diff --git "a/log.txt" "b/log.txt"
new file mode 100644--- /dev/null
+++ "b/log.txt"
@@ -0,0 +1,3552 @@
+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`.
+Params using prompt template alpaca:
+base_model: baichuan-inc/Baichuan2-7B-Base
+data_path: ../../data/belle_dolphine/p13.jsonl
+output_dir: ../out/lora/p13
+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-p13
+wandb_watch: 
+wandb_log_model: 
+resume_from_checkpoint: False
+
+gradient_accumulation_steps:  4
+gradient_accumulation_steps:  4
+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
+
Loading checkpoint shards: 100%|██████████| 2/2 [00:18<00:00,  8.45s/it]
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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
+trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183
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Map:  99%|█████████▉| 109482/110379 [02:52<00:01, 664.85 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-20231229_120011-rpu8pnl2
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run belle_dolphine-p13
+wandb: ⭐️ View project at https://wandb.ai/nus_keyu/lora-moe
+wandb: 🚀 View run at https://wandb.ai/nus_keyu/lora-moe/runs/rpu8pnl2
+
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+  warnings.warn(
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{'loss': 1.5236, 'learning_rate': 0.00022799999999999999, 'epoch': 0.02}
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{'loss': 1.3018, 'learning_rate': 0.000232, 'epoch': 0.02}
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{'loss': 1.4164, 'learning_rate': 0.000236, 'epoch': 0.02}
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{'loss': 1.3918, 'learning_rate': 0.00024, 'epoch': 0.02}
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{'loss': 1.5658, 'learning_rate': 0.000244, 'epoch': 0.02}
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{'loss': 1.3968, 'learning_rate': 0.000248, 'epoch': 0.02}
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{'loss': 1.4089, 'learning_rate': 0.000252, 'epoch': 0.02}
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{'loss': 1.3224, 'learning_rate': 0.00025600000000000004, 'epoch': 0.02}
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{'loss': 1.5052, 'learning_rate': 0.00026000000000000003, 'epoch': 0.02}
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{'loss': 1.4618, 'learning_rate': 0.000264, 'epoch': 0.02}
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{'loss': 1.405, 'learning_rate': 0.000268, 'epoch': 0.02}
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{'loss': 1.3175, 'learning_rate': 0.00027200000000000005, 'epoch': 0.02}
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{'loss': 1.2401, 'learning_rate': 0.000276, 'epoch': 0.02}
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{'loss': 1.3094, 'learning_rate': 0.00028, 'epoch': 0.02}
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{'loss': 1.2717, 'learning_rate': 0.000284, 'epoch': 0.02}
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{'loss': 1.2567, 'learning_rate': 0.000288, 'epoch': 0.02}
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{'loss': 1.4094, 'learning_rate': 0.000292, 'epoch': 0.02}
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{'loss': 1.3668, 'learning_rate': 0.000296, 'epoch': 0.02}
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{'loss': 1.2809, 'learning_rate': 0.00030000000000000003, 'epoch': 0.02}
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{'loss': 1.6195, 'learning_rate': 0.000304, 'epoch': 0.02}
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{'loss': 1.5164, 'learning_rate': 0.000308, 'epoch': 0.02}
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{'loss': 1.3275, 'learning_rate': 0.00031200000000000005, 'epoch': 0.02}
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{'loss': 1.3314, 'learning_rate': 0.00031600000000000004, 'epoch': 0.02}
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{'loss': 1.4825, 'learning_rate': 0.00032, 'epoch': 0.02}
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{'loss': 1.4019, 'learning_rate': 0.000324, 'epoch': 0.02}
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{'loss': 1.3452, 'learning_rate': 0.000328, 'epoch': 0.02}
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{'loss': 1.279, 'learning_rate': 0.000332, 'epoch': 0.02}
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{'loss': 1.4439, 'learning_rate': 0.000336, 'epoch': 0.02}
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{'loss': 1.3839, 'learning_rate': 0.00034, 'epoch': 0.02}
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{'loss': 1.2913, 'learning_rate': 0.000344, 'epoch': 0.02}
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{'loss': 1.3672, 'learning_rate': 0.000348, 'epoch': 0.03}
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{'loss': 1.4193, 'learning_rate': 0.00035200000000000005, 'epoch': 0.03}
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{'loss': 1.4455, 'learning_rate': 0.00035600000000000003, 'epoch': 0.03}
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{'loss': 1.2722, 'learning_rate': 0.00036, 'epoch': 0.03}
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{'loss': 1.5172, 'learning_rate': 0.000364, 'epoch': 0.03}
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{'loss': 1.4723, 'learning_rate': 0.00036800000000000005, 'epoch': 0.03}
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{'loss': 1.2708, 'learning_rate': 0.00037200000000000004, 'epoch': 0.03}
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{'loss': 1.4163, 'learning_rate': 0.000376, 'epoch': 0.03}
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{'loss': 1.4054, 'learning_rate': 0.00038, 'epoch': 0.03}
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{'loss': 1.3794, 'learning_rate': 0.000384, 'epoch': 0.03}
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{'loss': 1.3473, 'learning_rate': 0.000388, 'epoch': 0.03}
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{'loss': 1.5271, 'learning_rate': 0.000392, 'epoch': 0.03}
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{'loss': 1.4365, 'learning_rate': 0.00039600000000000003, 'epoch': 0.03}
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{'loss': 1.5053, 'learning_rate': 0.0004, 'epoch': 0.03}
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{'loss': 1.3237, 'learning_rate': 0.0003999999120026855, 'epoch': 0.03}
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{'loss': 1.5015, 'learning_rate': 0.00039999964801081954, 'epoch': 0.03}
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{'loss': 1.2449, 'learning_rate': 0.0003999992080246343, 'epoch': 0.03}
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{'loss': 1.4435, 'learning_rate': 0.0003999985920445171, 'epoch': 0.03}
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{'loss': 1.4326, 'learning_rate': 0.0003999978000710098, 'epoch': 0.03}
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{'loss': 1.4118, 'learning_rate': 0.0003999968321048095, 'epoch': 0.03}
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{'loss': 1.4152, 'learning_rate': 0.0003999956881467678, 'epoch': 0.03}
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{'loss': 1.3995, 'learning_rate': 0.00039999436819789153, 'epoch': 0.03}
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{'loss': 1.3273, 'learning_rate': 0.0003999928722593421, 'epoch': 0.03}
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{'loss': 1.2412, 'learning_rate': 0.000399991200332436, 'epoch': 0.03}
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{'loss': 1.404, 'learning_rate': 0.0003999893524186444, 'epoch': 0.03}
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{'loss': 1.4241, 'learning_rate': 0.0003999873285195934, 'epoch': 0.03}
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{'loss': 1.3263, 'learning_rate': 0.000399985128637064, 'epoch': 0.03}
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{'loss': 1.4066, 'learning_rate': 0.000399982752772992, 'epoch': 0.03}
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{'loss': 1.4737, 'learning_rate': 0.00039998020092946824, 'epoch': 0.03}
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{'loss': 1.3736, 'learning_rate': 0.0003999774731087381, 'epoch': 0.03}
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{'loss': 1.3962, 'learning_rate': 0.0003999745693132021, 'epoch': 0.03}
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{'loss': 1.4497, 'learning_rate': 0.00039997148954541543, 'epoch': 0.03}
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{'loss': 1.3684, 'learning_rate': 0.00039996823380808824, 'epoch': 0.03}
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{'loss': 1.396, 'learning_rate': 0.0003999648021040855, 'epoch': 0.03}
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{'loss': 1.4098, 'learning_rate': 0.000399961194436427, 'epoch': 0.04}
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{'loss': 1.4442, 'learning_rate': 0.00039995741080828734, 'epoch': 0.04}
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{'loss': 1.3268, 'learning_rate': 0.0003999534512229961, 'epoch': 0.04}
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{'loss': 1.4533, 'learning_rate': 0.00039994931568403764, 'epoch': 0.04}
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{'loss': 1.2902, 'learning_rate': 0.000399945004195051, 'epoch': 0.04}
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{'loss': 1.3231, 'learning_rate': 0.0003999405167598303, 'epoch': 0.04}
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{'loss': 1.4404, 'learning_rate': 0.0003999358533823242, 'epoch': 0.04}
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{'loss': 1.5074, 'learning_rate': 0.0003999310140666365, 'epoch': 0.04}
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{'loss': 1.5619, 'learning_rate': 0.00039992599881702554, 'epoch': 0.04}
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{'loss': 1.4426, 'learning_rate': 0.00039992080763790475, 'epoch': 0.04}
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{'loss': 1.2351, 'learning_rate': 0.00039991544053384213, 'epoch': 0.04}
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{'loss': 1.3252, 'learning_rate': 0.00039990989750956064, 'epoch': 0.04}
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{'loss': 1.4116, 'learning_rate': 0.00039990417856993794, 'epoch': 0.04}
