second commit
Browse files- all_results.json +7 -7
- generated_predictions.jsonl +0 -0
- llamaboard_config.yaml +12 -57
- predict_results.json +9 -0
- running_log.txt +87 -935
- trainer_log.jsonl +0 -0
- training_args.yaml +9 -21
all_results.json
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{
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{
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"predict_bleu-4": 86.3840886328125,
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"predict_rouge-1": 94.296875,
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"predict_rouge-2": 0.0,
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"predict_rouge-l": 94.296875,
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"predict_runtime": 17.1267,
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"predict_samples_per_second": 149.124,
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"predict_steps_per_second": 9.342
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}
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generated_predictions.jsonl
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llamaboard_config.yaml
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top.booster: auto
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top.checkpoint_path:
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top.finetuning_type: full
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top.model_name: LLaMA3-8B-Chat
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top.quantization_bit: none
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top.rope_scaling: none
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top.template: llama3
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top.visual_inputs: false
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train.additional_target: ''
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train.badam_mode: layer
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train.badam_switch_interval: 50
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train.badam_switch_mode: ascending
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train.badam_update_ratio: 0.05
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train.batch_size: 4
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train.compute_type: bf16
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train.create_new_adapter: false
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train.cutoff_len: 1024
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train.dataset:
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- truth_train
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train.dataset_dir: data
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train.ds_offload: false
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train.ds_stage: '2'
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train.freeze_extra_modules: ''
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train.freeze_trainable_layers: 2
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train.freeze_trainable_modules: all
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train.galore_rank: 16
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train.galore_scale: 0.25
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train.galore_target: all
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train.galore_update_interval: 200
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train.gradient_accumulation_steps: 8
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train.learning_rate: 5e-6
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train.logging_steps: 1
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train.lora_alpha: 16
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train.lora_dropout: 0
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train.lora_rank: 8
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train.lora_target: ''
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train.loraplus_lr_ratio: 0
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train.lr_scheduler_type: cosine
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train.max_grad_norm: '1.0'
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train.max_samples: '100000'
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train.neat_packing: false
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train.neftune_alpha: 0
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train.num_train_epochs: '5.0'
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train.optim: adamw_torch
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train.packing: false
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train.ppo_score_norm: false
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train.ppo_whiten_rewards: false
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train.pref_beta: 0.1
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train.pref_ftx: 0
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train.pref_loss: sigmoid
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train.report_to: false
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train.resize_vocab: false
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train.reward_model: null
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train.save_steps: 1000
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train.shift_attn: false
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train.training_stage: Supervised Fine-Tuning
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train.use_badam: false
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train.use_dora: false
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train.use_galore: false
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train.use_llama_pro: false
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train.use_pissa: false
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train.use_rslora: false
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train.val_size: 0
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train.warmup_steps: 600
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eval.batch_size: 2
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eval.cutoff_len: 1024
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eval.dataset:
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- truth_dev
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eval.dataset_dir: data
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eval.max_new_tokens: 512
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eval.max_samples: '100000'
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eval.output_dir: eval_2024-07-11-10-49-45
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eval.predict: true
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eval.temperature: 0.95
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eval.top_p: 0.7
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top.booster: auto
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top.checkpoint_path: train_2024-07-11-10-49-45_inst_llama3
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top.finetuning_type: full
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top.model_name: LLaMA3-8B-Chat
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top.quantization_bit: none
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top.rope_scaling: none
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top.template: llama3
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top.visual_inputs: false
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predict_results.json
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{
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"predict_bleu-4": 86.3840886328125,
