.0003993384853596065, 'epoch': 0.06}
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{'loss': 0.7937, 'learning_rate': 0.00039932142437510993, 'epoch': 0.06}
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{'loss': 0.9626, 'learning_rate': 0.00039930414654843925, 'epoch': 0.06}
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{'loss': 0.8674, 'learning_rate': 0.00039928665189839095, 'epoch': 0.06}
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{'loss': 0.8539, 'learning_rate': 0.0003992689404439977, 'epoch': 0.06}
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{'loss': 0.8264, 'learning_rate': 0.00039925101220452763, 'epoch': 0.06}
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{'loss': 0.9836, 'learning_rate': 0.000399232867199485, 'epoch': 0.06}
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{'loss': 0.9411, 'learning_rate': 0.00039921450544860973, 'epoch': 0.06}
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{'loss': 0.8775, 'learning_rate': 0.0003991959269718777, 'epoch': 0.06}
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{'loss': 0.9446, 'learning_rate': 0.0003991771317895004, 'epoch': 0.06}
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{'loss': 0.9181, 'learning_rate': 0.00039915811992192514, 'epoch': 0.06}
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{'loss': 0.8666, 'learning_rate': 0.00039913889138983505, 'epoch': 0.06}
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{'loss': 0.9672, 'learning_rate': 0.0003991194462141488, 'epoch': 0.06}
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{'loss': 0.9331, 'learning_rate': 0.00039909978441602094, 'epoch': 0.06}
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{'loss': 0.9452, 'learning_rate': 0.00039907990601684146, 'epoch': 0.06}
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{'loss': 0.9052, 'learning_rate': 0.00039905981103823614, 'epoch': 0.06}
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{'loss': 0.9412, 'learning_rate': 0.0003990394995020664, 'epoch': 0.06}
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{'loss': 0.8547, 'learning_rate': 0.00039901897143042916, 'epoch': 0.06}
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{'loss': 1.0161, 'learning_rate': 0.00039899822684565697, 'epoch': 0.06}
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{'loss': 0.9392, 'learning_rate': 0.0003989772657703179, 'epoch': 0.06}
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{'loss': 0.8965, 'learning_rate': 0.0003989560882272155, 'epoch': 0.06}
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{'loss': 1.0206, 'learning_rate': 0.00039893469423938894, 'epoch': 0.06}
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{'loss': 0.9311, 'learning_rate': 0.00039891308383011275, 'epoch': 0.06}
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{'loss': 0.8172, 'learning_rate': 0.0003988912570228969, 'epoch': 0.06}
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{'loss': 0.859, 'learning_rate': 0.0003988692138414869, 'epoch': 0.06}
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{'loss': 1.0104, 'learning_rate': 0.0003988469543098634, 'epoch': 0.07}
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{'loss': 1.0001, 'learning_rate': 0.0003988244784522428, 'epoch': 0.07}
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{'loss': 0.927, 'learning_rate': 0.00039880178629307655, 'epoch': 0.07}
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{'loss': 0.9264, 'learning_rate': 0.00039877887785705145, 'epoch': 0.07}
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{'loss': 0.932, 'learning_rate': 0.00039875575316908964, 'epoch': 0.07}
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{'loss': 0.8801, 'learning_rate': 0.0003987324122543486, 'epoch': 0.07}
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{'loss': 0.9196, 'learning_rate': 0.0003987088551382209, 'epoch': 0.07}
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{'loss': 0.963, 'learning_rate': 0.0003986850818463343, 'epoch': 0.07}
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{'loss': 0.9475, 'learning_rate': 0.0003986610924045519, 'epoch': 0.07}
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{'loss': 0.8879, 'learning_rate': 0.0003986368868389719, 'epoch': 0.07}
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{'loss': 0.9186, 'learning_rate': 0.00039861246517592747, 'epoch': 0.07}
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{'loss': 0.8936, 'learning_rate': 0.00039858782744198705, 'epoch': 0.07}
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{'loss': 0.8012, 'learning_rate': 0.0003985629736639541, 'epoch': 0.07}
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{'loss': 0.793, 'learning_rate': 0.00039853790386886704, 'epoch': 0.07}
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{'loss': 0.8714, 'learning_rate': 0.0003985126180839994, 'epoch': 0.07}
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{'loss': 0.9624, 'learning_rate': 0.00039848711633685963, 'epoch': 0.07}
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{'loss': 0.829, 'learning_rate': 0.00039846139865519107, 'epoch': 0.07}
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{'loss': 0.9879, 'learning_rate': 0.00039843546506697206, 'epoch': 0.07}
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{'loss': 0.7905, 'learning_rate': 0.0003984093156004159, 'epoch': 0.07}
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{'loss': 0.784, 'learning_rate': 0.0003983829502839705, 'epoch': 0.07}
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{'loss': 0.9276, 'learning_rate': 0.00039835636914631887, 'epoch': 0.07}
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{'loss': 0.9115, 'learning_rate': 0.00039832957221637856, 'epoch': 0.07}
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{'loss': 0.8191, 'learning_rate': 0.0003983025595233021, 'epoch': 0.07}
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{'loss': 0.7928, 'learning_rate': 0.0003982753310964766, 'epoch': 0.07}
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{'loss': 0.9718, 'learning_rate': 0.00039824788696552393, 'epoch': 0.07}
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{'loss': 0.8687, 'learning_rate': 0.0003982202271603006, 'epoch': 0.07}
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{'loss': 0.848, 'learning_rate': 0.0003981923517108979, 'epoch': 0.07}
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{'loss': 0.9673, 'learning_rate': 0.0003981642606476414, 'epoch': 0.07}
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{'loss': 0.9139, 'learning_rate': 0.00039813595400109154, 'epoch': 0.07}
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{'loss': 0.8629, 'learning_rate': 0.0003981074318020431, 'epoch': 0.07}
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{'loss': 0.8691, 'learning_rate': 0.00039807869408152544, 'epoch': 0.07}
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{'loss': 1.0033, 'learning_rate': 0.00039804974087080246, 'epoch': 0.08}