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{'loss': 1.3452, 'learning_rate': 0.00039989828372000657, 'epoch': 0.04}
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{'loss': 1.3856, 'learning_rate': 0.0003998922129649538, 'epoch': 0.04}
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{'loss': 1.3277, 'learning_rate': 0.0003998859663101218, 'epoch': 0.04}
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{'loss': 1.4707, 'learning_rate': 0.0003998795437610074, 'epoch': 0.04}
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{'loss': 1.4154, 'learning_rate': 0.00039987294532326233, 'epoch': 0.04}
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{'loss': 1.4648, 'learning_rate': 0.000399866171002693, 'epoch': 0.04}
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{'loss': 1.3241, 'learning_rate': 0.00039985922080526065, 'epoch': 0.04}
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{'loss': 1.3787, 'learning_rate': 0.0003998520947370812, 'epoch': 0.04}
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{'loss': 1.1477, 'learning_rate': 0.0003998447928044255, 'epoch': 0.04}
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{'loss': 1.4423, 'learning_rate': 0.000399837315013719, 'epoch': 0.04}
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{'loss': 1.4003, 'learning_rate': 0.00039982966137154193, 'epoch': 0.04}
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{'loss': 1.3892, 'learning_rate': 0.0003998218318846293, 'epoch': 0.04}
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{'loss': 1.4127, 'learning_rate': 0.00039981382655987095, 'epoch': 0.04}
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{'loss': 1.4172, 'learning_rate': 0.00039980564540431125, 'epoch': 0.04}
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{'loss': 1.3198, 'learning_rate': 0.00039979728842514935, 'epoch': 0.04}
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{'loss': 1.3332, 'learning_rate': 0.00039978875562973927, 'epoch': 0.04}
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{'loss': 1.4563, 'learning_rate': 0.00039978004702558966, 'epoch': 0.04}
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{'loss': 1.4628, 'learning_rate': 0.0003997711626203637, 'epoch': 0.04}
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{'loss': 1.358, 'learning_rate': 0.00039976210242187964, 'epoch': 0.04}
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{'loss': 1.2227, 'learning_rate': 0.00039975286643811, 'epoch': 0.04}
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{'loss': 1.4093, 'learning_rate': 0.0003997434546771824, 'epoch': 0.04}
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{'loss': 1.3938, 'learning_rate': 0.0003997338671473787, 'epoch': 0.04}
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{'loss': 1.2654, 'learning_rate': 0.0003997241038571359, 'epoch': 0.05}
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{'loss': 1.3301, 'learning_rate': 0.00039971416481504536, 'epoch': 0.05}
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{'loss': 1.4287, 'learning_rate': 0.0003997040500298531, 'epoch': 0.05}
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{'loss': 1.5021, 'learning_rate': 0.00039969375951045985, 'epoch': 0.05}
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{'loss': 1.3404, 'learning_rate': 0.00039968329326592117, 'epoch': 0.05}
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{'loss': 1.3392, 'learning_rate': 0.0003996726513054469, 'epoch': 0.05}
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{'loss': 1.4043, 'learning_rate': 0.0003996618336384017, 'epoch': 0.05}
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{'loss': 1.2441, 'learning_rate': 0.00039965084027430487, 'epoch': 0.05}
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{'loss': 1.442, 'learning_rate': 0.00039963967122283033, 'epoch': 0.05}
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{'loss': 1.3297, 'learning_rate': 0.00039962832649380635, 'epoch': 0.05}
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{'loss': 1.3974, 'learning_rate': 0.00039961680609721626, 'epoch': 0.05}
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{'loss': 1.4026, 'learning_rate': 0.00039960511004319746, 'epoch': 0.05}
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{'loss': 1.4038, 'learning_rate': 0.0003995932383420423, 'epoch': 0.05}
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{'loss': 1.2948, 'learning_rate': 0.0003995811910041975, 'epoch': 0.05}
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{'loss': 1.504, 'learning_rate': 0.0003995689680402644, 'epoch': 0.05}
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{'loss': 1.2047, 'learning_rate': 0.00039955656946099894, 'epoch': 0.05}
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{'loss': 1.328, 'learning_rate': 0.0003995439952773114, 'epoch': 0.05}
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{'loss': 1.3518, 'learning_rate': 0.0003995312455002669, 'epoch': 0.05}
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{'loss': 1.4515, 'learning_rate': 0.0003995183201410848, 'epoch': 0.05}
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{'loss': 1.3857, 'learning_rate': 0.000399505219211139, 'epoch': 0.05}
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{'loss': 1.4068, 'learning_rate': 0.0003994919427219581, 'epoch': 0.05}
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{'loss': 1.2225, 'learning_rate': 0.000399478490685225, 'epoch': 0.05}
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{'loss': 1.3794, 'learning_rate': 0.0003994648631127772, 'epoch': 0.05}
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{'loss': 1.4642, 'learning_rate': 0.0003994510600166065, 'epoch': 0.05}
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{'loss': 1.2692, 'learning_rate': 0.00039943708140885925, 'epoch': 0.05}
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{'loss': 1.3233, 'learning_rate': 0.00039942292730183625, 'epoch': 0.05}
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{'loss': 1.2888, 'learning_rate': 0.0003994085977079928, 'epoch': 0.05}
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{'loss': 1.4167, 'learning_rate': 0.00039939409263993854, 'epoch': 0.05}
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{'loss': 1.3193, 'learning_rate': 0.0003993794121104375, 'epoch': 0.05}
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{'loss': 1.4016, 'learning_rate': 0.0003993645561324082, 'epoch': 0.05}
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{'loss': 1.3544, 'learning_rate': 0.0003993495247189235, 'epoch': 0.05}
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{'loss': 1.4277, 'learning_rate': 0.0003993343178832106, 'epoch': 0.05}
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{'loss': 1.4128, 'learning_rate': 0.00039931893563865115, 'epoch': 0.05}
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{'loss': 1.3224, 'learning_rate': 0.00039930337799878103, 'epoch': 0.05}
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{'loss': 1.4007, 'learning_rate': 0.0003992876449772907, 'epoch': 0.06}
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{'loss': 1.2749, 'learning_rate': 0.0003992717365880246, 'epoch': 0.06}
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{'loss': 1.3118, 'learning_rate': 0.00039925565284498185, 'epoch': 0.06}
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{'loss': 1.2867, 'learning_rate': 0.00039923939376231563, 'epoch': 0.06}
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{'loss': 1.3441, 'learning_rate': 0.00039922295935433346, 'epoch': 0.06}
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{'loss': 1.3888, 'learning_rate': 0.0003992063496354973, 'epoch': 0.06}
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{'loss': 1.405, 'learning_rate': 0.0003991895646204232, 'epoch': 0.06}
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{'loss': 1.4234, 'learning_rate': 0.00039917260432388146, 'epoch': 0.06}
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{'loss': 1.4004, 'learning_rate': 0.0003991554687607968, 'epoch': 0.06}
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{'loss': 1.4156, 'learning_rate': 0.000399138157946248, 'epoch': 0.06}
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{'loss': 1.4495, 'learning_rate': 0.000399120671895468, 'epoch': 0.06}
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{'loss': 1.4346, 'learning_rate': 0.0003991030106238442, 'epoch': 0.06}
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{'loss': 1.2202, 'learning_rate': 0.000399085174146918, 'epoch': 0.06}
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{'loss': 1.473, 'learning_rate': 0.00039906716248038507, 'epoch': 0.06}
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{'loss': 1.3089, 'learning_rate': 0.0003990489756400951, 'epoch': 0.06}
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{'loss': 1.3049, 'learning_rate': 0.0003990306136420521, 'epoch': 0.06}
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{'loss': 1.3764, 'learning_rate': 0.0003990120765024141, 'epoch': 0.06}
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{'loss': 1.272, 'learning_rate': 0.0003989933642374933, 'epoch': 0.06}
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{'loss': 1.3248, 'learning_rate': 0.0003989744768637559, 'epoch': 0.06}
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{'loss': 1.3713, 'learning_rate': 0.0003989554143978224, 'epoch': 0.06}
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{'loss': 1.5846, 'learning_rate': 0.0003989361768564673, 'epoch': 0.06}
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{'loss': 1.3624, 'learning_rate': 0.000398916764256619, 'epoch': 0.06}
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{'loss': 1.3555, 'learning_rate': 0.00039889717661536003, 'epoch': 0.06}
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{'loss': 1.2427, 'learning_rate': 0.00039887741394992713, 'epoch': 0.06}
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{'loss': 1.4018, 'learning_rate': 0.0003988574762777108, 'epoch': 0.06}
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{'loss': 1.3019, 'learning_rate': 0.0003988373636162558, 'epoch': 0.06}
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{'loss': 1.3403, 'learning_rate': 0.0003988170759832605, 'epoch': 0.06}
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{'loss': 1.3435, 'learning_rate': 0.00039879661339657776, 'epoch': 0.06}