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"predict_rouge-1": 94.296875,
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"predict_rouge-2": 0.0,
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"predict_rouge-l": 94.296875,
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"predict_runtime": 17.1267,
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"predict_samples_per_second": 149.124,
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"predict_steps_per_second": 9.342
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}
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running_log.txt
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[INFO|parser.py:325] 2024-07-11
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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07/11/2024 11:02:35 - INFO - llamafactory.data.loader - Loading dataset train_output.json...
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[INFO|configuration_utils.py:733] 2024-07-11 11:02:39,782 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/snapshots/e1945c40cd546c78e41f1151f4db032b271faeaa/config.json
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[INFO|configuration_utils.py:800] 2024-07-11 11:02:39,785 >> Model config LlamaConfig {
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"_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.3",
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"use_cache":
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"vocab_size": 128256
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}
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"bos_token_id": 128000,
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"eos_token_id": 128009
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}
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[INFO|modeling_utils.py:4372] 2024-07-11
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If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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}
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[INFO|
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[INFO|attention.py:80] 2024-07-11 11:05:11,066 >> Using torch SDPA for faster training and inference.
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[INFO|adapter.py:302] 2024-07-11 11:05:11,066 >> Upcasting trainable params to float32.
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[INFO|adapter.py:48] 2024-07-11 11:05:11,066 >> Fine-tuning method: Full
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[INFO|loader.py:196] 2024-07-11 11:05:11,161 >> trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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07/11/2024 11:05:11 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
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[INFO|trainer.py:642] 2024-07-11 11:05:11,167 >> Using auto half precision backend
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[INFO|trainer.py:2128] 2024-07-11 11:05:33,940 >> ***** Running training *****
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[INFO|trainer.py:2129] 2024-07-11 11:05:33,940 >> Num examples = 19,880
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[INFO|trainer.py:2130] 2024-07-11 11:05:33,940 >> Num Epochs = 5
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[INFO|trainer.py:2131] 2024-07-11 11:05:33,940 >> Instantaneous batch size per device = 4
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[INFO|trainer.py:2134] 2024-07-11 11:05:33,940 >> Total train batch size (w. parallel, distributed & accumulation) = 256
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[INFO|trainer.py:2135] 2024-07-11 11:05:33,940 >> Gradient Accumulation steps = 8
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[INFO|trainer.py:2136] 2024-07-11 11:05:33,940 >> Total optimization steps = 385
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| 249 |
-
[INFO|trainer.py:2137] 2024-07-11 11:05:33,941 >> Number of trainable parameters = 8,030,261,248
|
| 250 |
-
|
| 251 |
-
[INFO|callbacks.py:310] 2024-07-11 11:05:52,173 >> {'loss': 13.9619, 'learning_rate': 8.3333e-09, 'epoch': 0.01, 'throughput': 830.24}
|
| 252 |
-
|
| 253 |
-
[INFO|callbacks.py:310] 2024-07-11 11:06:05,761 >> {'loss': 13.7129, 'learning_rate': 1.6667e-08, 'epoch': 0.03, 'throughput': 949.38}
|
| 254 |
-
|
| 255 |
-
[INFO|callbacks.py:310] 2024-07-11 11:06:19,304 >> {'loss': 13.8474, 'learning_rate': 2.5000e-08, 'epoch': 0.04, 'throughput': 1000.31}
|
| 256 |
-
|
| 257 |
-
[INFO|callbacks.py:310] 2024-07-11 11:06:32,865 >> {'loss': 13.8844, 'learning_rate': 3.3333e-08, 'epoch': 0.05, 'throughput': 1017.74}
|
| 258 |
-
|
| 259 |
-
[INFO|callbacks.py:310] 2024-07-11 11:06:46,377 >> {'loss': 14.1380, 'learning_rate': 4.1667e-08, 'epoch': 0.06, 'throughput': 1037.30}
|
| 260 |
-
|
| 261 |
-
[INFO|callbacks.py:310] 2024-07-11 11:06:59,966 >> {'loss': 13.9077, 'learning_rate': 5.0000e-08, 'epoch': 0.08, 'throughput': 1055.71}
|
| 262 |
-
|
| 263 |
-
[INFO|callbacks.py:310] 2024-07-11 11:07:13,513 >> {'loss': 13.8396, 'learning_rate': 5.8333e-08, 'epoch': 0.09, 'throughput': 1060.56}
|
| 264 |
-
|
| 265 |
-
[INFO|callbacks.py:310] 2024-07-11 11:07:27,086 >> {'loss': 13.9828, 'learning_rate': 6.6667e-08, 'epoch': 0.10, 'throughput': 1059.47}
|
| 266 |
-
|
| 267 |
-
[INFO|callbacks.py:310] 2024-07-11 11:07:40,694 >> {'loss': 14.0361, 'learning_rate': 7.5000e-08, 'epoch': 0.12, 'throughput': 1065.89}
|
| 268 |
-
|
| 269 |
-
[INFO|callbacks.py:310] 2024-07-11 11:07:54,240 >> {'loss': 13.9392, 'learning_rate': 8.3333e-08, 'epoch': 0.13, 'throughput': 1069.27}
|
| 270 |
-
|
| 271 |
-
[INFO|callbacks.py:310] 2024-07-11 11:08:07,884 >> {'loss': 14.0256, 'learning_rate': 9.1667e-08, 'epoch': 0.14, 'throughput': 1073.24}
|
| 272 |
-
|
| 273 |
-
[INFO|callbacks.py:310] 2024-07-11 11:08:21,425 >> {'loss': 13.6693, 'learning_rate': 1.0000e-07, 'epoch': 0.15, 'throughput': 1070.54}
|
| 274 |
-
|
| 275 |
-
[INFO|callbacks.py:310] 2024-07-11 11:08:34,965 >> {'loss': 13.9031, 'learning_rate': 1.0833e-07, 'epoch': 0.17, 'throughput': 1067.89}
|
| 276 |
-
|
| 277 |
-
[INFO|callbacks.py:310] 2024-07-11 11:08:48,495 >> {'loss': 13.8575, 'learning_rate': 1.1667e-07, 'epoch': 0.18, 'throughput': 1073.07}
|
| 278 |
-
|
| 279 |
-
[INFO|callbacks.py:310] 2024-07-11 11:09:02,114 >> {'loss': 13.8366, 'learning_rate': 1.2500e-07, 'epoch': 0.19, 'throughput': 1073.42}
|
| 280 |
-
|
| 281 |
-
[INFO|callbacks.py:310] 2024-07-11 11:09:15,701 >> {'loss': 13.8705, 'learning_rate': 1.3333e-07, 'epoch': 0.21, 'throughput': 1076.20}
|
| 282 |
-
|
| 283 |
-
[INFO|callbacks.py:310] 2024-07-11 11:09:29,305 >> {'loss': 13.1816, 'learning_rate': 1.4167e-07, 'epoch': 0.22, 'throughput': 1076.26}
|
| 284 |
-
|
| 285 |
-
[INFO|callbacks.py:310] 2024-07-11 11:09:42,873 >> {'loss': 13.2292, 'learning_rate': 1.5000e-07, 'epoch': 0.23, 'throughput': 1078.02}
|
| 286 |
-
|
| 287 |
-
[INFO|callbacks.py:310] 2024-07-11 11:09:56,380 >> {'loss': 13.4366, 'learning_rate': 1.5833e-07, 'epoch': 0.24, 'throughput': 1076.68}
|
| 288 |
-
|
| 289 |
-
[INFO|callbacks.py:310] 2024-07-11 11:10:09,892 >> {'loss': 12.9904, 'learning_rate': 1.6667e-07, 'epoch': 0.26, 'throughput': 1076.72}
|
| 290 |
-
|
| 291 |
-
[INFO|callbacks.py:310] 2024-07-11 11:10:23,491 >> {'loss': 12.8780, 'learning_rate': 1.7500e-07, 'epoch': 0.27, 'throughput': 1078.97}
|
| 292 |
-
|
| 293 |
-
[INFO|callbacks.py:310] 2024-07-11 11:10:37,113 >> {'loss': 12.7794, 'learning_rate': 1.8333e-07, 'epoch': 0.28, 'throughput': 1081.79}
|
| 294 |
-
|
| 295 |
-
[INFO|callbacks.py:310] 2024-07-11 11:10:50,680 >> {'loss': 11.3144, 'learning_rate': 1.9167e-07, 'epoch': 0.30, 'throughput': 1082.54}
|
| 296 |
-
|
| 297 |
-
[INFO|callbacks.py:310] 2024-07-11 11:11:04,260 >> {'loss': 10.8531, 'learning_rate': 2.0000e-07, 'epoch': 0.31, 'throughput': 1086.37}