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{'loss': 0.8981, 'learning_rate': 0.0003980205722013724, 'epoch': 0.08}
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{'loss': 0.9327, 'learning_rate': 0.00039799118810496785, 'epoch': 0.08}
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{'loss': 0.8811, 'learning_rate': 0.0003979615886135559, 'epoch': 0.08}
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{'loss': 0.9356, 'learning_rate': 0.0003979317737593378, 'epoch': 0.08}
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{'loss': 0.9039, 'learning_rate': 0.00039790174357474934, 'epoch': 0.08}
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{'loss': 0.9237, 'learning_rate': 0.00039787149809246023, 'epoch': 0.08}
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{'loss': 0.8911, 'learning_rate': 0.00039784103734537475, 'epoch': 0.08}
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{'loss': 0.9065, 'learning_rate': 0.0003978103613666311, 'epoch': 0.08}
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{'loss': 0.8854, 'learning_rate': 0.0003977794701896018, 'epoch': 0.08}
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{'loss': 0.8741, 'learning_rate': 0.0003977483638478934, 'epoch': 0.08}
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{'loss': 0.9292, 'learning_rate': 0.00039771704237534657, 'epoch': 0.08}
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{'loss': 0.9336, 'learning_rate': 0.000397685505806036, 'epoch': 0.08}
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{'loss': 0.8789, 'learning_rate': 0.00039765375417427027, 'epoch': 0.08}
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{'loss': 0.911, 'learning_rate': 0.0003976217875145923, 'epoch': 0.08}
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{'loss': 0.9081, 'learning_rate': 0.00039758960586177845, 'epoch': 0.08}
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{'loss': 0.8847, 'learning_rate': 0.0003975572092508393, 'epoch': 0.08}
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{'loss': 0.845, 'learning_rate': 0.0003975245977170191, 'epoch': 0.08}
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{'loss': 0.9535, 'learning_rate': 0.0003974917712957962, 'epoch': 0.08}
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{'loss': 0.8964, 'learning_rate': 0.00039745873002288237, 'epoch': 0.08}
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{'loss': 0.8121, 'learning_rate': 0.00039742547393422327, 'epoch': 0.08}
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{'loss': 1.0468, 'learning_rate': 0.00039739200306599835, 'epoch': 0.08}
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{'loss': 0.947, 'learning_rate': 0.0003973583174546206, 'epoch': 0.08}
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{'loss': 0.9131, 'learning_rate': 0.0003973244171367366, 'epoch': 0.08}
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{'loss': 0.9067, 'learning_rate': 0.00039729030214922666, 'epoch': 0.08}
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{'loss': 0.9515, 'learning_rate': 0.00039725597252920447, 'epoch': 0.08}
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{'loss': 0.8074, 'learning_rate': 0.00039722142831401735, 'epoch': 0.08}
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{'loss': 1.0034, 'learning_rate': 0.000397186669541246, 'epoch': 0.08}
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{'loss': 0.8533, 'learning_rate': 0.00039715169624870457, 'epoch': 0.08}
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{'loss': 0.8817, 'learning_rate': 0.00039711650847444054, 'epoch': 0.08}
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{'loss': 0.8399, 'learning_rate': 0.00039708110625673483, 'epoch': 0.08}
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{'loss': 0.9647, 'learning_rate': 0.0003970454896341015, 'epoch': 0.09}
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{'loss': 0.8725, 'learning_rate': 0.00039700965864528803, 'epoch': 0.09}
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{'loss': 0.8182, 'learning_rate': 0.0003969736133292749, 'epoch': 0.09}
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{'loss': 0.9308, 'learning_rate': 0.00039693735372527605, 'epoch': 0.09}
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{'loss': 0.9354, 'learning_rate': 0.0003969008798727382, 'epoch': 0.09}
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{'loss': 0.9201, 'learning_rate': 0.00039686419181134147, 'epoch': 0.09}
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{'loss': 0.9911, 'learning_rate': 0.00039682728958099876, 'epoch': 0.09}
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{'loss': 1.007, 'learning_rate': 0.0003967901732218562, 'epoch': 0.09}
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{'loss': 0.7949, 'learning_rate': 0.00039675284277429257, 'epoch': 0.09}
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{'loss': 0.9236, 'learning_rate': 0.00039671529827891997, 'epoch': 0.09}
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{'loss': 0.9022, 'learning_rate': 0.0003966775397765829, 'epoch': 0.09}
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{'loss': 0.8306, 'learning_rate': 0.0003966395673083591, 'epoch': 0.09}
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{'loss': 0.8714, 'learning_rate': 0.0003966013809155588, 'epoch': 0.09}
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{'loss': 0.8972, 'learning_rate': 0.0003965629806397251, 'epoch': 0.09}
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{'loss': 0.8911, 'learning_rate': 0.00039652436652263374, 'epoch': 0.09}
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{'loss': 0.7929, 'learning_rate': 0.000396485538606293, 'epoch': 0.09}
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{'loss': 0.8207, 'learning_rate': 0.00039644649693294386, 'epoch': 0.09}
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{'loss': 0.7907, 'learning_rate': 0.00039640724154506, 'epoch': 0.09}
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{'loss': 0.9995, 'learning_rate': 0.00039636777248534724, 'epoch': 0.09}
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{'loss': 0.8193, 'learning_rate': 0.00039632808979674414, 'epoch': 0.09}
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{'loss': 0.8342, 'learning_rate': 0.00039628819352242157, 'epoch': 0.09}
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{'loss': 0.9908, 'learning_rate': 0.00039624808370578273, 'epoch': 0.09}
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{'loss': 0.9465, 'learning_rate': 0.0003962077603904632, 'epoch': 0.09}
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{'loss': 0.9834, 'learning_rate': 0.00039616722362033086, 'epoch': 0.09}
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{'loss': 0.8794, 'learning_rate': 0.0003961264734394856, 'epoch': 0.09}
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  9%|▉         | 290/3112 [12:05<2:00:44,  2.57s/it]
                                                    