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{'loss': 1.4053, 'learning_rate': 0.0003987759758742138, 'epoch': 0.06}
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{'loss': 1.4096, 'learning_rate': 0.00039875516343432937, 'epoch': 0.06}
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{'loss': 1.4866, 'learning_rate': 0.00039873417609523863, 'epoch': 0.06}
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{'loss': 1.4622, 'learning_rate': 0.00039871301387541, 'epoch': 0.06}
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{'loss': 1.3574, 'learning_rate': 0.00039869167679346573, 'epoch': 0.06}
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{'loss': 1.4224, 'learning_rate': 0.0003986701648681817, 'epoch': 0.06}
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{'loss': 1.3927, 'learning_rate': 0.000398648478118488, 'epoch': 0.06}
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{'loss': 1.3708, 'learning_rate': 0.00039862661656346816, 'epoch': 0.07}
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{'loss': 1.2435, 'learning_rate': 0.00039860458022236, 'epoch': 0.07}
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{'loss': 1.2099, 'learning_rate': 0.0003985823691145548, 'epoch': 0.07}
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{'loss': 1.3541, 'learning_rate': 0.0003985599832595977, 'epoch': 0.07}
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{'loss': 1.3682, 'learning_rate': 0.00039853742267718776, 'epoch': 0.07}
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{'loss': 1.3608, 'learning_rate': 0.00039851468738717765, 'epoch': 0.07}
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{'loss': 1.4069, 'learning_rate': 0.00039849177740957373, 'epoch': 0.07}
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{'loss': 1.7075, 'learning_rate': 0.00039846869276453624, 'epoch': 0.07}
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{'loss': 1.257, 'learning_rate': 0.000398445433472379, 'epoch': 0.07}
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{'loss': 1.3831, 'learning_rate': 0.0003984219995535697, 'epoch': 0.07}
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{'loss': 1.3522, 'learning_rate': 0.00039839839102872936, 'epoch': 0.07}
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{'loss': 1.4216, 'learning_rate': 0.00039837460791863297, 'epoch': 0.07}
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{'loss': 1.3535, 'learning_rate': 0.000398350650244209, 'epoch': 0.07}
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{'loss': 1.3365, 'learning_rate': 0.0003983265180265396, 'epoch': 0.07}
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{'loss': 1.4763, 'learning_rate': 0.0003983022112868604, 'epoch': 0.07}
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{'loss': 1.3085, 'learning_rate': 0.0003982777300465608, 'epoch': 0.07}
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{'loss': 1.3435, 'learning_rate': 0.0003982530743271834, 'epoch': 0.07}
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{'loss': 1.1607, 'learning_rate': 0.0003982282441504248, 'epoch': 0.07}
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{'loss': 1.3848, 'learning_rate': 0.0003982032395381348, 'epoch': 0.07}
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{'loss': 1.5452, 'learning_rate': 0.00039817806051231685, 'epoch': 0.07}
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{'loss': 1.4223, 'learning_rate': 0.0003981527070951277, 'epoch': 0.07}
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{'loss': 1.5239, 'learning_rate': 0.00039812717930887777, 'epoch': 0.07}
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{'loss': 1.4098, 'learning_rate': 0.0003981014771760307, 'epoch': 0.07}
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{'loss': 1.2477, 'learning_rate': 0.0003980756007192039, 'epoch': 0.07}
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{'loss': 1.2293, 'learning_rate': 0.00039804954996116774, 'epoch': 0.07}
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{'loss': 1.2934, 'learning_rate': 0.0003980233249248463, 'epoch': 0.07}
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{'loss': 1.2993, 'learning_rate': 0.00039799692563331686, 'epoch': 0.07}
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{'loss': 1.4085, 'learning_rate': 0.0003979703521098102, 'epoch': 0.07}
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{'loss': 1.2628, 'learning_rate': 0.00039794360437771015, 'epoch': 0.07}
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{'loss': 1.4072, 'learning_rate': 0.00039791668246055406, 'epoch': 0.07}
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{'loss': 1.3316, 'learning_rate': 0.0003978895863820325, 'epoch': 0.07}
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{'loss': 1.3214, 'learning_rate': 0.00039786231616598935, 'epoch': 0.07}
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{'loss': 1.3406, 'learning_rate': 0.00039783487183642154, 'epoch': 0.07}
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{'loss': 1.5058, 'learning_rate': 0.00039780725341747946, 'epoch': 0.07}
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{'loss': 1.2867, 'learning_rate': 0.0003977794609334665, 'epoch': 0.08}
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{'loss': 1.3296, 'learning_rate': 0.00039775149440883934, 'epoch': 0.08}
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{'loss': 1.3374, 'learning_rate': 0.0003977233538682077, 'epoch': 0.08}
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{'loss': 1.3311, 'learning_rate': 0.0003976950393363346, 'epoch': 0.08}
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{'loss': 1.3599, 'learning_rate': 0.00039766655083813603, 'epoch': 0.08}
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{'loss': 1.2804, 'learning_rate': 0.0003976378883986811, 'epoch': 0.08}
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{'loss': 1.2712, 'learning_rate': 0.000397609052043192, 'epoch': 0.08}
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{'loss': 1.3557, 'learning_rate': 0.00039758004179704393, 'epoch': 0.08}
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{'loss': 1.2742, 'learning_rate': 0.0003975508576857651, 'epoch': 0.08}
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{'loss': 1.5171, 'learning_rate': 0.0003975214997350368, 'epoch': 0.08}
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{'loss': 1.4556, 'learning_rate': 0.00039749196797069316, 'epoch': 0.08}
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{'loss': 1.2967, 'learning_rate': 0.0003974622624187215, 'epoch': 0.08}
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{'loss': 1.3242, 'learning_rate': 0.00039743238310526173, 'epoch': 0.08}
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{'loss': 1.2761, 'learning_rate': 0.0003974023300566069, 'epoch': 0.08}
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{'loss': 1.3672, 'learning_rate': 0.0003973721032992029, 'epoch': 0.08}
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{'loss': 1.3849, 'learning_rate': 0.0003973417028596485, 'epoch': 0.08}
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{'loss': 1.3892, 'learning_rate': 0.00039731112876469527, 'epoch': 0.08}
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{'loss': 1.3909, 'learning_rate': 0.0003972803810412475, 'epoch': 0.08}
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{'loss': 1.407, 'learning_rate': 0.0003972494597163625, 'epoch': 0.08}
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{'loss': 1.3331, 'learning_rate': 0.00039721836481725, 'epoch': 0.08}
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{'loss': 1.4107, 'learning_rate': 0.00039718709637127285, 'epoch': 0.08}
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{'loss': 1.3444, 'learning_rate': 0.00039715565440594637, 'epoch': 0.08}
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{'loss': 1.3164, 'learning_rate': 0.00039712403894893875, 'epoch': 0.08}
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{'loss': 1.4395, 'learning_rate': 0.0003970922500280706, 'epoch': 0.08}
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{'loss': 1.4244, 'learning_rate': 0.00039706028767131545, 'epoch': 0.08}
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{'loss': 1.2813, 'learning_rate': 0.00039702815190679916, 'epoch': 0.08}
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{'loss': 1.427, 'learning_rate': 0.00039699584276280044, 'epoch': 0.08}
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{'loss': 1.1327, 'learning_rate': 0.00039696336026775043, 'epoch': 0.08}
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{'loss': 1.3982, 'learning_rate': 0.000396930704450233, 'epoch': 0.08}
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{'loss': 1.3648, 'learning_rate': 0.00039689787533898414, 'epoch': 0.08}
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{'loss': 1.3662, 'learning_rate': 0.0003968648729628927, 'epoch': 0.08}
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{'loss': 1.3564, 'learning_rate': 0.00039683169735099997, 'epoch': 0.08}
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{'loss': 1.3171, 'learning_rate': 0.00039679834853249954, 'epoch': 0.08}
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{'loss': 1.4295, 'learning_rate': 0.0003967648265367374, 'epoch': 0.08}
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{'loss': 1.2965, 'learning_rate': 0.00039673113139321217, 'epoch': 0.08}
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{'loss': 1.6415, 'learning_rate': 0.00039669726313157447, 'epoch': 0.09}
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{'loss': 1.2551, 'learning_rate': 0.00039666322178162763, 'epoch': 0.09}
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{'loss': 1.3234, 'learning_rate': 0.00039662900737332695, 'epoch': 0.09}
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{'loss': 1.3994, 'learning_rate': 0.0003965946199367804, 'epoch': 0.09}
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{'loss': 1.206, 'learning_rate': 0.0003965600595022478, 'epoch': 0.09}
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{'loss': 1.2827, 'learning_rate': 0.0003965253261001416, 'epoch': 0.09}
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{'loss': 1.3162, 'learning_rate': 0.0003964904197610261, 'epoch': 0.09}
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{'loss': 1.5978, 'learning_rate': 0.000396455340515618, 'epoch': 0.09}