|
| 298 |
-
|
| 299 |
-
[INFO|callbacks.py:310] 2024-07-11 11:11:17,809 >> {'loss': 10.7149, 'learning_rate': 2.0833e-07, 'epoch': 0.32, 'throughput': 1087.02}
|
| 300 |
-
|
| 301 |
-
[INFO|callbacks.py:310] 2024-07-11 11:11:31,356 >> {'loss': 10.5802, 'learning_rate': 2.1667e-07, 'epoch': 0.33, 'throughput': 1089.07}
|
| 302 |
-
|
| 303 |
-
[INFO|callbacks.py:310] 2024-07-11 11:11:44,856 >> {'loss': 10.3671, 'learning_rate': 2.2500e-07, 'epoch': 0.35, 'throughput': 1090.07}
|
| 304 |
-
|
| 305 |
-
[INFO|callbacks.py:310] 2024-07-11 11:11:58,442 >> {'loss': 10.1751, 'learning_rate': 2.3333e-07, 'epoch': 0.36, 'throughput': 1090.50}
|
| 306 |
-
|
| 307 |
-
[INFO|callbacks.py:310] 2024-07-11 11:12:11,969 >> {'loss': 9.7707, 'learning_rate': 2.4167e-07, 'epoch': 0.37, 'throughput': 1089.46}
|
| 308 |
-
|
| 309 |
-
[INFO|callbacks.py:310] 2024-07-11 11:12:25,588 >> {'loss': 9.6489, 'learning_rate': 2.5000e-07, 'epoch': 0.39, 'throughput': 1090.10}
|
| 310 |
-
|
| 311 |
-
[INFO|callbacks.py:310] 2024-07-11 11:12:39,159 >> {'loss': 8.6805, 'learning_rate': 2.5833e-07, 'epoch': 0.40, 'throughput': 1090.83}
|
| 312 |
-
|
| 313 |
-
[INFO|callbacks.py:310] 2024-07-11 11:12:52,757 >> {'loss': 5.9207, 'learning_rate': 2.6667e-07, 'epoch': 0.41, 'throughput': 1091.16}
|
| 314 |
-
|
| 315 |
-
[INFO|callbacks.py:310] 2024-07-11 11:13:06,329 >> {'loss': 5.7661, 'learning_rate': 2.7500e-07, 'epoch': 0.42, 'throughput': 1093.01}
|
| 316 |
-
|
| 317 |
-
[INFO|callbacks.py:310] 2024-07-11 11:13:19,934 >> {'loss': 5.6168, 'learning_rate': 2.8333e-07, 'epoch': 0.44, 'throughput': 1094.61}
|
| 318 |
-
|
| 319 |
-
[INFO|callbacks.py:310] 2024-07-11 11:13:33,491 >> {'loss': 5.3367, 'learning_rate': 2.9167e-07, 'epoch': 0.45, 'throughput': 1095.23}
|
| 320 |
-
|
| 321 |
-
[INFO|callbacks.py:310] 2024-07-11 11:13:47,054 >> {'loss': 4.9751, 'learning_rate': 3.0000e-07, 'epoch': 0.46, 'throughput': 1094.37}
|
| 322 |
-
|
| 323 |
-
[INFO|callbacks.py:310] 2024-07-11 11:14:00,636 >> {'loss': 4.7041, 'learning_rate': 3.0833e-07, 'epoch': 0.48, 'throughput': 1093.52}
|
| 324 |
-
|
| 325 |
-
[INFO|callbacks.py:310] 2024-07-11 11:14:14,270 >> {'loss': 4.4631, 'learning_rate': 3.1667e-07, 'epoch': 0.49, 'throughput': 1093.46}
|
| 326 |
-
|
| 327 |
-
[INFO|callbacks.py:310] 2024-07-11 11:14:27,823 >> {'loss': 4.1912, 'learning_rate': 3.2500e-07, 'epoch': 0.50, 'throughput': 1093.70}
|
| 328 |
-
|
| 329 |
-
[INFO|callbacks.py:310] 2024-07-11 11:14:41,381 >> {'loss': 3.9146, 'learning_rate': 3.3333e-07, 'epoch': 0.51, 'throughput': 1093.91}
|
| 330 |
-
|
| 331 |
-
[INFO|callbacks.py:310] 2024-07-11 11:14:55,032 >> {'loss': 3.0530, 'learning_rate': 3.4167e-07, 'epoch': 0.53, 'throughput': 1094.67}
|
| 332 |
-
|
| 333 |
-
[INFO|callbacks.py:310] 2024-07-11 11:15:08,599 >> {'loss': 1.5544, 'learning_rate': 3.5000e-07, 'epoch': 0.54, 'throughput': 1095.05}
|
| 334 |
-
|
| 335 |
-
[INFO|callbacks.py:310] 2024-07-11 11:15:22,200 >> {'loss': 1.0549, 'learning_rate': 3.5833e-07, 'epoch': 0.55, 'throughput': 1094.70}
|
| 336 |
-
|
| 337 |
-
[INFO|callbacks.py:310] 2024-07-11 11:15:35,757 >> {'loss': 0.7110, 'learning_rate': 3.6667e-07, 'epoch': 0.57, 'throughput': 1095.09}
|
| 338 |
-
|
| 339 |
-
[INFO|callbacks.py:310] 2024-07-11 11:15:49,369 >> {'loss': 0.5127, 'learning_rate': 3.7500e-07, 'epoch': 0.58, 'throughput': 1095.93}
|
| 340 |
-
|
| 341 |
-
[INFO|callbacks.py:310] 2024-07-11 11:16:02,947 >> {'loss': 0.4143, 'learning_rate': 3.8333e-07, 'epoch': 0.59, 'throughput': 1095.72}
|
| 342 |
-
|
| 343 |
-
[INFO|callbacks.py:310] 2024-07-11 11:16:16,516 >> {'loss': 0.4014, 'learning_rate': 3.9167e-07, 'epoch': 0.60, 'throughput': 1096.29}
|
| 344 |
-
|
| 345 |
-
[INFO|callbacks.py:310] 2024-07-11 11:16:30,096 >> {'loss': 0.3840, 'learning_rate': 4.0000e-07, 'epoch': 0.62, 'throughput': 1096.57}
|
| 346 |
-
|
| 347 |
-
[INFO|callbacks.py:310] 2024-07-11 11:16:43,682 >> {'loss': 0.3017, 'learning_rate': 4.0833e-07, 'epoch': 0.63, 'throughput': 1098.22}
|
| 348 |
-
|
| 349 |
-
[INFO|callbacks.py:310] 2024-07-11 11:16:57,245 >> {'loss': 0.3031, 'learning_rate': 4.1667e-07, 'epoch': 0.64, 'throughput': 1099.04}
|
| 350 |
-
|
| 351 |
-
[INFO|callbacks.py:310] 2024-07-11 11:17:10,757 >> {'loss': 0.2818, 'learning_rate': 4.2500e-07, 'epoch': 0.66, 'throughput': 1098.90}
|
| 352 |
-
|
| 353 |
-
[INFO|callbacks.py:310] 2024-07-11 11:17:24,345 >> {'loss': 0.2881, 'learning_rate': 4.3333e-07, 'epoch': 0.67, 'throughput': 1098.82}
|
| 354 |
-
|
| 355 |
-
[INFO|callbacks.py:310] 2024-07-11 11:17:37,932 >> {'loss': 0.2943, 'learning_rate': 4.4167e-07, 'epoch': 0.68, 'throughput': 1099.73}
|
| 356 |
-
|
| 357 |
-
[INFO|callbacks.py:310] 2024-07-11 11:17:51,440 >> {'loss': 0.2781, 'learning_rate': 4.5000e-07, 'epoch': 0.69, 'throughput': 1098.98}
|
| 358 |
-
|
| 359 |
-
[INFO|callbacks.py:310] 2024-07-11 11:18:04,974 >> {'loss': 0.2724, 'learning_rate': 4.5833e-07, 'epoch': 0.71, 'throughput': 1099.88}
|
| 360 |
-
|
| 361 |
-
[INFO|callbacks.py:310] 2024-07-11 11:18:18,537 >> {'loss': 0.2520, 'learning_rate': 4.6667e-07, 'epoch': 0.72, 'throughput': 1099.29}
|
| 362 |
-
|
| 363 |
-
[INFO|callbacks.py:310] 2024-07-11 11:18:32,042 >> {'loss': 0.2482, 'learning_rate': 4.7500e-07, 'epoch': 0.73, 'throughput': 1099.87}
|
| 364 |
-
|
| 365 |
-
[INFO|callbacks.py:310] 2024-07-11 11:18:45,666 >> {'loss': 0.2212, 'learning_rate': 4.8333e-07, 'epoch': 0.75, 'throughput': 1100.79}
|
| 366 |
-
|
| 367 |
-
[INFO|callbacks.py:310] 2024-07-11 11:18:59,238 >> {'loss': 0.2408, 'learning_rate': 4.9167e-07, 'epoch': 0.76, 'throughput': 1100.40}
|
| 368 |
-
|
| 369 |
-
[INFO|callbacks.py:310] 2024-07-11 11:19:12,837 >> {'loss': 0.2133, 'learning_rate': 5.0000e-07, 'epoch': 0.77, 'throughput': 1100.76}
|
| 370 |
-
|
| 371 |
-
[INFO|callbacks.py:310] 2024-07-11 11:19:26,415 >> {'loss': 0.2319, 'learning_rate': 5.0833e-07, 'epoch': 0.78, 'throughput': 1101.14}
|
| 372 |
-
|
| 373 |
-
[INFO|callbacks.py:310] 2024-07-11 11:19:39,970 >> {'loss': 0.2703, 'learning_rate': 5.1667e-07, 'epoch': 0.80, 'throughput': 1100.64}
|
| 374 |
-
|
| 375 |
-
[INFO|callbacks.py:310] 2024-07-11 11:19:53,514 >> {'loss': 0.2647, 'learning_rate': 5.2500e-07, 'epoch': 0.81, 'throughput': 1101.50}
|
| 376 |
-
|
| 377 |
-
[INFO|callbacks.py:310] 2024-07-11 11:20:07,052 >> {'loss': 0.2288, 'learning_rate': 5.3333e-07, 'epoch': 0.82, 'throughput': 1100.95}
|
| 378 |
-
|
| 379 |
-
[INFO|callbacks.py:310] 2024-07-11 11:20:20,590 >> {'loss': 0.2426, 'learning_rate': 5.4167e-07, 'epoch': 0.84, 'throughput': 1101.61}
|
| 380 |
-
|
| 381 |
-
[INFO|callbacks.py:310] 2024-07-11 11:20:34,211 >> {'loss': 0.1936, 'learning_rate': 5.5000e-07, 'epoch': 0.85, 'throughput': 1102.11}
|
| 382 |
-
|
| 383 |
-
[INFO|callbacks.py:310] 2024-07-11 11:20:47,778 >> {'loss': 0.1920, 'learning_rate': 5.5833e-07, 'epoch': 0.86, 'throughput': 1102.83}
|
| 384 |
-
|
| 385 |
-
[INFO|callbacks.py:310] 2024-07-11 11:21:01,424 >> {'loss': 0.1956, 'learning_rate': 5.6667e-07, 'epoch': 0.87, 'throughput': 1102.10}
|
| 386 |
-
|
| 387 |
-
[INFO|callbacks.py:310] 2024-07-11 11:21:15,063 >> {'loss': 0.1862, 'learning_rate': 5.7500e-07, 'epoch': 0.89, 'throughput': 1102.18}
|
| 388 |
-
|
| 389 |
-
[INFO|callbacks.py:310] 2024-07-11 11:21:28,594 >> {'loss': 0.1950, 'learning_rate': 5.8333e-07, 'epoch': 0.90, 'throughput': 1101.34}
|
| 390 |
-
|
| 391 |
-
[INFO|callbacks.py:310] 2024-07-11 11:21:42,130 >> {'loss': 0.1971, 'learning_rate': 5.9167e-07, 'epoch': 0.91, 'throughput': 1101.47}
|
| 392 |
-
|
| 393 |
-
[INFO|callbacks.py:310] 2024-07-11 11:21:55,703 >> {'loss': 0.1683, 'learning_rate': 6.0000e-07, 'epoch': 0.93, 'throughput': 1101.79}
|
| 394 |
-
|
| 395 |
-
[INFO|callbacks.py:310] 2024-07-11 11:22:09,285 >> {'loss': 0.2003, 'learning_rate': 6.0833e-07, 'epoch': 0.94, 'throughput': 1101.80}
|
| 396 |
-
|
| 397 |
-