{'loss': 0.9583, 'learning_rate': 0.0003960855098922597, 'epoch': 0.09}
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{'loss': 0.8139, 'learning_rate': 0.0003960443330232176, 'epoch': 0.09}
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{'loss': 0.9616, 'learning_rate': 0.00039600294287715543, 'epoch': 0.09}
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{'loss': 0.7973, 'learning_rate': 0.00039596133949910186, 'epoch': 0.09}
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{'loss': 0.8786, 'learning_rate': 0.00039591952293431716, 'epoch': 0.09}
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{'loss': 0.9212, 'learning_rate': 0.00039587749322829365, 'epoch': 0.09}
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{'loss': 0.9779, 'learning_rate': 0.00039583525042675554, 'epoch': 0.1}
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{'loss': 0.942, 'learning_rate': 0.00039579279457565897, 'epoch': 0.1}
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{'loss': 0.8292, 'learning_rate': 0.00039575012572119165, 'epoch': 0.1}
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{'loss': 0.9525, 'learning_rate': 0.0003957072439097731, 'epoch': 0.1}
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{'loss': 0.897, 'learning_rate': 0.00039566414918805454, 'epoch': 0.1}
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{'loss': 0.8055, 'learning_rate': 0.00039562084160291877, 'epoch': 0.1}
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{'loss': 0.8881, 'learning_rate': 0.0003955773212014803, 'epoch': 0.1}
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{'loss': 0.9634, 'learning_rate': 0.000395533588031085, 'epoch': 0.1}
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{'loss': 0.9648, 'learning_rate': 0.00039548964213931027, 'epoch': 0.1}
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{'loss': 0.8019, 'learning_rate': 0.0003954454835739649, 'epoch': 0.1}
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{'loss': 0.9155, 'learning_rate': 0.0003954011123830891, 'epoch': 0.1}
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{'loss': 0.9295, 'learning_rate': 0.00039535652861495446, 'epoch': 0.1}
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{'loss': 0.9069, 'learning_rate': 0.00039531173231806364, 'epoch': 0.1}
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{'loss': 0.9526, 'learning_rate': 0.0003952667235411507, 'epoch': 0.1}
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{'loss': 0.855, 'learning_rate': 0.0003952215023331807, 'epoch': 0.1}
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{'loss': 0.7897, 'learning_rate': 0.00039517606874335, 'epoch': 0.1}
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{'loss': 0.9163, 'learning_rate': 0.00039513042282108575, 'epoch': 0.1}
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{'loss': 0.8563, 'learning_rate': 0.00039508456461604633, 'epoch': 0.1}
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{'loss': 0.983, 'learning_rate': 0.00039503849417812096, 'epoch': 0.1}
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{'loss': 0.8935, 'learning_rate': 0.0003949922115574297, 'epoch': 0.1}
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{'loss': 0.9759, 'learning_rate': 0.00039494571680432364, 'epoch': 0.1}
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{'loss': 0.908, 'learning_rate': 0.00039489900996938436, 'epoch': 0.1}
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{'loss': 0.822, 'learning_rate': 0.0003948520911034244, 'epoch': 0.1}
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{'loss': 0.8766, 'learning_rate': 0.00039480496025748687, 'epoch': 0.1}
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{'loss': 0.9377, 'learning_rate': 0.0003947576174828456, 'epoch': 0.1}
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{'loss': 0.8959, 'learning_rate': 0.00039471006283100466, 'epoch': 0.1}
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{'loss': 0.7954, 'learning_rate': 0.0003946622963536991, 'epoch': 0.1}
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{'loss': 0.9065, 'learning_rate': 0.00039461431810289407, 'epoch': 0.1}
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{'loss': 0.962, 'learning_rate': 0.0003945661281307852, 'epoch': 0.1}
+
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{'loss': 0.8344, 'learning_rate': 0.0003945177264897984, 'epoch': 0.1}
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{'loss': 0.8187, 'learning_rate': 0.00039446911323259, 'epoch': 0.1}
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{'loss': 1.0666, 'learning_rate': 0.0003944202884120464, 'epoch': 0.11}
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{'loss': 0.8967, 'learning_rate': 0.00039437125208128425, 'epoch': 0.11}
+
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{'loss': 0.8107, 'learning_rate': 0.0003943220042936503, 'epoch': 0.11}
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{'loss': 0.8605, 'learning_rate': 0.00039427254510272124, 'epoch': 0.11}
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{'loss': 0.92, 'learning_rate': 0.00039422287456230393, 'epoch': 0.11}
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{'loss': 0.8836, 'learning_rate': 0.0003941729927264349, 'epoch': 0.11}
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{'loss': 0.9853, 'learning_rate': 0.0003941228996493808, 'epoch': 0.11}
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{'loss': 0.9218, 'learning_rate': 0.000394072595385638, 'epoch': 0.11}
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{'loss': 0.8688, 'learning_rate': 0.0003940220799899326, 'epoch': 0.11}
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{'loss': 0.8721, 'learning_rate': 0.0003939713535172203, 'epoch': 0.11}
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{'loss': 0.8698, 'learning_rate': 0.0003939204160226867, 'epoch': 0.11}
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{'loss': 0.8577, 'learning_rate': 0.0003938692675617466, 'epoch': 0.11}
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{'loss': 0.9867, 'learning_rate': 0.00039381790819004473, 'epoch': 0.11}
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+  warnings.warn(
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{'loss': 0.8154, 'learning_rate': 0.00012820957435659172, 'epoch': 0.63}
+
 63%|██████▎   | 1959/3112 [1:21:24<47:05,  2.45s/it]
 63%|██████▎   | 1960/3112 [1:21:27<47:47,  2.49s/it]
                                                     