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{'loss': 1.3232, 'learning_rate': 0.00039642008839478615, 'epoch': 0.09}
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{'loss': 1.2778, 'learning_rate': 0.0003963846634295513, 'epoch': 0.09}
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{'loss': 1.3913, 'learning_rate': 0.00039634906565108667, 'epoch': 0.09}
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{'loss': 1.2894, 'learning_rate': 0.00039631329509071724, 'epoch': 0.09}
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{'loss': 1.3261, 'learning_rate': 0.00039627735177992024, 'epoch': 0.09}
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{'loss': 1.3329, 'learning_rate': 0.0003962412357503246, 'epoch': 0.09}
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{'loss': 1.3168, 'learning_rate': 0.00039620494703371175, 'epoch': 0.09}
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{'loss': 1.3801, 'learning_rate': 0.00039616848566201453, 'epoch': 0.09}
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{'loss': 1.3349, 'learning_rate': 0.00039613185166731814, 'epoch': 0.09}
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{'loss': 1.2949, 'learning_rate': 0.00039609504508185937, 'epoch': 0.09}
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{'loss': 1.1931, 'learning_rate': 0.00039605806593802714, 'epoch': 0.09}
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{'loss': 1.5426, 'learning_rate': 0.00039602091426836205, 'epoch': 0.09}
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{'loss': 1.4269, 'learning_rate': 0.0003959835901055566, 'epoch': 0.09}
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{'loss': 1.3779, 'learning_rate': 0.00039594609348245506, 'epoch': 0.09}
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{'loss': 1.3227, 'learning_rate': 0.00039590842443205335, 'epoch': 0.09}
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{'loss': 1.331, 'learning_rate': 0.0003958705829874994, 'epoch': 0.09}
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{'loss': 1.2632, 'learning_rate': 0.0003958325691820925, 'epoch': 0.09}
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{'loss': 1.3223, 'learning_rate': 0.00039579438304928385, 'epoch': 0.09}
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{'loss': 1.396, 'learning_rate': 0.0003957560246226763, 'epoch': 0.09}
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{'loss': 1.3239, 'learning_rate': 0.00039571749393602405, 'epoch': 0.09}
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{'loss': 1.4516, 'learning_rate': 0.0003956787910232332, 'epoch': 0.09}
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{'loss': 1.3951, 'learning_rate': 0.00039563991591836127, 'epoch': 0.09}
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{'loss': 1.3145, 'learning_rate': 0.0003956008686556172, 'epoch': 0.09}
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{'loss': 1.298, 'learning_rate': 0.0003955616492693617, 'epoch': 0.09}
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{'loss': 1.3131, 'learning_rate': 0.0003955222577941067, 'epoch': 0.09}
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{'loss': 1.2039, 'learning_rate': 0.00039548269426451557, 'epoch': 0.09}
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{'loss': 1.3435, 'learning_rate': 0.0003954429587154032, 'epoch': 0.1}
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{'loss': 1.4773, 'learning_rate': 0.0003954030511817359, 'epoch': 0.1}
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{'loss': 1.4621, 'learning_rate': 0.0003953629716986311, 'epoch': 0.1}
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{'loss': 1.4583, 'learning_rate': 0.0003953227203013577, 'epoch': 0.1}
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{'loss': 1.4588, 'learning_rate': 0.0003952822970253359, 'epoch': 0.1}
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{'loss': 1.3788, 'learning_rate': 0.0003952417019061371, 'epoch': 0.1}
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{'loss': 1.3653, 'learning_rate': 0.0003952009349794838, 'epoch': 0.1}
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{'loss': 1.2425, 'learning_rate': 0.0003951599962812499, 'epoch': 0.1}
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{'loss': 1.2731, 'learning_rate': 0.00039511888584746035, 'epoch': 0.1}
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{'loss': 1.3504, 'learning_rate': 0.00039507760371429126, 'epoch': 0.1}
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{'loss': 1.3654, 'learning_rate': 0.0003950361499180697, 'epoch': 0.1}
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{'loss': 1.3129, 'learning_rate': 0.00039499452449527395, 'epoch': 0.1}
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{'loss': 1.4244, 'learning_rate': 0.00039495272748253326, 'epoch': 0.1}
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{'loss': 1.41, 'learning_rate': 0.00039491075891662794, 'epoch': 0.1}
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{'loss': 1.2094, 'learning_rate': 0.00039486861883448907, 'epoch': 0.1}
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{'loss': 1.5471, 'learning_rate': 0.00039482630727319883, 'epoch': 0.1}
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{'loss': 1.2915, 'learning_rate': 0.0003947838242699904, 'epoch': 0.1}
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{'loss': 1.3057, 'learning_rate': 0.0003947411698622475, 'epoch': 0.1}
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{'loss': 1.374, 'learning_rate': 0.000394698344087505, 'epoch': 0.1}
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{'loss': 1.3786, 'learning_rate': 0.00039465534698344826, 'epoch': 0.1}
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{'loss': 1.4415, 'learning_rate': 0.0003946121785879138, 'epoch': 0.1}
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{'loss': 1.2374, 'learning_rate': 0.00039456883893888845, 'epoch': 0.1}
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{'loss': 1.3662, 'learning_rate': 0.00039452532807451006, 'epoch': 0.1}
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{'loss': 1.2629, 'learning_rate': 0.0003944816460330669, 'epoch': 0.1}
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{'loss': 1.2885, 'learning_rate': 0.00039443779285299824, 'epoch': 0.1}
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{'loss': 1.3265, 'learning_rate': 0.0003943937685728934, 'epoch': 0.1}
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{'loss': 1.2861, 'learning_rate': 0.00039434957323149277, 'epoch': 0.1}
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{'loss': 1.27, 'learning_rate': 0.00039430520686768695, 'epoch': 0.1}
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{'loss': 1.1879, 'learning_rate': 0.0003942606695205172, 'epoch': 0.1}
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{'loss': 1.4018, 'learning_rate': 0.0003942159612291752, 'epoch': 0.1}
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{'loss': 1.3663, 'learning_rate': 0.000394171082033003, 'epoch': 0.1}
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{'loss': 1.2719, 'learning_rate': 0.0003941260319714932, 'epoch': 0.1}
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{'loss': 1.3557, 'learning_rate': 0.00039408081108428843, 'epoch': 0.1}
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{'loss': 1.251, 'learning_rate': 0.0003940354194111821, 'epoch': 0.1}
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{'loss': 1.4242, 'learning_rate': 0.0003939898569921175, 'epoch': 0.1}
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{'loss': 1.464, 'learning_rate': 0.0003939441238671884, 'epoch': 0.11}
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{'loss': 1.279, 'learning_rate': 0.00039389822007663866, 'epoch': 0.11}
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+
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+  warnings.warn(
+
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{'loss': 1.2417, 'learning_rate': 0.0003327163269169282, 'epoch': 0.29}
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{'loss': 1.3875, 'learning_rate': 0.00017408853306386579, 'epoch': 0.55}
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{'loss': 1.413, 'learning_rate': 0.00017390251193895472, 'epoch': 0.55}
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{'loss': 1.1537, 'learning_rate': 0.00017334458700942644, 'epoch': 0.56}
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{'loss': 1.2471, 'learning_rate': 0.00017297275406389032, 'epoch': 0.56}
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{'loss': 1.2943, 'learning_rate': 0.00017260101625133563, 'epoch': 0.56}
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{'loss': 1.3282, 'learning_rate': 0.0001724151834288387, 'epoch': 0.56}
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{'loss': 1.5019, 'learning_rate': 0.0001722293748802394, 'epoch': 0.56}
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{'loss': 1.2648, 'learning_rate': 0.00017204359076904445, 'epoch': 0.56}
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{'loss': 1.1127, 'learning_rate': 0.00017185783125873893, 'epoch': 0.56}
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{'loss': 1.272, 'learning_rate': 0.00017167209651278604, 'epoch': 0.56}
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{'loss': 1.1037, 'learning_rate': 0.0001714863866946276, 'epoch': 0.56}
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{'loss': 1.2949, 'learning_rate': 0.00017130070196768297, 'epoch': 0.56}
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{'loss': 1.2811, 'learning_rate': 0.00017111504249534996, 'epoch': 0.56}
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{'loss': 1.1952, 'learning_rate': 0.00017092940844100387, 'epoch': 0.56}
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{'loss': 1.2638, 'learning_rate': 0.00017074379996799758, 'epoch': 0.56}
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{'loss': 1.1908, 'learning_rate': 0.0001705582172396617, 'epoch': 0.56}
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{'loss': 1.3337, 'learning_rate': 0.00017018712967020941, 'epoch': 0.56}
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{'loss': 1.2085, 'learning_rate': 0.00017000162515564007, 'epoch': 0.56}
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 57%|█████▋    | 1973/3449 [1:33:02<1:12:02,  2.93s/it]
                                                       