[INFO|callbacks.py:310] 2024-07-11 11:22:22,833 >> {'loss': 0.1543, 'learning_rate': 6.1667e-07, 'epoch': 0.95, 'throughput': 1101.69}
|
| 398 |
-
|
| 399 |
-
[INFO|callbacks.py:310] 2024-07-11 11:22:36,430 >> {'loss': 0.1982, 'learning_rate': 6.2500e-07, 'epoch': 0.96, 'throughput': 1101.60}
|
| 400 |
-
|
| 401 |
-
[INFO|callbacks.py:310] 2024-07-11 11:22:50,024 >> {'loss': 0.1545, 'learning_rate': 6.3333e-07, 'epoch': 0.98, 'throughput': 1101.78}
|
| 402 |
-
|
| 403 |
-
[INFO|callbacks.py:310] 2024-07-11 11:23:03,557 >> {'loss': 0.1573, 'learning_rate': 6.4167e-07, 'epoch': 0.99, 'throughput': 1101.66}
|
| 404 |
-
|
| 405 |
-
[INFO|callbacks.py:310] 2024-07-11 11:23:17,169 >> {'loss': 0.1788, 'learning_rate': 6.5000e-07, 'epoch': 1.00, 'throughput': 1102.22}
|
| 406 |
-
|
| 407 |
-
[INFO|callbacks.py:310] 2024-07-11 11:23:30,798 >> {'loss': 0.1870, 'learning_rate': 6.5833e-07, 'epoch': 1.02, 'throughput': 1102.50}
|
| 408 |
-
|
| 409 |
-
[INFO|callbacks.py:310] 2024-07-11 11:23:44,378 >> {'loss': 0.2009, 'learning_rate': 6.6667e-07, 'epoch': 1.03, 'throughput': 1101.97}
|
| 410 |
-
|
| 411 |
-
[INFO|callbacks.py:310] 2024-07-11 11:23:57,975 >> {'loss': 0.2655, 'learning_rate': 6.7500e-07, 'epoch': 1.04, 'throughput': 1102.17}
|
| 412 |
-
|
| 413 |
-
[INFO|callbacks.py:310] 2024-07-11 11:24:11,555 >> {'loss': 0.2041, 'learning_rate': 6.8333e-07, 'epoch': 1.05, 'throughput': 1102.46}
|
| 414 |
-
|
| 415 |
-
[INFO|callbacks.py:310] 2024-07-11 11:24:25,136 >> {'loss': 0.1675, 'learning_rate': 6.9167e-07, 'epoch': 1.07, 'throughput': 1102.33}
|
| 416 |
-
|
| 417 |
-
[INFO|callbacks.py:310] 2024-07-11 11:24:38,737 >> {'loss': 0.2097, 'learning_rate': 7.0000e-07, 'epoch': 1.08, 'throughput': 1102.93}
|
| 418 |
-
|
| 419 |
-
[INFO|callbacks.py:310] 2024-07-11 11:24:52,408 >> {'loss': 0.2171, 'learning_rate': 7.0833e-07, 'epoch': 1.09, 'throughput': 1103.25}
|
| 420 |
-
|
| 421 |
-
[INFO|callbacks.py:310] 2024-07-11 11:25:05,972 >> {'loss': 0.1702, 'learning_rate': 7.1667e-07, 'epoch': 1.11, 'throughput': 1103.45}
|
| 422 |
-
|
| 423 |
-
[INFO|callbacks.py:310] 2024-07-11 11:25:19,464 >> {'loss': 0.1255, 'learning_rate': 7.2500e-07, 'epoch': 1.12, 'throughput': 1102.85}
|
| 424 |
-
|
| 425 |
-
[INFO|callbacks.py:310] 2024-07-11 11:25:33,033 >> {'loss': 0.1826, 'learning_rate': 7.3333e-07, 'epoch': 1.13, 'throughput': 1103.48}
|
| 426 |
-
|
| 427 |
-
[INFO|callbacks.py:310] 2024-07-11 11:25:46,607 >> {'loss': 0.2039, 'learning_rate': 7.4167e-07, 'epoch': 1.14, 'throughput': 1103.34}
|
| 428 |
-
|
| 429 |
-
[INFO|callbacks.py:310] 2024-07-11 11:26:00,228 >> {'loss': 0.2337, 'learning_rate': 7.5000e-07, 'epoch': 1.16, 'throughput': 1103.79}
|
| 430 |
-
|
| 431 |
-
[INFO|callbacks.py:310] 2024-07-11 11:26:13,832 >> {'loss': 0.1466, 'learning_rate': 7.5833e-07, 'epoch': 1.17, 'throughput': 1103.97}
|
| 432 |
-
|
| 433 |
-
[INFO|callbacks.py:310] 2024-07-11 11:26:27,410 >> {'loss': 0.1295, 'learning_rate': 7.6667e-07, 'epoch': 1.18, 'throughput': 1104.29}
|
| 434 |
-
|
| 435 |
-
[INFO|callbacks.py:310] 2024-07-11 11:26:40,924 >> {'loss': 0.1525, 'learning_rate': 7.7500e-07, 'epoch': 1.20, 'throughput': 1104.03}
|
| 436 |
-
|
| 437 |
-
[INFO|callbacks.py:310] 2024-07-11 11:26:54,523 >> {'loss': 0.1735, 'learning_rate': 7.8333e-07, 'epoch': 1.21, 'throughput': 1104.42}
|
| 438 |
-
|
| 439 |
-
[INFO|callbacks.py:310] 2024-07-11 11:27:08,160 >> {'loss': 0.1484, 'learning_rate': 7.9167e-07, 'epoch': 1.22, 'throughput': 1105.59}
|
| 440 |
-
|
| 441 |
-
[INFO|callbacks.py:310] 2024-07-11 11:27:21,753 >> {'loss': 0.1517, 'learning_rate': 8.0000e-07, 'epoch': 1.23, 'throughput': 1105.48}
|
| 442 |
-
|
| 443 |
-
[INFO|callbacks.py:310] 2024-07-11 11:27:35,370 >> {'loss': 0.1429, 'learning_rate': 8.0833e-07, 'epoch': 1.25, 'throughput': 1105.30}
|
| 444 |
-
|
| 445 |
-
[INFO|callbacks.py:310] 2024-07-11 11:27:49,027 >> {'loss': 0.1510, 'learning_rate': 8.1667e-07, 'epoch': 1.26, 'throughput': 1105.57}
|
| 446 |
-
|
| 447 |
-
[INFO|callbacks.py:310] 2024-07-11 11:28:02,582 >> {'loss': 0.1217, 'learning_rate': 8.2500e-07, 'epoch': 1.27, 'throughput': 1105.56}
|
| 448 |
-
|
| 449 |
-
[INFO|callbacks.py:310] 2024-07-11 11:28:16,078 >> {'loss': 0.1366, 'learning_rate': 8.3333e-07, 'epoch': 1.29, 'throughput': 1104.97}
|
| 450 |
-
|
| 451 |
-
[INFO|callbacks.py:310] 2024-07-11 11:28:29,637 >> {'loss': 0.1534, 'learning_rate': 8.4167e-07, 'epoch': 1.30, 'throughput': 1105.43}
|
| 452 |
-
|
| 453 |
-
[INFO|callbacks.py:310] 2024-07-11 11:28:43,152 >> {'loss': 0.1410, 'learning_rate': 8.5000e-07, 'epoch': 1.31, 'throughput': 1105.25}
|
| 454 |
-
|
| 455 |
-
[INFO|callbacks.py:310] 2024-07-11 11:28:56,697 >> {'loss': 0.1238, 'learning_rate': 8.5833e-07, 'epoch': 1.32, 'throughput': 1104.87}
|
| 456 |
-
|
| 457 |
-
[INFO|callbacks.py:310] 2024-07-11 11:29:10,263 >> {'loss': 0.1241, 'learning_rate': 8.6667e-07, 'epoch': 1.34, 'throughput': 1104.63}
|
| 458 |
-
|
| 459 |
-
[INFO|callbacks.py:310] 2024-07-11 11:29:23,827 >> {'loss': 0.1414, 'learning_rate': 8.7500e-07, 'epoch': 1.35, 'throughput': 1104.27}
|
| 460 |
-
|
| 461 |
-
[INFO|callbacks.py:310] 2024-07-11 11:29:37,411 >> {'loss': 0.1296, 'learning_rate': 8.8333e-07, 'epoch': 1.36, 'throughput': 1104.25}
|
| 462 |
-
|
| 463 |
-
[INFO|callbacks.py:310] 2024-07-11 11:29:50,947 >> {'loss': 0.1232, 'learning_rate': 8.9167e-07, 'epoch': 1.38, 'throughput': 1104.12}
|
| 464 |
-
|
| 465 |
-
[INFO|callbacks.py:310] 2024-07-11 11:30:04,589 >> {'loss': 0.1625, 'learning_rate': 9.0000e-07, 'epoch': 1.39, 'throughput': 1104.23}
|
| 466 |
-
|
| 467 |
-
[INFO|callbacks.py:310] 2024-07-11 11:30:18,108 >> {'loss': 0.1509, 'learning_rate': 9.0833e-07, 'epoch': 1.40, 'throughput': 1104.50}
|
| 468 |
-
|
| 469 |
-
[INFO|callbacks.py:310] 2024-07-11 11:30:31,663 >> {'loss': 0.1416, 'learning_rate': 9.1667e-07, 'epoch': 1.41, 'throughput': 1104.70}
|
| 470 |
-
|
| 471 |
-
[INFO|callbacks.py:310] 2024-07-11 11:30:45,219 >> {'loss': 0.1481, 'learning_rate': 9.2500e-07, 'epoch': 1.43, 'throughput': 1104.91}
|
| 472 |
-
|
| 473 |
-
[INFO|callbacks.py:310] 2024-07-11 11:30:58,830 >> {'loss': 0.1303, 'learning_rate': 9.3333e-07, 'epoch': 1.44, 'throughput': 1104.87}
|
| 474 |
-
|
| 475 |
-
[INFO|callbacks.py:310] 2024-07-11 11:31:12,462 >> {'loss': 0.1160, 'learning_rate': 9.4167e-07, 'epoch': 1.45, 'throughput': 1104.77}
|
| 476 |
-
|
| 477 |
-
[INFO|callbacks.py:310] 2024-07-11 11:31:25,996 >> {'loss': 0.0981, 'learning_rate': 9.5000e-07, 'epoch': 1.47, 'throughput': 1104.48}
|
| 478 |
-
|
| 479 |
-
[INFO|callbacks.py:310] 2024-07-11 11:31:39,542 >> {'loss': 0.1174, 'learning_rate': 9.5833e-07, 'epoch': 1.48, 'throughput': 1104.16}
|
| 480 |
-
|
| 481 |
-
[INFO|callbacks.py:310] 2024-07-11 11:31:53,119 >> {'loss': 0.1458, 'learning_rate': 9.6667e-07, 'epoch': 1.49, 'throughput': 1103.74}
|
| 482 |
-
|
| 483 |
-
[INFO|callbacks.py:310] 2024-07-11 11:32:06,699 >> {'loss': 0.0952, 'learning_rate': 9.7500e-07, 'epoch': 1.50, 'throughput': 1103.76}
|
| 484 |
-
|
| 485 |
-
[INFO|callbacks.py:310] 2024-07-11 11:32:20,301 >> {'loss': 0.1233, 'learning_rate': 9.8333e-07, 'epoch': 1.52, 'throughput': 1103.43}
|
| 486 |
-
|
| 487 |
-
[INFO|callbacks.py:310] 2024-07-11 11:32:33,923 >> {'loss': 0.1270, 'learning_rate': 9.9167e-07, 'epoch': 1.53, 'throughput': 1103.60}
|
| 488 |
-
|
| 489 |
-
[INFO|callbacks.py:310] 2024-07-11 11:32:47,494 >> {'loss': 0.1121, 'learning_rate': 1.0000e-06, 'epoch': 1.54, 'throughput': 1103.79}
|
| 490 |
-
|
| 491 |
-
[INFO|callbacks.py:310] 2024-07-11 11:33:01,024 >> {'loss': 0.1432, 'learning_rate': 1.0083e-06, 'epoch': 1.56, 'throughput': 1103.55}
|
| 492 |
-
|
| 493 |
-
[INFO|callbacks.py:310] 2024-07-11 11:33:14,596 >> {'loss': 0.1446, 'learning_rate': 1.0167e-06, 'epoch': 1.57, 'throughput': 1103.53}
|
| 494 |
-
|
| 495 |
-
[INFO|callbacks.py:310] 2024-07-11 11:33:28,145 >> {'loss': 0.1056, 'learning_rate': 1.0250e-06, 'epoch': 1.58, 'throughput': 1103.73}