{'loss': 0.8007, 'learning_rate': 0.0001280149106360308, 'epoch': 0.63}
+
 63%|██████▎   | 1960/3112 [1:21:27<47:47,  2.49s/it]
 63%|██████▎   | 1961/3112 [1:21:29<46:53,  2.44s/it]
                                                     
{'loss': 0.8978, 'learning_rate': 0.0001278203252281918, 'epoch': 0.63}
+
 63%|██████▎   | 1961/3112 [1:21:29<46:53,  2.44s/it]
 63%|██████▎   | 1962/3112 [1:21:32<50:14,  2.62s/it]
                                                     
{'loss': 0.7552, 'learning_rate': 0.00012762581834476456, 'epoch': 0.63}
+
 63%|██████▎   | 1962/3112 [1:21:32<50:14,  2.62s/it]
 63%|██████▎   | 1963/3112 [1:21:35<48:33,  2.54s/it]
                                                     
{'loss': 0.8178, 'learning_rate': 0.00012743139019735352, 'epoch': 0.63}
+
 63%|██████▎   | 1963/3112 [1:21:35<48:33,  2.54s/it]
 63%|██████▎   | 1964/3112 [1:21:37<47:26,  2.48s/it]
                                                     
{'loss': 0.7282, 'learning_rate': 0.00012723704099747748, 'epoch': 0.63}
+
 63%|██████▎   | 1964/3112 [1:21:37<47:26,  2.48s/it]
 63%|██████▎   | 1965/3112 [1:21:39<47:26,  2.48s/it]
                                                     
{'loss': 0.724, 'learning_rate': 0.00012704277095656932, 'epoch': 0.63}
+
 63%|██████▎   | 1965/3112 [1:21:39<47:26,  2.48s/it]
 63%|██████▎   | 1966/3112 [1:21:42<49:27,  2.59s/it]
                                                     
{'loss': 0.734, 'learning_rate': 0.0001268485802859757, 'epoch': 0.63}
+
 63%|██████▎   | 1966/3112 [1:21:42<49:27,  2.59s/it]
 63%|██████▎   | 1967/3112 [1:21:45<50:42,  2.66s/it]
                                                     