{'loss': 1.1763, 'learning_rate': 0.00016297350396910247, 'epoch': 0.57}
+
 57%|█████▋    | 1973/3449 [1:33:02<1:12:02,  2.93s/it]
 57%|█████▋    | 1974/3449 [1:33:05<1:10:52,  2.88s/it]
                                                       
{'loss': 1.2755, 'learning_rate': 0.00016278914968481374, 'epoch': 0.57}
+
 57%|█████▋    | 1974/3449 [1:33:05<1:10:52,  2.88s/it]
 57%|█████▋    | 1975/3449 [1:33:08<1:09:28,  2.83s/it]
                                                       
{'loss': 1.247, 'learning_rate': 0.00016260482814507387, 'epoch': 0.57}
+
 57%|█████▋    | 1975/3449 [1:33:08<1:09:28,  2.83s/it]
 57%|█████▋    | 1976/3449 [1:33:10<1:06:41,  2.72s/it]
                                                       
{'loss': 1.2318, 'learning_rate': 0.00016242053951208104, 'epoch': 0.57}
+
 57%|█████▋    | 1976/3449 [1:33:10<1:06:41,  2.72s/it]
 57%|█████▋    | 1977/3449 [1:33:13<1:07:09,  2.74s/it]
                                                       
{'loss': 1.215, 'learning_rate': 0.00016223628394800425, 'epoch': 0.57}
+
 57%|█████▋    | 1977/3449 [1:33:13<1:07:09,  2.74s/it]
 57%|█████▋    | 1978/3449 [1:33:16<1:04:55,  2.65s/it]
                                                       
{'loss': 1.0413, 'learning_rate': 0.00016205206161498342, 'epoch': 0.57}
+
 57%|█████▋    | 1978/3449 [1:33:16<1:04:55,  2.65s/it]
 57%|█████▋    | 1979/3449 [1:33:18<1:03:44,  2.60s/it]
                                                       
{'loss': 1.4229, 'learning_rate': 0.00016186787267512918, 'epoch': 0.57}
+
 57%|█████▋    | 1979/3449 [1:33:18<1:03:44,  2.60s/it]
 57%|█████▋    | 1980/3449 [1:33:21<1:03:15,  2.58s/it]
                                                       
{'loss': 1.2017, 'learning_rate': 0.00016168371729052307, 'epoch': 0.57}
+
 57%|█████▋    | 1980/3449 [1:33:21<1:03:15,  2.58s/it]
 57%|█████▋    | 1981/3449 [1:33:23<1:05:51,  2.69s/it]
                                                       