|
| 496 |
-
|
| 497 |
-
[INFO|callbacks.py:310] 2024-07-11 11:33:41,757 >> {'loss': 0.1193, 'learning_rate': 1.0333e-06, 'epoch': 1.59, 'throughput': 1103.80}
|
| 498 |
-
|
| 499 |
-
[INFO|callbacks.py:310] 2024-07-11 11:33:55,372 >> {'loss': 0.1409, 'learning_rate': 1.0417e-06, 'epoch': 1.61, 'throughput': 1104.18}
|
| 500 |
-
|
| 501 |
-
[INFO|callbacks.py:310] 2024-07-11 11:34:08,971 >> {'loss': 0.1154, 'learning_rate': 1.0500e-06, 'epoch': 1.62, 'throughput': 1104.44}
|
| 502 |
-
|
| 503 |
-
[INFO|callbacks.py:310] 2024-07-11 11:34:22,537 >> {'loss': 0.1046, 'learning_rate': 1.0583e-06, 'epoch': 1.63, 'throughput': 1104.66}
|
| 504 |
-
|
| 505 |
-
[INFO|callbacks.py:310] 2024-07-11 11:34:36,093 >> {'loss': 0.0900, 'learning_rate': 1.0667e-06, 'epoch': 1.65, 'throughput': 1104.90}
|
| 506 |
-
|
| 507 |
-
[INFO|callbacks.py:310] 2024-07-11 11:34:49,618 >> {'loss': 0.0858, 'learning_rate': 1.0750e-06, 'epoch': 1.66, 'throughput': 1104.55}
|
| 508 |
-
|
| 509 |
-
[INFO|callbacks.py:310] 2024-07-11 11:35:03,212 >> {'loss': 0.0782, 'learning_rate': 1.0833e-06, 'epoch': 1.67, 'throughput': 1104.82}
|
| 510 |
-
|
| 511 |
-
[INFO|callbacks.py:310] 2024-07-11 11:35:16,738 >> {'loss': 0.1429, 'learning_rate': 1.0917e-06, 'epoch': 1.68, 'throughput': 1104.80}
|
| 512 |
-
|
| 513 |
-
[INFO|callbacks.py:310] 2024-07-11 11:35:30,359 >> {'loss': 0.1121, 'learning_rate': 1.1000e-06, 'epoch': 1.70, 'throughput': 1104.72}
|
| 514 |
-
|
| 515 |
-
[INFO|callbacks.py:310] 2024-07-11 11:35:43,915 >> {'loss': 0.0458, 'learning_rate': 1.1083e-06, 'epoch': 1.71, 'throughput': 1104.76}
|
| 516 |
-
|
| 517 |
-
[INFO|callbacks.py:310] 2024-07-11 11:35:57,555 >> {'loss': 0.1217, 'learning_rate': 1.1167e-06, 'epoch': 1.72, 'throughput': 1104.43}
|
| 518 |
-
|
| 519 |
-
[INFO|callbacks.py:310] 2024-07-11 11:36:11,110 >> {'loss': 0.1253, 'learning_rate': 1.1250e-06, 'epoch': 1.74, 'throughput': 1104.59}
|
| 520 |
-
|
| 521 |
-
[INFO|callbacks.py:310] 2024-07-11 11:36:24,626 >> {'loss': 0.0770, 'learning_rate': 1.1333e-06, 'epoch': 1.75, 'throughput': 1104.56}
|
| 522 |
-
|
| 523 |
-
[INFO|callbacks.py:310] 2024-07-11 11:36:38,205 >> {'loss': 0.0719, 'learning_rate': 1.1417e-06, 'epoch': 1.76, 'throughput': 1104.75}
|
| 524 |
-
|
| 525 |
-
[INFO|callbacks.py:310] 2024-07-11 11:36:51,771 >> {'loss': 0.0916, 'learning_rate': 1.1500e-06, 'epoch': 1.77, 'throughput': 1104.65}
|
| 526 |
-
|
| 527 |
-
[INFO|callbacks.py:310] 2024-07-11 11:37:05,307 >> {'loss': 0.0812, 'learning_rate': 1.1583e-06, 'epoch': 1.79, 'throughput': 1104.64}
|
| 528 |
-
|
| 529 |
-
[INFO|callbacks.py:310] 2024-07-11 11:37:18,865 >> {'loss': 0.1176, 'learning_rate': 1.1667e-06, 'epoch': 1.80, 'throughput': 1104.64}
|
| 530 |
-
|
| 531 |
-
[INFO|callbacks.py:310] 2024-07-11 11:37:32,443 >> {'loss': 0.0631, 'learning_rate': 1.1750e-06, 'epoch': 1.81, 'throughput': 1104.63}
|
| 532 |
-
|
| 533 |
-
[INFO|callbacks.py:310] 2024-07-11 11:37:45,990 >> {'loss': 0.1137, 'learning_rate': 1.1833e-06, 'epoch': 1.83, 'throughput': 1104.44}
|
| 534 |
-
|
| 535 |
-
[INFO|callbacks.py:310] 2024-07-11 11:37:59,512 >> {'loss': 0.0958, 'learning_rate': 1.1917e-06, 'epoch': 1.84, 'throughput': 1104.29}
|
| 536 |
-
|
| 537 |
-
[INFO|callbacks.py:310] 2024-07-11 11:38:13,063 >> {'loss': 0.1343, 'learning_rate': 1.2000e-06, 'epoch': 1.85, 'throughput': 1104.23}
|
| 538 |
-
|
| 539 |
-
[INFO|callbacks.py:310] 2024-07-11 11:38:26,593 >> {'loss': 0.1101, 'learning_rate': 1.2083e-06, 'epoch': 1.86, 'throughput': 1103.88}
|
| 540 |
-
|
| 541 |
-
[INFO|callbacks.py:310] 2024-07-11 11:38:40,125 >> {'loss': 0.0914, 'learning_rate': 1.2167e-06, 'epoch': 1.88, 'throughput': 1103.82}
|
| 542 |
-
|
| 543 |
-
[INFO|callbacks.py:310] 2024-07-11 11:38:53,738 >> {'loss': 0.1114, 'learning_rate': 1.2250e-06, 'epoch': 1.89, 'throughput': 1103.91}
|
| 544 |
-
|
| 545 |
-
[INFO|callbacks.py:310] 2024-07-11 11:39:07,254 >> {'loss': 0.0830, 'learning_rate': 1.2333e-06, 'epoch': 1.90, 'throughput': 1103.76}
|
| 546 |
-
|
| 547 |
-
[INFO|callbacks.py:310] 2024-07-11 11:39:20,835 >> {'loss': 0.1095, 'learning_rate': 1.2417e-06, 'epoch': 1.92, 'throughput': 1104.03}
|
| 548 |
-
|
| 549 |
-
[INFO|callbacks.py:310] 2024-07-11 11:39:34,388 >> {'loss': 0.0662, 'learning_rate': 1.2500e-06, 'epoch': 1.93, 'throughput': 1104.06}
|
| 550 |
-
|
| 551 |
-
[INFO|callbacks.py:310] 2024-07-11 11:39:47,946 >> {'loss': 0.0979, 'learning_rate': 1.2583e-06, 'epoch': 1.94, 'throughput': 1104.54}
|
| 552 |
-
|
| 553 |
-
[INFO|callbacks.py:310] 2024-07-11 11:40:01,559 >> {'loss': 0.0847, 'learning_rate': 1.2667e-06, 'epoch': 1.95, 'throughput': 1104.58}
|
| 554 |
-
|
| 555 |
-
[INFO|callbacks.py:310] 2024-07-11 11:40:15,082 >> {'loss': 0.0949, 'learning_rate': 1.2750e-06, 'epoch': 1.97, 'throughput': 1104.06}
|
| 556 |
-
|
| 557 |
-
[INFO|callbacks.py:310] 2024-07-11 11:40:28,684 >> {'loss': 0.1206, 'learning_rate': 1.2833e-06, 'epoch': 1.98, 'throughput': 1103.69}
|
| 558 |
-
|
| 559 |
-
[INFO|callbacks.py:310] 2024-07-11 11:40:42,245 >> {'loss': 0.1390, 'learning_rate': 1.2917e-06, 'epoch': 1.99, 'throughput': 1103.92}
|
| 560 |
-
|
| 561 |
-
[INFO|callbacks.py:310] 2024-07-11 11:40:55,833 >> {'loss': 0.0700, 'learning_rate': 1.3000e-06, 'epoch': 2.01, 'throughput': 1103.71}
|
| 562 |
-
|
| 563 |
-
[INFO|callbacks.py:310] 2024-07-11 11:41:09,423 >> {'loss': 0.0562, 'learning_rate': 1.3083e-06, 'epoch': 2.02, 'throughput': 1103.62}
|
| 564 |
-
|
| 565 |
-
[INFO|callbacks.py:310] 2024-07-11 11:41:23,078 >> {'loss': 0.0456, 'learning_rate': 1.3167e-06, 'epoch': 2.03, 'throughput': 1103.51}
|
| 566 |
-
|
| 567 |
-
[INFO|callbacks.py:310] 2024-07-11 11:41:36,635 >> {'loss': 0.0582, 'learning_rate': 1.3250e-06, 'epoch': 2.05, 'throughput': 1103.66}
|
| 568 |
-
|
| 569 |
-
[INFO|callbacks.py:310] 2024-07-11 11:41:50,227 >> {'loss': 0.0452, 'learning_rate': 1.3333e-06, 'epoch': 2.06, 'throughput': 1103.37}
|
| 570 |
-
|
| 571 |
-
[INFO|callbacks.py:310] 2024-07-11 11:42:03,798 >> {'loss': 0.0553, 'learning_rate': 1.3417e-06, 'epoch': 2.07, 'throughput': 1103.25}
|
| 572 |
-
|
| 573 |
-
[INFO|callbacks.py:310] 2024-07-11 11:42:17,344 >> {'loss': 0.1108, 'learning_rate': 1.3500e-06, 'epoch': 2.08, 'throughput': 1103.01}
|
| 574 |
-
|
| 575 |
-
[INFO|callbacks.py:310] 2024-07-11 11:42:30,912 >> {'loss': 0.0791, 'learning_rate': 1.3583e-06, 'epoch': 2.10, 'throughput': 1102.88}
|
| 576 |
-
|
| 577 |
-
[INFO|callbacks.py:310] 2024-07-11 11:42:44,458 >> {'loss': 0.0637, 'learning_rate': 1.3667e-06, 'epoch': 2.11, 'throughput': 1102.77}
|
| 578 |
-
|
| 579 |
-
[INFO|callbacks.py:310] 2024-07-11 11:42:57,976 >> {'loss': 0.0404, 'learning_rate': 1.3750e-06, 'epoch': 2.12, 'throughput': 1102.66}
|
| 580 |
-
|
| 581 |
-
[INFO|callbacks.py:310] 2024-07-11 11:43:11,545 >> {'loss': 0.0372, 'learning_rate': 1.3833e-06, 'epoch': 2.14, 'throughput': 1102.62}
|
| 582 |
-
|
| 583 |
-
[INFO|callbacks.py:310] 2024-07-11 11:43:25,078 >> {'loss': 0.0450, 'learning_rate': 1.3917e-06, 'epoch': 2.15, 'throughput': 1102.43}
|
| 584 |
-
|
| 585 |
-
[INFO|callbacks.py:310] 2024-07-11 11:43:38,684 >> {'loss': 0.0964, 'learning_rate': 1.4000e-06, 'epoch': 2.16, 'throughput': 1102.42}
|
| 586 |
-
|
| 587 |
-
[INFO|callbacks.py:310] 2024-07-11 11:43:52,267 >> {'loss': 0.0543, 'learning_rate': 1.4083e-06, 'epoch': 2.17, 'throughput': 1102.35}
|
| 588 |
-
|
| 589 |
-
[INFO|callbacks.py:310] 2024-07-11 11:44:05,854 >> {'loss': 0.0710, 'learning_rate': 1.4167e-06, 'epoch': 2.19, 'throughput': 1102.39}
|
| 590 |
-
|
| 591 |
-
[INFO|callbacks.py:310] 2024-07-11 11:44:19,373 >> {'loss': 0.0285, 'learning_rate': 1.4250e-06, 'epoch': 2.20, 'throughput': 1102.26}
|
| 592 |
-
|
| 593 |
-
[INFO|callbacks.py:310] 2024-07-11 11:44:32,944 >> {'loss': 0.0399, 'learning_rate': 1.4333e-06, 'epoch': 2.21, 'throughput': 1102.32}
|
| 594 |
-
|
| 595 |
-