{'loss': 0.7849, 'learning_rate': 0.00012665446919695722, 'epoch': 0.63}
+
 63%|██████▎   | 1967/3112 [1:21:45<50:42,  2.66s/it]
 63%|██████▎   | 1968/3112 [1:21:48<51:58,  2.73s/it]
                                                     
{'loss': 0.8954, 'learning_rate': 0.00012646043790068756, 'epoch': 0.63}
+
 63%|██████▎   | 1968/3112 [1:21:48<51:58,  2.73s/it]
 63%|██████▎   | 1969/3112 [1:21:50<47:40,  2.50s/it]
                                                     
{'loss': 0.6898, 'learning_rate': 0.00012626648660825384, 'epoch': 0.63}
+
 63%|██████▎   | 1969/3112 [1:21:50<47:40,  2.50s/it]
 63%|██████▎   | 1970/3112 [1:21:52<47:33,  2.50s/it]
                                                     
{'loss': 0.7147, 'learning_rate': 0.00012607261553065598, 'epoch': 0.63}
+
 63%|██████▎   | 1970/3112 [1:21:52<47:33,  2.50s/it]
 63%|██████▎   | 1971/3112 [1:21:55<47:59,  2.52s/it]
                                                     
{'loss': 0.7674, 'learning_rate': 0.0001258788248788068, 'epoch': 0.63}
+
 63%|██████▎   | 1971/3112 [1:21:55<47:59,  2.52s/it]
 63%|██████▎   | 1972/3112 [1:21:58<49:22,  2.60s/it]
                                                     
{'loss': 0.8382, 'learning_rate': 0.00012568511486353153, 'epoch': 0.63}
+
 63%|██████▎   | 1972/3112 [1:21:58<49:22,  2.60s/it]
 63%|██████▎   | 1973/3112 [1:22:00<48:56,  2.58s/it]
                                                     
{'loss': 0.7625, 'learning_rate': 0.00012549148569556757, 'epoch': 0.63}
+
 63%|██████▎   | 1973/3112 [1:22:00<48:56,  2.58s/it]
 63%|██████▎   | 1974/3112 [1:22:03<48:42,  2.57s/it]
                                                     
{'loss': 0.7775, 'learning_rate': 0.0001252979375855646, 'epoch': 0.63}
+
 63%|██████▎   | 1974/3112 [1:22:03<48:42,  2.57s/it]
 63%|██████▎   | 1975/3112 [1:22:05<48:50,  2.58s/it]
                                                     
{'loss': 0.7342, 'learning_rate': 0.00012510447074408395, 'epoch': 0.63}
+
 63%|██████▎   | 1975/3112 [1:22:05<48:50,  2.58s/it]
 63%|██████▎   | 1976/3112 [1:22:07<45:54,  2.42s/it]
                                                     
{'loss': 0.7572, 'learning_rate': 0.00012491108538159858, 'epoch': 0.63}
+
 63%|██████▎   | 1976/3112 [1:22:07<45:54,  2.42s/it]
 64%|██████▎   | 1977/3112 [1:22:10<48:23,  2.56s/it]
                                                     
{'loss': 0.7195, 'learning_rate': 0.00012471778170849274, 'epoch': 0.64}
+
 64%|██████▎   | 1977/3112 [1:22:10<48:23,  2.56s/it]
 64%|██████▎   | 1978/3112 [1:22:13<46:52,  2.48s/it]
                                                     
{'loss': 0.8503, 'learning_rate': 0.00012452455993506204, 'epoch': 0.64}
+
 64%|██████▎   | 1978/3112 [1:22:13<46:52,  2.48s/it]
 64%|██████▎   | 1979/3112 [1:22:15<46:13,  2.45s/it]
                                                     
{'loss': 0.7265, 'learning_rate': 0.00012433142027151273, 'epoch': 0.64}
+
 64%|██████▎   | 1979/3112 [1:22:15<46:13,  2.45s/it]
 64%|██████▎   | 1980/3112 [1:22:18<46:59,  2.49s/it]
                                                     
{'loss': 0.7178, 'learning_rate': 0.0001241383629279618, 'epoch': 0.64}
+
 64%|██████▎   | 1980/3112 [1:22:18<46:59,  2.49s/it]
 64%|██████▎   | 1981/3112 [1:22:20<47:06,  2.50s/it]
                                                     
{'loss': 0.7758, 'learning_rate': 0.0001239453881144368, 'epoch': 0.64}
+
 64%|██████▎   | 1981/3112 [1:22:20<47:06,  2.50s/it]
 64%|██████▎   | 1982/3112 [1:22:23<48:51,  2.59s/it]
                                                     
{'loss': 0.7486, 'learning_rate': 0.0001237524960408754, 'epoch': 0.64}
+
 64%|██████▎   | 1982/3112 [1:22:23<48:51,  2.59s/it]
 64%|██████▎   | 1983/3112 [1:22:25<46:11,  2.45s/it]
                                                     