{'loss': 1.2663, 'learning_rate': 0.00016149959562321672, 'epoch': 0.57}
+
 57%|█████▋    | 1981/3449 [1:33:23<1:05:51,  2.69s/it]
 57%|█████▋    | 1982/3449 [1:33:26<1:05:46,  2.69s/it]
                                                       
{'loss': 1.1215, 'learning_rate': 0.0001613155078352322, 'epoch': 0.57}
+
 57%|█████▋    | 1982/3449 [1:33:26<1:05:46,  2.69s/it]
 57%|█████▋    | 1983/3449 [1:33:29<1:09:06,  2.83s/it]
                                                       
{'loss': 1.1153, 'learning_rate': 0.00016113145408856207, 'epoch': 0.57}
+
 57%|█████▋    | 1983/3449 [1:33:29<1:09:06,  2.83s/it]
 58%|█████▊    | 1984/3449 [1:33:33<1:14:15,  3.04s/it]
                                                       
{'loss': 1.1303, 'learning_rate': 0.0001609474345451684, 'epoch': 0.58}
+
 58%|█████▊    | 1984/3449 [1:33:33<1:14:15,  3.04s/it]
 58%|█████▊    | 1985/3449 [1:33:36<1:17:39,  3.18s/it]
                                                       
{'loss': 1.1184, 'learning_rate': 0.00016076344936698356, 'epoch': 0.58}
+
 58%|█████▊    | 1985/3449 [1:33:36<1:17:39,  3.18s/it]
 58%|█████▊    | 1986/3449 [1:33:39<1:15:55,  3.11s/it]
                                                       
{'loss': 1.2001, 'learning_rate': 0.00016057949871590963, 'epoch': 0.58}
+
 58%|█████▊    | 1986/3449 [1:33:39<1:15:55,  3.11s/it]
 58%|█████▊    | 1987/3449 [1:33:43<1:19:43,  3.27s/it]
                                                       
{'loss': 1.2126, 'learning_rate': 0.00016039558275381813, 'epoch': 0.58}
+
 58%|█████▊    | 1987/3449 [1:33:43<1:19:43,  3.27s/it]
 58%|█████▊    | 1988/3449 [1:33:45<1:12:09,  2.96s/it]
                                                       
{'loss': 1.2566, 'learning_rate': 0.00016021170164255027, 'epoch': 0.58}
+
 58%|█████▊    | 1988/3449 [1:33:45<1:12:09,  2.96s/it]
 58%|█████▊    | 1989/3449 [1:33:48<1:09:10,  2.84s/it]
                                                       
{'loss': 1.2464, 'learning_rate': 0.00016002785554391642, 'epoch': 0.58}
+
 58%|█████▊    | 1989/3449 [1:33:48<1:09:10,  2.84s/it]
 58%|█████▊    | 1990/3449 [1:33:50<1:05:22,  2.69s/it]
                                                       
{'loss': 1.3024, 'learning_rate': 0.00015984404461969623, 'epoch': 0.58}
+
 58%|█████▊    | 1990/3449 [1:33:50<1:05:22,  2.69s/it]
 58%|█████▊    | 1991/3449 [1:33:53<1:03:47,  2.63s/it]
                                                       
{'loss': 1.2382, 'learning_rate': 0.0001596602690316384, 'epoch': 0.58}
+
 58%|█████▊    | 1991/3449 [1:33:53<1:03:47,  2.63s/it]
 58%|█████▊    | 1992/3449 [1:33:56<1:08:27,  2.82s/it]
                                                       
{'loss': 1.2314, 'learning_rate': 0.0001594765289414604, 'epoch': 0.58}
+
 58%|█████▊    | 1992/3449 [1:33:56<1:08:27,  2.82s/it]
 58%|█████▊    | 1993/3449 [1:33:59<1:08:38,  2.83s/it]
                                                       
{'loss': 1.2229, 'learning_rate': 0.00015929282451084876, 'epoch': 0.58}
+
 58%|█████▊    | 1993/3449 [1:33:59<1:08:38,  2.83s/it]
 58%|█████▊    | 1994/3449 [1:34:02<1:10:03,  2.89s/it]
                                                       
{'loss': 1.3042, 'learning_rate': 0.00015910915590145835, 'epoch': 0.58}
+
 58%|█████▊    | 1994/3449 [1:34:02<1:10:03,  2.89s/it]
 58%|█████▊    | 1995/3449 [1:34:04<1:08:08,  2.81s/it]
                                                       
{'loss': 1.3198, 'learning_rate': 0.0001589255232749126, 'epoch': 0.58}
+
 58%|█████▊    | 1995/3449 [1:34:04<1:08:08,  2.81s/it]
 58%|█████▊    | 1996/3449 [1:34:07<1:04:59,  2.68s/it]
                                                       
{'loss': 1.4086, 'learning_rate': 0.00015874192679280323, 'epoch': 0.58}
+
 58%|█████▊    | 1996/3449 [1:34:07<1:04:59,  2.68s/it]
 58%|█████▊    | 1997/3449 [1:34:10<1:08:54,  2.85s/it]
                                                       
{'loss': 1.1759, 'learning_rate': 0.0001585583666166904, 'epoch': 0.58}
+
 58%|█████▊    | 1997/3449 [1:34:10<1:08:54,  2.85s/it]
 58%|█████▊    | 1998/3449 [1:34:12<1:05:59,  2.73s/it]
                                                       
{'loss': 1.2343, 'learning_rate': 0.00015837484290810196, 'epoch': 0.58}
+
 58%|█████▊    | 1998/3449 [1:34:12<1:05:59,  2.73s/it]
 58%|█████▊    | 1999/3449 [1:34:15<1:06:34,  2.75s/it]
                                                       
{'loss': 1.2502, 'learning_rate': 0.00015819135582853395, 'epoch': 0.58}
+
 58%|█████▊    | 1999/3449 [1:34:15<1:06:34,  2.75s/it]
 58%|█████▊    | 2000/3449 [1:34:19<1:11:52,  2.98s/it]
                                                       
{'loss': 1.2231, 'learning_rate': 0.00015800790553945003, 'epoch': 0.58}
+
 58%|█████▊    | 2000/3449 [1:34:19<1:11:52,  2.98s/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(
+
 58%|█████▊    | 2001/3449 [1:34:22<1:10:38,  2.93s/it]
                                                       
{'loss': 1.18, 'learning_rate': 0.00015782449220228144, 'epoch': 0.58}
+
 58%|█████▊    | 2001/3449 [1:34:22<1:10:38,  2.93s/it]
 58%|█████▊    | 2002/3449 [1:34:24<1:08:48,  2.85s/it]
                                                       
{'loss': 1.2754, 'learning_rate': 0.00015764111597842717, 'epoch': 0.58}
+
 58%|█████▊    | 2002/3449 [1:34:24<1:08:48,  2.85s/it]
 58%|█████▊    | 2003/3449 [1:34:26<1:04:15,  2.67s/it]
                                                       
{'loss': 1.3271, 'learning_rate': 0.00015745777702925326, 'epoch': 0.58}
+
 58%|█████▊    | 2003/3449 [1:34:26<1:04:15,  2.67s/it]
 58%|█████▊    | 2004/3449 [1:34:29<1:05:26,  2.72s/it]
                                                       
{'loss': 1.1925, 'learning_rate': 0.00015727447551609303, 'epoch': 0.58}
+
 58%|█████▊    | 2004/3449 [1:34:29<1:05:26,  2.72s/it]
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{'loss': 1.2643, 'learning_rate': 0.00015709121160024703, 'epoch': 0.58}
+
 58%|█████▊    | 2005/3449 [1:34:32<1:05:08,  2.71s/it]
 58%|█████▊    | 2006/3449 [1:34:35<1:06:30,  2.77s/it]
                                                       