[INFO|callbacks.py:310] 2024-07-11 11:44:46,571 >> {'loss': 0.0670, 'learning_rate': 1.4417e-06, 'epoch': 2.23, 'throughput': 1102.74}
|
| 596 |
-
|
| 597 |
-
[INFO|callbacks.py:310] 2024-07-11 11:45:00,151 >> {'loss': 0.0436, 'learning_rate': 1.4500e-06, 'epoch': 2.24, 'throughput': 1102.78}
|
| 598 |
-
|
| 599 |
-
[INFO|callbacks.py:310] 2024-07-11 11:45:13,718 >> {'loss': 0.0522, 'learning_rate': 1.4583e-06, 'epoch': 2.25, 'throughput': 1102.75}
|
| 600 |
-
|
| 601 |
-
[INFO|callbacks.py:310] 2024-07-11 11:45:27,246 >> {'loss': 0.0521, 'learning_rate': 1.4667e-06, 'epoch': 2.26, 'throughput': 1102.78}
|
| 602 |
-
|
| 603 |
-
[INFO|callbacks.py:310] 2024-07-11 11:45:40,843 >> {'loss': 0.0446, 'learning_rate': 1.4750e-06, 'epoch': 2.28, 'throughput': 1102.60}
|
| 604 |
-
|
| 605 |
-
[INFO|callbacks.py:310] 2024-07-11 11:45:54,432 >> {'loss': 0.0378, 'learning_rate': 1.4833e-06, 'epoch': 2.29, 'throughput': 1102.76}
|
| 606 |
-
|
| 607 |
-
[INFO|callbacks.py:310] 2024-07-11 11:46:08,019 >> {'loss': 0.0387, 'learning_rate': 1.4917e-06, 'epoch': 2.30, 'throughput': 1102.37}
|
| 608 |
-
|
| 609 |
-
[INFO|callbacks.py:310] 2024-07-11 11:46:21,587 >> {'loss': 0.0360, 'learning_rate': 1.5000e-06, 'epoch': 2.32, 'throughput': 1102.33}
|
| 610 |
-
|
| 611 |
-
[INFO|callbacks.py:310] 2024-07-11 11:46:35,138 >> {'loss': 0.0765, 'learning_rate': 1.5083e-06, 'epoch': 2.33, 'throughput': 1102.23}
|
| 612 |
-
|
| 613 |
-
[INFO|callbacks.py:310] 2024-07-11 11:46:48,709 >> {'loss': 0.0884, 'learning_rate': 1.5167e-06, 'epoch': 2.34, 'throughput': 1102.34}
|
| 614 |
-
|
| 615 |
-
[INFO|callbacks.py:310] 2024-07-11 11:47:02,246 >> {'loss': 0.0801, 'learning_rate': 1.5250e-06, 'epoch': 2.35, 'throughput': 1102.13}
|
| 616 |
-
|
| 617 |
-
[INFO|callbacks.py:310] 2024-07-11 11:47:15,861 >> {'loss': 0.0276, 'learning_rate': 1.5333e-06, 'epoch': 2.37, 'throughput': 1102.42}
|
| 618 |
-
|
| 619 |
-
[INFO|callbacks.py:310] 2024-07-11 11:47:29,458 >> {'loss': 0.0778, 'learning_rate': 1.5417e-06, 'epoch': 2.38, 'throughput': 1102.27}
|
| 620 |
-
|
| 621 |
-
[INFO|callbacks.py:310] 2024-07-11 11:47:42,996 >> {'loss': 0.0726, 'learning_rate': 1.5500e-06, 'epoch': 2.39, 'throughput': 1102.38}
|
| 622 |
-
|
| 623 |
-
[INFO|callbacks.py:310] 2024-07-11 11:47:56,560 >> {'loss': 0.1381, 'learning_rate': 1.5583e-06, 'epoch': 2.41, 'throughput': 1102.23}
|
| 624 |
-
|
| 625 |
-
[INFO|callbacks.py:310] 2024-07-11 11:48:10,123 >> {'loss': 0.0408, 'learning_rate': 1.5667e-06, 'epoch': 2.42, 'throughput': 1102.37}
|
| 626 |
-
|
| 627 |
-
[INFO|callbacks.py:310] 2024-07-11 11:48:23,689 >> {'loss': 0.1066, 'learning_rate': 1.5750e-06, 'epoch': 2.43, 'throughput': 1102.60}
|
| 628 |
-
|
| 629 |
-
[INFO|callbacks.py:310] 2024-07-11 11:48:37,250 >> {'loss': 0.0686, 'learning_rate': 1.5833e-06, 'epoch': 2.44, 'throughput': 1102.78}
|
| 630 |
-
|
| 631 |
-
[INFO|callbacks.py:310] 2024-07-11 11:48:50,791 >> {'loss': 0.0428, 'learning_rate': 1.5917e-06, 'epoch': 2.46, 'throughput': 1102.86}
|
| 632 |
-
|
| 633 |
-
[INFO|callbacks.py:310] 2024-07-11 11:49:04,395 >> {'loss': 0.0387, 'learning_rate': 1.6000e-06, 'epoch': 2.47, 'throughput': 1102.85}
|
| 634 |
-
|
| 635 |
-
[INFO|callbacks.py:310] 2024-07-11 11:49:17,985 >> {'loss': 0.0489, 'learning_rate': 1.6083e-06, 'epoch': 2.48, 'throughput': 1102.92}
|
| 636 |
-
|
| 637 |
-
[INFO|callbacks.py:310] 2024-07-11 11:49:31,560 >> {'loss': 0.0621, 'learning_rate': 1.6167e-06, 'epoch': 2.50, 'throughput': 1102.86}
|
| 638 |
-
|
| 639 |
-
[INFO|callbacks.py:310] 2024-07-11 11:49:45,187 >> {'loss': 0.0651, 'learning_rate': 1.6250e-06, 'epoch': 2.51, 'throughput': 1102.76}
|
| 640 |
-
|
| 641 |
-
[INFO|callbacks.py:310] 2024-07-11 11:49:58,749 >> {'loss': 0.0398, 'learning_rate': 1.6333e-06, 'epoch': 2.52, 'throughput': 1102.95}
|
| 642 |
-
|
| 643 |
-
[INFO|callbacks.py:310] 2024-07-11 11:50:12,256 >> {'loss': 0.0369, 'learning_rate': 1.6417e-06, 'epoch': 2.53, 'throughput': 1102.84}
|
| 644 |
-
|
| 645 |
-
[INFO|callbacks.py:310] 2024-07-11 11:50:25,823 >> {'loss': 0.0582, 'learning_rate': 1.6500e-06, 'epoch': 2.55, 'throughput': 1102.81}
|
| 646 |
-
|
| 647 |
-
[INFO|callbacks.py:310] 2024-07-11 11:50:39,396 >> {'loss': 0.0479, 'learning_rate': 1.6583e-06, 'epoch': 2.56, 'throughput': 1102.76}
|
| 648 |
-
|
| 649 |
-
[INFO|callbacks.py:310] 2024-07-11 11:50:52,966 >> {'loss': 0.0561, 'learning_rate': 1.6667e-06, 'epoch': 2.57, 'throughput': 1102.65}
|
| 650 |
-
|
| 651 |
-
[INFO|callbacks.py:310] 2024-07-11 11:51:06,538 >> {'loss': 0.0497, 'learning_rate': 1.6750e-06, 'epoch': 2.59, 'throughput': 1102.71}
|
| 652 |
-
|
| 653 |
-
[INFO|callbacks.py:310] 2024-07-11 11:51:20,116 >> {'loss': 0.0630, 'learning_rate': 1.6833e-06, 'epoch': 2.60, 'throughput': 1102.74}
|
| 654 |
-
|
| 655 |
-
[INFO|callbacks.py:310] 2024-07-11 11:51:33,695 >> {'loss': 0.0540, 'learning_rate': 1.6917e-06, 'epoch': 2.61, 'throughput': 1102.72}
|
| 656 |
-
|
| 657 |
-
[INFO|callbacks.py:310] 2024-07-11 11:51:47,299 >> {'loss': 0.0729, 'learning_rate': 1.7000e-06, 'epoch': 2.62, 'throughput': 1102.94}
|
| 658 |
-
|
| 659 |
-
[INFO|callbacks.py:310] 2024-07-11 11:52:00,917 >> {'loss': 0.0685, 'learning_rate': 1.7083e-06, 'epoch': 2.64, 'throughput': 1103.02}
|
| 660 |
-
|
| 661 |
-
[INFO|callbacks.py:310] 2024-07-11 11:52:14,561 >> {'loss': 0.0823, 'learning_rate': 1.7167e-06, 'epoch': 2.65, 'throughput': 1103.02}
|
| 662 |
-
|
| 663 |
-
[INFO|callbacks.py:310] 2024-07-11 11:52:28,087 >> {'loss': 0.0322, 'learning_rate': 1.7250e-06, 'epoch': 2.66, 'throughput': 1102.86}
|
| 664 |
-
|
| 665 |
-
[INFO|callbacks.py:310] 2024-07-11 11:52:41,692 >> {'loss': 0.0821, 'learning_rate': 1.7333e-06, 'epoch': 2.68, 'throughput': 1103.07}
|
| 666 |
-
|
| 667 |
-
[INFO|callbacks.py:310] 2024-07-11 11:52:55,309 >> {'loss': 0.0561, 'learning_rate': 1.7417e-06, 'epoch': 2.69, 'throughput': 1103.13}
|
| 668 |
-
|
| 669 |
-
[INFO|callbacks.py:310] 2024-07-11 11:53:08,887 >> {'loss': 0.0468, 'learning_rate': 1.7500e-06, 'epoch': 2.70, 'throughput': 1103.09}
|
| 670 |
-
|
| 671 |
-
[INFO|callbacks.py:310] 2024-07-11 11:53:22,397 >> {'loss': 0.0593, 'learning_rate': 1.7583e-06, 'epoch': 2.71, 'throughput': 1103.31}
|
| 672 |
-
|
| 673 |
-
[INFO|callbacks.py:310] 2024-07-11 11:53:35,991 >> {'loss': 0.0403, 'learning_rate': 1.7667e-06, 'epoch': 2.73, 'throughput': 1103.22}
|
| 674 |
-
|
| 675 |
-
[INFO|callbacks.py:310] 2024-07-11 11:53:49,564 >> {'loss': 0.0459, 'learning_rate': 1.7750e-06, 'epoch': 2.74, 'throughput': 1103.05}
|
| 676 |
-
|
| 677 |
-
[INFO|callbacks.py:310] 2024-07-11 11:54:03,161 >> {'loss': 0.0509, 'learning_rate': 1.7833e-06, 'epoch': 2.75, 'throughput': 1102.91}
|
| 678 |
-
|
| 679 |
-
[INFO|callbacks.py:310] 2024-07-11 11:54:16,779 >> {'loss': 0.0873, 'learning_rate': 1.7917e-06, 'epoch': 2.77, 'throughput': 1103.07}
|
| 680 |
-
|
| 681 |
-
[INFO|callbacks.py:310] 2024-07-11 11:54:30,334 >> {'loss': 0.0618, 'learning_rate': 1.8000e-06, 'epoch': 2.78, 'throughput': 1103.10}
|
| 682 |
-
|
| 683 |
-
[INFO|callbacks.py:310] 2024-07-11 11:54:43,886 >> {'loss': 0.1025, 'learning_rate': 1.8083e-06, 'epoch': 2.79, 'throughput': 1102.80}
|
| 684 |
-
|
| 685 |
-
[INFO|callbacks.py:310] 2024-07-11 11:54:57,475 >> {'loss': 0.0669, 'learning_rate': 1.8167e-06, 'epoch': 2.80, 'throughput': 1102.74}
|
| 686 |
-
|
| 687 |
-
[INFO|callbacks.py:310] 2024-07-11 11:55:11,084 >> {'loss': 0.0447, 'learning_rate': 1.8250e-06, 'epoch': 2.82, 'throughput': 1102.58}
|
| 688 |
-
|
| 689 |
-
[INFO|callbacks.py:310] 2024-07-11 11:55:24,676 >> {'loss': 0.0635, 'learning_rate': 1.8333e-06, 'epoch': 2.83, 'throughput': 1102.68}
|
| 690 |
-
|
| 691 |
-
[INFO|callbacks.py:310] 2024-07-11 11:55:38,261 >> {'loss': 0.0614, 'learning_rate': 1.8417e-06, 'epoch': 2.84, 'throughput': 1102.57}
|
| 692 |
-
|
| 693 |
-
[INFO|callbacks.py:310] 2024-07-11 11:55:51,820 >> {'loss': 0.0646, 'learning_rate': 1.8500e-06, 'epoch': 2.86, 'throughput': 1102.56}
|