{'loss': 0.6596, 'learning_rate': 0.00012355968691712523, 'epoch': 0.64}
+
 64%|██████▎   | 1983/3112 [1:22:25<46:11,  2.45s/it]
 64%|██████▍   | 1984/3112 [1:22:27<45:41,  2.43s/it]
                                                     
{'loss': 0.8075, 'learning_rate': 0.00012336696095294368, 'epoch': 0.64}
+
 64%|██████▍   | 1984/3112 [1:22:27<45:41,  2.43s/it]
 64%|██████▍   | 1985/3112 [1:22:30<45:28,  2.42s/it]
                                                     
{'loss': 0.7528, 'learning_rate': 0.00012317431835799785, 'epoch': 0.64}
+
 64%|██████▍   | 1985/3112 [1:22:30<45:28,  2.42s/it]
 64%|██████▍   | 1986/3112 [1:22:33<48:13,  2.57s/it]
                                                     
{'loss': 0.7661, 'learning_rate': 0.0001229817593418639, 'epoch': 0.64}
+
 64%|██████▍   | 1986/3112 [1:22:33<48:13,  2.57s/it]
 64%|██████▍   | 1987/3112 [1:22:35<46:08,  2.46s/it]
                                                     
{'loss': 0.7071, 'learning_rate': 0.0001227892841140272, 'epoch': 0.64}
+
 64%|██████▍   | 1987/3112 [1:22:35<46:08,  2.46s/it]
 64%|██████▍   | 1988/3112 [1:22:37<45:12,  2.41s/it]
                                                     
{'loss': 0.6794, 'learning_rate': 0.00012259689288388184, 'epoch': 0.64}
+
 64%|██████▍   | 1988/3112 [1:22:37<45:12,  2.41s/it]
 64%|██████▍   | 1989/3112 [1:22:40<46:32,  2.49s/it]
                                                     
{'loss': 0.8315, 'learning_rate': 0.00012240458586073078, 'epoch': 0.64}
+
 64%|██████▍   | 1989/3112 [1:22:40<46:32,  2.49s/it]
 64%|██████▍   | 1990/3112 [1:22:42<44:56,  2.40s/it]
                                                     
{'loss': 0.8007, 'learning_rate': 0.00012221236325378514, 'epoch': 0.64}
+
 64%|██████▍   | 1990/3112 [1:22:42<44:56,  2.40s/it]
 64%|██████▍   | 1991/3112 [1:22:44<44:11,  2.37s/it]
                                                     
{'loss': 0.8108, 'learning_rate': 0.00012202022527216419, 'epoch': 0.64}
+
 64%|██████▍   | 1991/3112 [1:22:44<44:11,  2.37s/it]
 64%|██████▍   | 1992/3112 [1:22:47<45:10,  2.42s/it]
                                                     
{'loss': 0.7695, 'learning_rate': 0.00012182817212489536, 'epoch': 0.64}
+
 64%|██████▍   | 1992/3112 [1:22:47<45:10,  2.42s/it]
 64%|██████▍   | 1993/3112 [1:22:50<46:24,  2.49s/it]
                                                     
{'loss': 0.7752, 'learning_rate': 0.00012163620402091362, 'epoch': 0.64}
+
 64%|██████▍   | 1993/3112 [1:22:50<46:24,  2.49s/it]
 64%|██████▍   | 1994/3112 [1:22:52<47:23,  2.54s/it]
                                                     
{'loss': 0.7886, 'learning_rate': 0.00012144432116906141, 'epoch': 0.64}
+
 64%|██████▍   | 1994/3112 [1:22:52<47:23,  2.54s/it]
 64%|██████▍   | 1995/3112 [1:22:55<46:19,  2.49s/it]
                                                     
{'loss': 0.707, 'learning_rate': 0.00012125252377808847, 'epoch': 0.64}
+
 64%|██████▍   | 1995/3112 [1:22:55<46:19,  2.49s/it]
 64%|██████▍   | 1996/3112 [1:22:57<45:09,  2.43s/it]
                                                     
{'loss': 0.6416, 'learning_rate': 0.0001210608120566516, 'epoch': 0.64}
+
 64%|██████▍   | 1996/3112 [1:22:57<45:09,  2.43s/it]
 64%|██████▍   | 1997/3112 [1:22:59<44:40,  2.40s/it]
                                                     
{'loss': 0.7626, 'learning_rate': 0.00012086918621331431, 'epoch': 0.64}
+
 64%|██████▍   | 1997/3112 [1:22:59<44:40,  2.40s/it]
 64%|██████▍   | 1998/3112 [1:23:02<45:36,  2.46s/it]
                                                     
{'loss': 0.7415, 'learning_rate': 0.00012067764645654675, 'epoch': 0.64}
+
 64%|██████▍   | 1998/3112 [1:23:02<45:36,  2.46s/it]
 64%|██████▍   | 1999/3112 [1:23:04<45:28,  2.45s/it]
                                                     