{'loss': 1.1895, 'learning_rate': 0.00015690798544298247, 'epoch': 0.58}
+
 58%|█████▊    | 2006/3449 [1:34:35<1:06:30,  2.77s/it]
 58%|█████▊    | 2007/3449 [1:34:37<1:02:54,  2.62s/it]
                                                       
{'loss': 1.2227, 'learning_rate': 0.00015672479720553342, 'epoch': 0.58}
+
 58%|█████▊    | 2007/3449 [1:34:37<1:02:54,  2.62s/it]
 58%|█████▊    | 2008/3449 [1:34:40<1:03:47,  2.66s/it]
                                                       
{'loss': 1.2444, 'learning_rate': 0.00015654164704910077, 'epoch': 0.58}
+
 58%|█████▊    | 2008/3449 [1:34:40<1:03:47,  2.66s/it]
 58%|█████▊    | 2009/3449 [1:34:43<1:06:12,  2.76s/it]
                                                       
{'loss': 1.2285, 'learning_rate': 0.00015635853513485152, 'epoch': 0.58}
+
 58%|█████▊    | 2009/3449 [1:34:43<1:06:12,  2.76s/it]
 58%|█████▊    | 2010/3449 [1:34:46<1:05:47,  2.74s/it]
                                                       
{'loss': 1.2886, 'learning_rate': 0.00015617546162391944, 'epoch': 0.58}
+
 58%|█████▊    | 2010/3449 [1:34:46<1:05:47,  2.74s/it]
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{'loss': 1.2606, 'learning_rate': 0.00015599242667740416, 'epoch': 0.58}
+
 58%|█████▊    | 2011/3449 [1:34:48<1:03:11,  2.64s/it]
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{'loss': 1.3832, 'learning_rate': 0.0001558094304563716, 'epoch': 0.58}
+
 58%|█████▊    | 2012/3449 [1:34:51<1:03:56,  2.67s/it]
 58%|█████▊    | 2013/3449 [1:34:54<1:08:51,  2.88s/it]
                                                       
{'loss': 1.2479, 'learning_rate': 0.00015562647312185353, 'epoch': 0.58}
+
 58%|█████▊    | 2013/3449 [1:34:54<1:08:51,  2.88s/it]
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{'loss': 0.9849, 'learning_rate': 0.00015544355483484737, 'epoch': 0.58}
+
 58%|█████▊    | 2014/3449 [1:34:58<1:13:49,  3.09s/it]
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{'loss': 1.2859, 'learning_rate': 0.0001552606757563164, 'epoch': 0.58}
+
 58%|█████▊    | 2015/3449 [1:35:00<1:09:52,  2.92s/it]
 58%|█████▊    | 2016/3449 [1:35:03<1:08:46,  2.88s/it]
                                                       
{'loss': 1.1702, 'learning_rate': 0.00015507783604718932, 'epoch': 0.58}
+
 58%|█████▊    | 2016/3449 [1:35:03<1:08:46,  2.88s/it]
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{'loss': 1.1684, 'learning_rate': 0.0001548950358683601, 'epoch': 0.58}
+
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{'loss': 1.2288, 'learning_rate': 0.00015471227538068797, 'epoch': 0.59}
+
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{'loss': 1.2113, 'learning_rate': 0.00015452955474499737, 'epoch': 0.59}
+
 59%|█████▊    | 2019/3449 [1:35:12<1:12:29,  3.04s/it]
 59%|█████▊    | 2020/3449 [1:35:15<1:10:49,  2.97s/it]
                                                       
{'loss': 1.1303, 'learning_rate': 0.00015434687412207746, 'epoch': 0.59}
+
 59%|█████▊    | 2020/3449 [1:35:15<1:10:49,  2.97s/it]
 59%|█████▊    | 2021/3449 [1:35:18<1:09:30,  2.92s/it]
                                                       
{'loss': 1.3073, 'learning_rate': 0.00015416423367268223, 'epoch': 0.59}
+
 59%|█████▊    | 2021/3449 [1:35:18<1:09:30,  2.92s/it]
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{'loss': 1.2214, 'learning_rate': 0.00015398163355753052, 'epoch': 0.59}
+
 59%|█████▊    | 2022/3449 [1:35:21<1:10:48,  2.98s/it]
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{'loss': 1.2013, 'learning_rate': 0.00015379907393730542, 'epoch': 0.59}
+
 59%|█████▊    | 2023/3449 [1:35:24<1:08:17,  2.87s/it]
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{'loss': 1.0688, 'learning_rate': 0.00015361655497265456, 'epoch': 0.59}
+
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{'loss': 1.1376, 'learning_rate': 0.00015343407682418962, 'epoch': 0.59}
+
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{'loss': 1.2122, 'learning_rate': 0.00015325163965248652, 'epoch': 0.59}
+
 59%|█████▊    | 2026/3449 [1:35:34<1:15:37,  3.19s/it]
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{'loss': 1.236, 'learning_rate': 0.00015306924361808516, 'epoch': 0.59}
+
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 59%|█████▉    | 2028/3449 [1:35:39<1:10:17,  2.97s/it]
                                                       