| 694 |
-
|
| 695 |
-
[INFO|callbacks.py:310] 2024-07-11 11:56:05,370 >> {'loss': 0.0893, 'learning_rate': 1.8583e-06, 'epoch': 2.87, 'throughput': 1102.76}
|
| 696 |
-
|
| 697 |
-
[INFO|callbacks.py:310] 2024-07-11 11:56:18,931 >> {'loss': 0.0653, 'learning_rate': 1.8667e-06, 'epoch': 2.88, 'throughput': 1102.83}
|
| 698 |
-
|
| 699 |
-
[INFO|callbacks.py:310] 2024-07-11 11:56:32,505 >> {'loss': 0.0402, 'learning_rate': 1.8750e-06, 'epoch': 2.89, 'throughput': 1102.96}
|
| 700 |
-
|
| 701 |
-
[INFO|callbacks.py:310] 2024-07-11 11:56:46,074 >> {'loss': 0.0407, 'learning_rate': 1.8833e-06, 'epoch': 2.91, 'throughput': 1103.17}
|
| 702 |
-
|
| 703 |
-
[INFO|callbacks.py:310] 2024-07-11 11:56:59,621 >> {'loss': 0.0949, 'learning_rate': 1.8917e-06, 'epoch': 2.92, 'throughput': 1103.04}
|
| 704 |
-
|
| 705 |
-
[INFO|callbacks.py:310] 2024-07-11 11:57:13,230 >> {'loss': 0.0789, 'learning_rate': 1.9000e-06, 'epoch': 2.93, 'throughput': 1103.14}
|
| 706 |
-
|
| 707 |
-
[INFO|callbacks.py:310] 2024-07-11 11:57:26,812 >> {'loss': 0.0438, 'learning_rate': 1.9083e-06, 'epoch': 2.95, 'throughput': 1103.18}
|
| 708 |
-
|
| 709 |
-
[INFO|callbacks.py:310] 2024-07-11 11:57:40,386 >> {'loss': 0.0905, 'learning_rate': 1.9167e-06, 'epoch': 2.96, 'throughput': 1103.48}
|
| 710 |
-
|
| 711 |
-
[INFO|callbacks.py:310] 2024-07-11 11:57:53,898 >> {'loss': 0.0495, 'learning_rate': 1.9250e-06, 'epoch': 2.97, 'throughput': 1103.43}
|
| 712 |
-
|
| 713 |
-
[INFO|callbacks.py:310] 2024-07-11 11:58:07,535 >> {'loss': 0.0642, 'learning_rate': 1.9333e-06, 'epoch': 2.98, 'throughput': 1103.64}
|
| 714 |
-
|
| 715 |
-
[INFO|callbacks.py:310] 2024-07-11 11:58:21,083 >> {'loss': 0.0607, 'learning_rate': 1.9417e-06, 'epoch': 3.00, 'throughput': 1103.60}
|
| 716 |
-
|
| 717 |
-
[INFO|callbacks.py:310] 2024-07-11 11:58:34,682 >> {'loss': 0.0363, 'learning_rate': 1.9500e-06, 'epoch': 3.01, 'throughput': 1103.54}
|
| 718 |
-
|
| 719 |
-
[INFO|callbacks.py:310] 2024-07-11 11:58:48,313 >> {'loss': 0.0492, 'learning_rate': 1.9583e-06, 'epoch': 3.02, 'throughput': 1103.42}
|
| 720 |
-
|
| 721 |
-
[INFO|callbacks.py:310] 2024-07-11 11:59:01,897 >> {'loss': 0.0373, 'learning_rate': 1.9667e-06, 'epoch': 3.04, 'throughput': 1103.50}
|
| 722 |
-
|
| 723 |
-
[INFO|callbacks.py:310] 2024-07-11 11:59:15,458 >> {'loss': 0.0203, 'learning_rate': 1.9750e-06, 'epoch': 3.05, 'throughput': 1103.35}
|
| 724 |
-
|
| 725 |
-
[INFO|callbacks.py:310] 2024-07-11 11:59:28,966 >> {'loss': 0.0175, 'learning_rate': 1.9833e-06, 'epoch': 3.06, 'throughput': 1103.44}
|
| 726 |
-
|
| 727 |
-
[INFO|callbacks.py:310] 2024-07-11 11:59:42,557 >> {'loss': 0.0497, 'learning_rate': 1.9917e-06, 'epoch': 3.07, 'throughput': 1103.62}
|
| 728 |
-
|
| 729 |
-
[INFO|callbacks.py:310] 2024-07-11 11:59:56,112 >> {'loss': 0.0361, 'learning_rate': 2.0000e-06, 'epoch': 3.09, 'throughput': 1103.61}
|
| 730 |
-
|
| 731 |
-
[INFO|callbacks.py:310] 2024-07-11 12:00:09,694 >> {'loss': 0.0193, 'learning_rate': 2.0083e-06, 'epoch': 3.10, 'throughput': 1103.63}
|
| 732 |
-
|
| 733 |
-
[INFO|callbacks.py:310] 2024-07-11 12:00:23,246 >> {'loss': 0.0142, 'learning_rate': 2.0167e-06, 'epoch': 3.11, 'throughput': 1103.37}
|
| 734 |
-
|
| 735 |
-
[INFO|callbacks.py:310] 2024-07-11 12:00:36,823 >> {'loss': 0.0415, 'learning_rate': 2.0250e-06, 'epoch': 3.13, 'throughput': 1103.30}
|
| 736 |
-
|
| 737 |
-
[INFO|callbacks.py:310] 2024-07-11 12:00:50,432 >> {'loss': 0.0178, 'learning_rate': 2.0333e-06, 'epoch': 3.14, 'throughput': 1103.37}
|
| 738 |
-
|
| 739 |
-
[INFO|callbacks.py:310] 2024-07-11 12:01:03,966 >> {'loss': 0.0166, 'learning_rate': 2.0417e-06, 'epoch': 3.15, 'throughput': 1103.51}
|
| 740 |
-
|
| 741 |
-
[INFO|callbacks.py:310] 2024-07-11 12:01:17,574 >> {'loss': 0.0424, 'learning_rate': 2.0500e-06, 'epoch': 3.16, 'throughput': 1103.59}
|
| 742 |
-
|
| 743 |
-
[INFO|callbacks.py:310] 2024-07-11 12:01:31,112 >> {'loss': 0.0464, 'learning_rate': 2.0583e-06, 'epoch': 3.18, 'throughput': 1103.67}
|
| 744 |
-
|
| 745 |
-
[INFO|callbacks.py:310] 2024-07-11 12:01:44,672 >> {'loss': 0.0235, 'learning_rate': 2.0667e-06, 'epoch': 3.19, 'throughput': 1103.53}
|
| 746 |
-
|
| 747 |
-
[INFO|callbacks.py:310] 2024-07-11 12:01:58,233 >> {'loss': 0.0128, 'learning_rate': 2.0750e-06, 'epoch': 3.20, 'throughput': 1103.41}
|
| 748 |
-
|
| 749 |
-
[INFO|callbacks.py:310] 2024-07-11 12:02:11,831 >> {'loss': 0.0319, 'learning_rate': 2.0833e-06, 'epoch': 3.22, 'throughput': 1103.70}
|
| 750 |
-
|
| 751 |
-
[INFO|callbacks.py:310] 2024-07-11 12:02:25,358 >> {'loss': 0.0196, 'learning_rate': 2.0917e-06, 'epoch': 3.23, 'throughput': 1103.57}
|
| 752 |
-
|
| 753 |
-
[INFO|callbacks.py:310] 2024-07-11 12:02:38,937 >> {'loss': 0.0326, 'learning_rate': 2.1000e-06, 'epoch': 3.24, 'throughput': 1103.58}
|
| 754 |
-
|
| 755 |
-
[INFO|callbacks.py:310] 2024-07-11 12:02:52,507 >> {'loss': 0.0170, 'learning_rate': 2.1083e-06, 'epoch': 3.25, 'throughput': 1103.43}
|
| 756 |
-
|
| 757 |
-
[INFO|callbacks.py:310] 2024-07-11 12:03:06,088 >> {'loss': 0.0372, 'learning_rate': 2.1167e-06, 'epoch': 3.27, 'throughput': 1103.48}
|
| 758 |
-
|
| 759 |
-
[INFO|callbacks.py:310] 2024-07-11 12:03:19,699 >> {'loss': 0.0165, 'learning_rate': 2.1250e-06, 'epoch': 3.28, 'throughput': 1103.48}
|
| 760 |
-
|
| 761 |
-
[INFO|callbacks.py:310] 2024-07-11 12:03:33,259 >> {'loss': 0.0142, 'learning_rate': 2.1333e-06, 'epoch': 3.29, 'throughput': 1103.67}
|
| 762 |
-
|
| 763 |
-
[INFO|callbacks.py:310] 2024-07-11 12:03:46,842 >> {'loss': 0.0117, 'learning_rate': 2.1417e-06, 'epoch': 3.31, 'throughput': 1103.45}
|
| 764 |
-
|
| 765 |
-
[INFO|callbacks.py:310] 2024-07-11 12:04:00,471 >> {'loss': 0.0264, 'learning_rate': 2.1500e-06, 'epoch': 3.32, 'throughput': 1103.60}
|
| 766 |
-
|
| 767 |
-
[INFO|callbacks.py:310] 2024-07-11 12:04:14,075 >> {'loss': 0.0340, 'learning_rate': 2.1583e-06, 'epoch': 3.33, 'throughput': 1103.69}
|
| 768 |
-
|
| 769 |
-
[INFO|callbacks.py:310] 2024-07-11 12:04:27,601 >> {'loss': 0.0310, 'learning_rate': 2.1667e-06, 'epoch': 3.34, 'throughput': 1103.68}
|
| 770 |
-
|
| 771 |
-
[INFO|callbacks.py:310] 2024-07-11 12:04:41,161 >> {'loss': 0.0180, 'learning_rate': 2.1750e-06, 'epoch': 3.36, 'throughput': 1103.52}
|
| 772 |
-
|
| 773 |
-
[INFO|callbacks.py:310] 2024-07-11 12:04:54,755 >> {'loss': 0.0461, 'learning_rate': 2.1833e-06, 'epoch': 3.37, 'throughput': 1103.57}
|
| 774 |
-
|
| 775 |
-
[INFO|callbacks.py:310] 2024-07-11 12:05:08,257 >> {'loss': 0.0372, 'learning_rate': 2.1917e-06, 'epoch': 3.38, 'throughput': 1103.44}
|
| 776 |
-
|
| 777 |
-
[INFO|callbacks.py:310] 2024-07-11 12:05:21,809 >> {'loss': 0.0471, 'learning_rate': 2.2000e-06, 'epoch': 3.40, 'throughput': 1103.29}
|
| 778 |
-
|
| 779 |
-
[INFO|callbacks.py:310] 2024-07-11 12:05:35,395 >> {'loss': 0.0370, 'learning_rate': 2.2083e-06, 'epoch': 3.41, 'throughput': 1103.17}
|
| 780 |
-
|
| 781 |
-
[INFO|callbacks.py:310] 2024-07-11 12:05:48,966 >> {'loss': 0.0245, 'learning_rate': 2.2167e-06, 'epoch': 3.42, 'throughput': 1103.04}
|
| 782 |
-
|
| 783 |
-
[INFO|callbacks.py:310] 2024-07-11 12:06:02,486 >> {'loss': 0.0233, 'learning_rate': 2.2250e-06, 'epoch': 3.43, 'throughput': 1103.10}
|
| 784 |
-
|
| 785 |
-
[INFO|callbacks.py:310] 2024-07-11 12:06:16,087 >> {'loss': 0.0256, 'learning_rate': 2.2333e-06, 'epoch': 3.45, 'throughput': 1103.15}
|
| 786 |
-
|
| 787 |
-
[INFO|callbacks.py:310] 2024-07-11 12:06:29,677 >> {'loss': 0.0345, 'learning_rate': 2.2417e-06, 'epoch': 3.46, 'throughput': 1103.06}
|
| 788 |
-
|
| 789 |
-
[INFO|callbacks.py:310] 2024-07-11 12:06:43,259 >> {'loss': 0.0656, 'learning_rate': 2.2500e-06, 'epoch': 3.47, 'throughput': 1103.11}
|
| 790 |
-
|
| 791 |
-
[INFO|callbacks.py:310] 2024-07-11 12:06:56,838 >> {'loss': 0.0247, 'learning_rate': 2.2583e-06, 'epoch': 3.49, 'throughput': 1103.12}
|
| 792 |
-
|
| 793 |
-
[INFO|callbacks.py:310] 2024-07-11 12:07:10,402 >> {'loss': 0.0510, 'learning_rate': 2.2667e-06, 'epoch': 3.50, 'throughput': 1103.18}