{'loss': 0.7189, 'learning_rate': 0.0001204861929947255, 'epoch': 0.64}
+
 64%|██████▍   | 1999/3112 [1:23:04<45:28,  2.45s/it]
 64%|██████▍   | 2000/3112 [1:23:07<46:04,  2.49s/it]
                                                     
{'loss': 0.7357, 'learning_rate': 0.00012029482603613302, 'epoch': 0.64}
+
 64%|██████▍   | 2000/3112 [1:23:07<46:04,  2.49s/it]/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+
 64%|██████▍   | 2001/3112 [1:23:10<49:45,  2.69s/it]
                                                     
{'loss': 0.7909, 'learning_rate': 0.00012010354578895788, 'epoch': 0.64}
+
 64%|██████▍   | 2001/3112 [1:23:10<49:45,  2.69s/it]
 64%|██████▍   | 2002/3112 [1:23:12<47:06,  2.55s/it]
                                                     
{'loss': 0.6961, 'learning_rate': 0.00011991235246129421, 'epoch': 0.64}
+
 64%|██████▍   | 2002/3112 [1:23:12<47:06,  2.55s/it]
 64%|██████▍   | 2003/3112 [1:23:15<48:29,  2.62s/it]
                                                     
{'loss': 0.7272, 'learning_rate': 0.00011972124626114167, 'epoch': 0.64}
+
 64%|██████▍   | 2003/3112 [1:23:15<48:29,  2.62s/it]
 64%|██████▍   | 2004/3112 [1:23:18<47:42,  2.58s/it]
                                                     
{'loss': 0.7409, 'learning_rate': 0.000119530227396405, 'epoch': 0.64}
+
 64%|██████▍   | 2004/3112 [1:23:18<47:42,  2.58s/it]
 64%|██████▍   | 2005/3112 [1:23:20<48:19,  2.62s/it]
                                                     
{'loss': 0.7532, 'learning_rate': 0.00011933929607489403, 'epoch': 0.64}
+
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 64%|██████▍   | 2006/3112 [1:23:22<45:19,  2.46s/it]
                                                     
{'loss': 0.7915, 'learning_rate': 0.00011914845250432336, 'epoch': 0.64}
+
 64%|██████▍   | 2006/3112 [1:23:22<45:19,  2.46s/it]
 64%|██████▍   | 2007/3112 [1:23:25<45:41,  2.48s/it]
                                                     
{'loss': 0.8186, 'learning_rate': 0.00011895769689231207, 'epoch': 0.64}
+
 64%|██████▍   | 2007/3112 [1:23:25<45:41,  2.48s/it]
 65%|██████▍   | 2008/3112 [1:23:27<44:47,  2.43s/it]
                                                     
{'loss': 0.7606, 'learning_rate': 0.00011876702944638353, 'epoch': 0.65}
+
 65%|██████▍   | 2008/3112 [1:23:27<44:47,  2.43s/it]
 65%|██████▍   | 2009/3112 [1:23:30<45:11,  2.46s/it]
                                                     
{'loss': 0.7402, 'learning_rate': 0.00011857645037396525, 'epoch': 0.65}
+
 65%|██████▍   | 2009/3112 [1:23:30<45:11,  2.46s/it]
 65%|██████▍   | 2010/3112 [1:23:32<45:23,  2.47s/it]
                                                     
{'loss': 0.7321, 'learning_rate': 0.00011838595988238865, 'epoch': 0.65}
+
 65%|██████▍   | 2010/3112 [1:23:32<45:23,  2.47s/it]
 65%|██████▍   | 2011/3112 [1:23:35<47:30,  2.59s/it]
                                                     
{'loss': 0.782, 'learning_rate': 0.00011819555817888865, 'epoch': 0.65}
+
 65%|██████▍   | 2011/3112 [1:23:35<47:30,  2.59s/it]
 65%|██████▍   | 2012/3112 [1:23:37<46:15,  2.52s/it]
                                                     
{'loss': 0.7983, 'learning_rate': 0.00011800524547060363, 'epoch': 0.65}
+
 65%|██████▍   | 2012/3112 [1:23:37<46:15,  2.52s/it]
 65%|██████▍   | 2013/3112 [1:23:40<46:18,  2.53s/it]
                                                     
{'loss': 0.7841, 'learning_rate': 0.00011781502196457519, 'epoch': 0.65}
+
 65%|██████▍   | 2013/3112 [1:23:40<46:18,  2.53s/it]
 65%|██████▍   | 2014/3112 [1:23:43<47:21,  2.59s/it]
                                                     
{'loss': 0.8789, 'learning_rate': 0.00011762488786774791, 'epoch': 0.65}
+
 65%|██████▍   | 2014/3112 [1:23:43<47:21,  2.59s/it]
 65%|██████▍   | 2015/3112 [1:23:45<46:26,  2.54s/it]
                                                     
{'loss': 0.8101, 'learning_rate': 0.00011743484338696895, 'epoch': 0.65}
+
 65%|██████▍   | 2015/3112 [1:23:45<46:26,  2.54s/it]
 65%|██████▍   | 2016/3112 [1:23:47<44:25,  2.43s/it]
                                                     