{'loss': 1.0954, 'learning_rate': 0.00015288688888148906, 'epoch': 0.59}
+
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{'loss': 1.2584, 'learning_rate': 0.00015270457560316548, 'epoch': 0.59}
+
 59%|█████▉    | 2029/3449 [1:35:42<1:07:20,  2.85s/it]
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{'loss': 1.3267, 'learning_rate': 0.00015252230394354531, 'epoch': 0.59}
+
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{'loss': 1.3077, 'learning_rate': 0.00015234007406302258, 'epoch': 0.59}
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{'loss': 1.092, 'learning_rate': 0.00015215788612195477, 'epoch': 0.59}
+
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{'loss': 1.2471, 'learning_rate': 0.0001519757402806624, 'epoch': 0.59}
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{'loss': 1.1059, 'learning_rate': 0.00015179363669942886, 'epoch': 0.59}
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{'loss': 1.1881, 'learning_rate': 0.00015161157553850044, 'epoch': 0.59}
+
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{'loss': 1.3145, 'learning_rate': 0.000151429556958086, 'epoch': 0.59}
+
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{'loss': 1.2441, 'learning_rate': 0.00015124758111835713, 'epoch': 0.59}
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{'loss': 1.1415, 'learning_rate': 0.00015106564817944765, 'epoch': 0.59}
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{'loss': 1.2954, 'learning_rate': 0.00015088375830145352, 'epoch': 0.59}
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{'loss': 1.2318, 'learning_rate': 0.0001507019116444331, 'epoch': 0.59}
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{'loss': 1.3016, 'learning_rate': 0.00015052010836840663, 'epoch': 0.59}
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{'loss': 1.287, 'learning_rate': 2.042709994289107e-05, 'epoch': 0.86}
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{'loss': 1.1133, 'learning_rate': 2.0262213935386232e-05, 'epoch': 0.86}
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{'loss': 1.3774, 'learning_rate': 2.0180008142032092e-05, 'epoch': 0.86}
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{'loss': 1.1204, 'learning_rate': 2.0097960585441645e-05, 'epoch': 0.86}
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{'loss': 1.1569, 'learning_rate': 2.0016071337814558e-05, 'epoch': 0.86}
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{'loss': 1.1471, 'learning_rate': 1.993434047121123e-05, 'epoch': 0.86}
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{'loss': 1.081, 'learning_rate': 1.9852768057552585e-05, 'epoch': 0.86}
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{'loss': 1.2108, 'learning_rate': 1.9771354168620125e-05, 'epoch': 0.86}
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{'loss': 1.2698, 'learning_rate': 1.9690098876055953e-05, 'epoch': 0.86}
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{'loss': 1.1888, 'learning_rate': 1.9609002251362486e-05, 'epoch': 0.86}
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{'loss': 1.1195, 'learning_rate': 1.95280643659026e-05, 'epoch': 0.86}
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{'loss': 1.2994, 'learning_rate': 1.9447285290899474e-05, 'epoch': 0.86}
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{'loss': 1.2338, 'learning_rate': 1.9366665097436497e-05, 'epoch': 0.86}
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{'loss': 1.177, 'learning_rate': 1.928620385645734e-05, 'epoch': 0.86}
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{'loss': 1.2411, 'learning_rate': 1.9205901638765612e-05, 'epoch': 0.86}
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{'loss': 1.1876, 'learning_rate': 1.9125758515025205e-05, 'epoch': 0.86}
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{'loss': 1.2754, 'learning_rate': 1.904577455575991e-05, 'epoch': 0.86}
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{'loss': 1.1749, 'learning_rate': 1.8965949831353403e-05, 'epoch': 0.86}
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{'loss': 1.1482, 'learning_rate': 1.8886284412049337e-05, 'epoch': 0.86}
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{'loss': 1.1196, 'learning_rate': 1.8806778367951218e-05, 'epoch': 0.87}
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{'loss': 1.3412, 'learning_rate': 1.8727431769022098e-05, 'epoch': 0.87}
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{'loss': 1.2215, 'learning_rate': 1.8648244685084858e-05, 'epoch': 0.87}
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{'loss': 1.1714, 'learning_rate': 1.8569217185822164e-05, 'epoch': 0.87}
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{'loss': 1.2181, 'learning_rate': 1.8490349340775916e-05, 'epoch': 0.87}
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{'loss': 1.1393, 'learning_rate': 1.8411641219347775e-05, 'epoch': 0.87}
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{'loss': 1.3228, 'learning_rate': 1.8333092890798788e-05, 'epoch': 0.87}
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{'loss': 1.1247, 'learning_rate': 1.8254704424249324e-05, 'epoch': 0.87}
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{'loss': 1.3653, 'learning_rate': 1.817647588867919e-05, 'epoch': 0.87}
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{'loss': 1.2809, 'learning_rate': 1.809840735292736e-05, 'epoch': 0.87}
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{'loss': 1.0852, 'learning_rate': 1.802049888569204e-05, 'epoch': 0.87}
+
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{'loss': 1.1379, 'learning_rate': 1.794275055553065e-05, 'epoch': 0.87}
+
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{'loss': 1.2159, 'learning_rate': 1.7865162430859496e-05, 'epoch': 0.87}
+
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{'loss': 1.0354, 'learning_rate': 1.7787734579954195e-05, 'epoch': 0.87}
+
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{'loss': 1.3856, 'learning_rate': 1.7710467070949142e-05, 'epoch': 0.87}
+
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{'loss': 1.1133, 'learning_rate': 1.763335997183757e-05, 'epoch': 0.87}
+
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{'loss': 1.2981, 'learning_rate': 1.75564133504718e-05, 'epoch': 0.87}
+
 87%|████████▋ | 3000/3449 [2:21:29<21:15,  2.84s/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(
+
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{'loss': 1.2427, 'learning_rate': 1.7479627274562672e-05, 'epoch': 0.87}
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{'loss': 1.1127, 'learning_rate': 1.7403001811679932e-05, 'epoch': 0.87}
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{'loss': 1.2479, 'learning_rate': 1.7326537029251933e-05, 'epoch': 0.87}
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{'loss': 1.1347, 'learning_rate': 1.7250232994565606e-05, 'epoch': 0.87}
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{'loss': 1.218, 'learning_rate': 1.7174089774766443e-05, 'epoch': 0.87}
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{'loss': 1.1078, 'learning_rate': 1.709810743685847e-05, 'epoch': 0.87}
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{'loss': 1.3854, 'learning_rate': 1.7022286047704082e-05, 'epoch': 0.87}
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{'loss': 1.243, 'learning_rate': 1.6946625674024052e-05, 'epoch': 0.87}
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{'loss': 1.1764, 'learning_rate': 1.6871126382397516e-05, 'epoch': 0.87}
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{'loss': 1.1269, 'learning_rate': 1.679578823926178e-05, 'epoch': 0.87}
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{'loss': 1.2676, 'learning_rate': 1.672061131091245e-05, 'epoch': 0.87}
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{'loss': 1.1869, 'learning_rate': 1.6645595663503122e-05, 'epoch': 0.87}
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{'loss': 1.0474, 'learning_rate': 1.657074136304555e-05, 'epoch': 0.87}
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{'loss': 1.1431, 'learning_rate': 1.6496048475409643e-05, 'epoch': 0.87}
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{'loss': 1.0896, 'learning_rate': 1.6421517066322976e-05, 'epoch': 0.87}
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{'loss': 1.1337, 'learning_rate': 1.6347147201371228e-05, 'epoch': 0.87}
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{'loss': 1.1946, 'learning_rate': 1.627293894599797e-05, 'epoch': 0.87}
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{'loss': 1.1951, 'learning_rate': 1.6198892365504403e-05, 'epoch': 0.87}
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{'loss': 1.2758, 'learning_rate': 1.6125007525049508e-05, 'epoch': 0.88}
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{'loss': 1.1122, 'learning_rate': 1.605128448965001e-05, 'epoch': 0.88}
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{'loss': 1.2284, 'learning_rate': 1.597772332418017e-05, 'epoch': 0.88}
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{'loss': 1.232, 'learning_rate': 1.590432409337186e-05, 'epoch': 0.88}
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{'loss': 1.2455, 'learning_rate': 1.5831086861814404e-05, 'epoch': 0.88}
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{'loss': 1.2027, 'learning_rate': 1.575801169395461e-05, 'epoch': 0.88}
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{'loss': 1.3046, 'learning_rate': 1.568509865409673e-05, 'epoch': 0.88}
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{'loss': 1.1795, 'learning_rate': 1.5612347806402127e-05, 'epoch': 0.88}
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{'loss': 1.3274, 'learning_rate': 1.5539759214889725e-05, 'epoch': 0.88}
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{'loss': 1.1827, 'learning_rate': 1.5467332943435543e-05, 'epoch': 0.88}
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{'loss': 1.1546, 'learning_rate': 1.539506905577266e-05, 'epoch': 0.88}
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{'loss': 1.2797, 'learning_rate': 1.5322967615491367e-05, 'epoch': 0.88}
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{'loss': 1.1132, 'learning_rate': 1.5251028686039115e-05, 'epoch': 0.88}
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{'loss': 1.2323, 'learning_rate': 1.5179252330720107e-05, 'epoch': 0.88}
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{'loss': 1.0618, 'learning_rate': 8.799731447961534e-11, 'epoch': 1.0}
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{'train_runtime': 9769.0231, 'train_samples_per_second': 11.299, 'train_steps_per_second': 0.353, 'train_loss': 1.2677869534416453, '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 3449
+wandb:            train/learning_rate 0.0
+wandb:                     train/loss 1.0618
+wandb:               train/total_flos 1.0345695487385928e+18
+wandb:               train/train_loss 1.26779
+wandb:            train/train_runtime 9769.0231
+wandb: train/train_samples_per_second 11.299
+wandb:   train/train_steps_per_second 0.353
+wandb: 
+wandb: 🚀 View run belle_dolphine-p13 at: https://wandb.ai/nus_keyu/lora-moe/runs/rpu8pnl2
+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-20231229_120011-rpu8pnl2/logs