{'loss': 0.689, 'learning_rate': 0.00011724488872898811, 'epoch': 0.65}
+
 65%|██████▍   | 2016/3112 [1:23:47<44:25,  2.43s/it]
 65%|██████▍   | 2017/3112 [1:23:50<43:22,  2.38s/it]
                                                     
{'loss': 0.6798, 'learning_rate': 0.00011705502410045749, 'epoch': 0.65}
+
 65%|██████▍   | 2017/3112 [1:23:50<43:22,  2.38s/it]
 65%|██████▍   | 2018/3112 [1:23:52<44:54,  2.46s/it]
                                                     
{'loss': 0.7577, 'learning_rate': 0.0001168652497079312, 'epoch': 0.65}
+
 65%|██████▍   | 2018/3112 [1:23:52<44:54,  2.46s/it]
 65%|██████▍   | 2019/3112 [1:23:55<46:04,  2.53s/it]
                                                     
{'loss': 0.8506, 'learning_rate': 0.00011667556575786513, 'epoch': 0.65}
+
 65%|██████▍   | 2019/3112 [1:23:55<46:04,  2.53s/it]
 65%|██████▍   | 2020/3112 [1:23:57<44:36,  2.45s/it]
                                                     
{'loss': 0.7136, 'learning_rate': 0.00011648597245661679, 'epoch': 0.65}
+
 65%|██████▍   | 2020/3112 [1:23:57<44:36,  2.45s/it]
 65%|██████▍   | 2021/3112 [1:24:00<45:37,  2.51s/it]
                                                     
{'loss': 0.7641, 'learning_rate': 0.0001162964700104452, 'epoch': 0.65}
+
 65%|██████▍   | 2021/3112 [1:24:00<45:37,  2.51s/it]
 65%|██████▍   | 2022/3112 [1:24:02<44:40,  2.46s/it]
                                                     
{'loss': 0.7238, 'learning_rate': 0.00011610705862551043, 'epoch': 0.65}
+
 65%|██████▍   | 2022/3112 [1:24:02<44:40,  2.46s/it]
 65%|██████▌   | 2023/3112 [1:24:05<46:38,  2.57s/it]
                                                     
{'loss': 0.7979, 'learning_rate': 0.00011591773850787345, 'epoch': 0.65}
+
 65%|██████▌   | 2023/3112 [1:24:05<46:38,  2.57s/it]
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{'loss': 0.6941, 'learning_rate': 0.00011572850986349601, 'epoch': 0.65}
+
 65%|██████▌   | 2024/3112 [1:24:07<44:39,  2.46s/it]
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{'loss': 0.8353, 'learning_rate': 0.00011553937289824044, 'epoch': 0.65}
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{'loss': 0.7174, 'learning_rate': 0.00011535032781786901, 'epoch': 0.65}
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{'loss': 0.7849, 'learning_rate': 0.00011516137482804446, 'epoch': 0.65}
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{'loss': 0.7046, 'learning_rate': 0.00011497251413432895, 'epoch': 0.65}
+
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{'loss': 0.7235, 'learning_rate': 0.00011478374594218446, 'epoch': 0.65}
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 65%|██████▌   | 2030/3112 [1:24:22<44:50,  2.49s/it]
                                                     
{'loss': 0.6886, 'learning_rate': 0.00011459507045697235, 'epoch': 0.65}
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+  warnings.warn(
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{'train_runtime': 7776.3275, 'train_samples_per_second': 12.807, 'train_steps_per_second': 0.4, 'train_loss': 0.8083491923531714, 'epoch': 1.0}
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+wandb: 
+wandb: Run history:
+wandb:                    train/epoch ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
+wandb:              train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
+wandb:            train/learning_rate ▃███████▇▇▇▇▇▆▆▆▆▅▅▅▅▄▄▄▃▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁
+wandb:                     train/loss █▇▇▇▇▄▅▅▄▅▅▅▅▃▅▄▄▆▃▅▂▆▄▃▁▄▄▁▄▁▄▄▅▃▄▃▂▃▆▇
+wandb:               train/total_flos ▁
+wandb:               train/train_loss ▁
+wandb:            train/train_runtime ▁
+wandb: train/train_samples_per_second ▁
+wandb:   train/train_steps_per_second ▁
+wandb: 
+wandb: Run summary:
+wandb:                    train/epoch 1.0
+wandb:              train/global_step 3112
+wandb:            train/learning_rate 0.0
+wandb:                     train/loss 0.771
+wandb:               train/total_flos 8.2233084707917e+17
+wandb:               train/train_loss 0.80835
+wandb:            train/train_runtime 7776.3275
+wandb: train/train_samples_per_second 12.807
+wandb:   train/train_steps_per_second 0.4
+wandb: 
+wandb: 🚀 View run belle_dolphine-p8 at: https://wandb.ai/nus_keyu/lora-moe/runs/gzzd4w5x
+wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20231228_232541-gzzd4w5x/logs