|
| 794 |
-
|
| 795 |
-
[INFO|callbacks.py:310] 2024-07-11 12:07:23,955 >> {'loss': 0.0417, 'learning_rate': 2.2750e-06, 'epoch': 3.51, 'throughput': 1103.19}
|
| 796 |
-
|
| 797 |
-
[INFO|callbacks.py:310] 2024-07-11 12:07:37,561 >> {'loss': 0.0176, 'learning_rate': 2.2833e-06, 'epoch': 3.52, 'throughput': 1103.12}
|
| 798 |
-
|
| 799 |
-
[INFO|callbacks.py:310] 2024-07-11 12:07:51,125 >> {'loss': 0.0155, 'learning_rate': 2.2917e-06, 'epoch': 3.54, 'throughput': 1102.99}
|
| 800 |
-
|
| 801 |
-
[INFO|callbacks.py:310] 2024-07-11 12:08:04,721 >> {'loss': 0.0135, 'learning_rate': 2.3000e-06, 'epoch': 3.55, 'throughput': 1103.13}
|
| 802 |
-
|
| 803 |
-
[INFO|callbacks.py:310] 2024-07-11 12:08:18,303 >> {'loss': 0.0222, 'learning_rate': 2.3083e-06, 'epoch': 3.56, 'throughput': 1103.23}
|
| 804 |
-
|
| 805 |
-
[INFO|callbacks.py:310] 2024-07-11 12:08:31,900 >> {'loss': 0.0289, 'learning_rate': 2.3167e-06, 'epoch': 3.58, 'throughput': 1103.41}
|
| 806 |
-
|
| 807 |
-
[INFO|callbacks.py:310] 2024-07-11 12:08:45,544 >> {'loss': 0.0314, 'learning_rate': 2.3250e-06, 'epoch': 3.59, 'throughput': 1103.39}
|
| 808 |
-
|
| 809 |
-
[INFO|callbacks.py:310] 2024-07-11 12:08:59,085 >> {'loss': 0.0582, 'learning_rate': 2.3333e-06, 'epoch': 3.60, 'throughput': 1103.12}
|
| 810 |
-
|
| 811 |
-
[INFO|callbacks.py:310] 2024-07-11 12:09:12,702 >> {'loss': 0.0339, 'learning_rate': 2.3417e-06, 'epoch': 3.61, 'throughput': 1103.28}
|
| 812 |
-
|
| 813 |
-
[INFO|callbacks.py:310] 2024-07-11 12:09:26,307 >> {'loss': 0.0656, 'learning_rate': 2.3500e-06, 'epoch': 3.63, 'throughput': 1103.32}
|
| 814 |
-
|
| 815 |
-
[INFO|callbacks.py:310] 2024-07-11 12:09:39,915 >> {'loss': 0.0355, 'learning_rate': 2.3583e-06, 'epoch': 3.64, 'throughput': 1103.41}
|
| 816 |
-
|
| 817 |
-
[INFO|callbacks.py:310] 2024-07-11 12:09:53,473 >> {'loss': 0.0370, 'learning_rate': 2.3667e-06, 'epoch': 3.65, 'throughput': 1103.36}
|
| 818 |
-
|
| 819 |
-
[INFO|callbacks.py:310] 2024-07-11 12:10:07,008 >> {'loss': 0.0206, 'learning_rate': 2.3750e-06, 'epoch': 3.67, 'throughput': 1103.45}
|
| 820 |
-
|
| 821 |
-
[INFO|callbacks.py:310] 2024-07-11 12:10:20,629 >> {'loss': 0.0286, 'learning_rate': 2.3833e-06, 'epoch': 3.68, 'throughput': 1103.36}
|
| 822 |
-
|
| 823 |
-
[INFO|callbacks.py:310] 2024-07-11 12:10:34,205 >> {'loss': 0.0316, 'learning_rate': 2.3917e-06, 'epoch': 3.69, 'throughput': 1103.44}
|
| 824 |
-
|
| 825 |
-
[INFO|callbacks.py:310] 2024-07-11 12:10:47,785 >> {'loss': 0.0263, 'learning_rate': 2.4000e-06, 'epoch': 3.70, 'throughput': 1103.55}
|
| 826 |
-
|
| 827 |
-
[INFO|callbacks.py:310] 2024-07-11 12:11:01,368 >> {'loss': 0.0265, 'learning_rate': 2.4083e-06, 'epoch': 3.72, 'throughput': 1103.75}
|
| 828 |
-
|
| 829 |
-
[INFO|callbacks.py:310] 2024-07-11 12:11:14,942 >> {'loss': 0.0251, 'learning_rate': 2.4167e-06, 'epoch': 3.73, 'throughput': 1103.60}
|
| 830 |
-
|
| 831 |
-
[INFO|callbacks.py:310] 2024-07-11 12:11:28,479 >> {'loss': 0.0293, 'learning_rate': 2.4250e-06, 'epoch': 3.74, 'throughput': 1103.51}
|
| 832 |
-
|
| 833 |
-
[INFO|callbacks.py:310] 2024-07-11 12:11:42,009 >> {'loss': 0.0278, 'learning_rate': 2.4333e-06, 'epoch': 3.76, 'throughput': 1103.50}
|
| 834 |
-
|
| 835 |
-
[INFO|callbacks.py:310] 2024-07-11 12:11:55,561 >> {'loss': 0.0350, 'learning_rate': 2.4417e-06, 'epoch': 3.77, 'throughput': 1103.52}
|
| 836 |
-
|
| 837 |
-
[INFO|callbacks.py:310] 2024-07-11 12:12:09,121 >> {'loss': 0.0455, 'learning_rate': 2.4500e-06, 'epoch': 3.78, 'throughput': 1103.38}
|
| 838 |
-
|
| 839 |
-
[INFO|callbacks.py:310] 2024-07-11 12:12:22,668 >> {'loss': 0.0429, 'learning_rate': 2.4583e-06, 'epoch': 3.79, 'throughput': 1103.20}
|
| 840 |
-
|
| 841 |
-
[INFO|callbacks.py:310] 2024-07-11 12:12:36,239 >> {'loss': 0.0560, 'learning_rate': 2.4667e-06, 'epoch': 3.81, 'throughput': 1103.28}
|
| 842 |
-
|
| 843 |
-
[INFO|callbacks.py:310] 2024-07-11 12:12:49,764 >> {'loss': 0.0325, 'learning_rate': 2.4750e-06, 'epoch': 3.82, 'throughput': 1103.35}
|
| 844 |
-
|
| 845 |
-
[INFO|callbacks.py:310] 2024-07-11 12:13:03,351 >> {'loss': 0.0401, 'learning_rate': 2.4833e-06, 'epoch': 3.83, 'throughput': 1103.39}
|
| 846 |
-
|
| 847 |
-
[INFO|callbacks.py:310] 2024-07-11 12:13:16,918 >> {'loss': 0.0182, 'learning_rate': 2.4917e-06, 'epoch': 3.85, 'throughput': 1103.37}
|
| 848 |
-
|
| 849 |
-
[INFO|callbacks.py:310] 2024-07-11 12:13:30,518 >> {'loss': 0.0344, 'learning_rate': 2.5000e-06, 'epoch': 3.86, 'throughput': 1103.37}
|
| 850 |
-
|
| 851 |
-
[INFO|callbacks.py:310] 2024-07-11 12:13:44,052 >> {'loss': 0.0111, 'learning_rate': 2.5083e-06, 'epoch': 3.87, 'throughput': 1103.37}
|
| 852 |
-
|
| 853 |
-
[INFO|callbacks.py:310] 2024-07-11 12:13:57,592 >> {'loss': 0.0088, 'learning_rate': 2.5167e-06, 'epoch': 3.88, 'throughput': 1103.52}
|
| 854 |
-
|
| 855 |
-
[INFO|callbacks.py:310] 2024-07-11 12:14:11,256 >> {'loss': 0.0130, 'learning_rate': 2.5250e-06, 'epoch': 3.90, 'throughput': 1103.56}
|
| 856 |
-
|
| 857 |
-
[INFO|callbacks.py:310] 2024-07-11 12:14:24,831 >> {'loss': 0.0433, 'learning_rate': 2.5333e-06, 'epoch': 3.91, 'throughput': 1103.64}
|
| 858 |
-
|
| 859 |
-
[INFO|callbacks.py:310] 2024-07-11 12:14:38,383 >> {'loss': 0.0214, 'learning_rate': 2.5417e-06, 'epoch': 3.92, 'throughput': 1103.57}
|
| 860 |
-
|
| 861 |
-
[INFO|callbacks.py:310] 2024-07-11 12:14:51,973 >> {'loss': 0.0416, 'learning_rate': 2.5500e-06, 'epoch': 3.94, 'throughput': 1103.50}
|
| 862 |
-
|
| 863 |
-
[INFO|callbacks.py:310] 2024-07-11 12:15:05,507 >> {'loss': 0.0321, 'learning_rate': 2.5583e-06, 'epoch': 3.95, 'throughput': 1103.41}
|
| 864 |
-
|
| 865 |
-
[INFO|callbacks.py:310] 2024-07-11 12:15:19,098 >> {'loss': 0.0526, 'learning_rate': 2.5667e-06, 'epoch': 3.96, 'throughput': 1103.50}
|
| 866 |
-
|
| 867 |
-
[INFO|callbacks.py:310] 2024-07-11 12:15:32,712 >> {'loss': 0.0250, 'learning_rate': 2.5750e-06, 'epoch': 3.97, 'throughput': 1103.53}
|
| 868 |
-
|
| 869 |
-
[INFO|callbacks.py:310] 2024-07-11 12:15:46,305 >> {'loss': 0.0787, 'learning_rate': 2.5833e-06, 'epoch': 3.99, 'throughput': 1103.77}
|
| 870 |
-
|
| 871 |
-
[INFO|callbacks.py:310] 2024-07-11 12:15:59,841 >> {'loss': 0.0559, 'learning_rate': 2.5917e-06, 'epoch': 4.00, 'throughput': 1103.76}
|
| 872 |
-
|
| 873 |
-
[INFO|callbacks.py:310] 2024-07-11 12:16:13,395 >> {'loss': 0.0259, 'learning_rate': 2.6000e-06, 'epoch': 4.01, 'throughput': 1103.73}
|
| 874 |
-
|
| 875 |
-
[INFO|callbacks.py:310] 2024-07-11 12:16:27,011 >> {'loss': 0.0370, 'learning_rate': 2.6083e-06, 'epoch': 4.03, 'throughput': 1103.60}
|
| 876 |
-
|
| 877 |
-
[INFO|callbacks.py:310] 2024-07-11 12:16:40,583 >> {'loss': 0.0293, 'learning_rate': 2.6167e-06, 'epoch': 4.04, 'throughput': 1103.59}
|
| 878 |
-
|
| 879 |
-
[INFO|callbacks.py:310] 2024-07-11 12:16:54,092 >> {'loss': 0.0153, 'learning_rate': 2.6250e-06, 'epoch': 4.05, 'throughput': 1103.45}
|
| 880 |
-
|
| 881 |
-
[INFO|callbacks.py:310] 2024-07-11 12:17:07,743 >> {'loss': 0.0234, 'learning_rate': 2.6333e-06, 'epoch': 4.06, 'throughput': 1103.75}
|
| 882 |
-
|
| 883 |
-
[INFO|callbacks.py:310] 2024-07-11 12:17:21,282 >> {'loss': 0.0212, 'learning_rate': 2.6417e-06, 'epoch': 4.08, 'throughput': 1103.71}
|
| 884 |
-
|
| 885 |
-
[INFO|callbacks.py:310] 2024-07-11 12:17:34,845 >> {'loss': 0.0187, 'learning_rate': 2.6500e-06, 'epoch': 4.09, 'throughput': 1103.79}
|
| 886 |
-
|
| 887 |
-
[INFO|callbacks.py:310] 2024-07-11 12:17:48,409 >> {'loss': 0.0245, 'learning_rate': 2.6583e-06, 'epoch': 4.10, 'throughput': 1103.86}
|
| 888 |
-
|
| 889 |
-
[INFO|callbacks.py:310] 2024-07-11 12:18:01,997 >> {'loss': 0.0032, 'learning_rate': 2.6667e-06, 'epoch': 4.12, 'throughput': 1103.80}
|
| 890 |
-
|
| 891 |
-
[INFO|callbacks.py:310] 2024-07-11 12:18:15,570 >> {'loss': 0.0367, 'learning_rate': 2.6750e-06, 'epoch': 4.13, 'throughput': 1103.65}
|
| 892 |
-
|
| 893 |
-
[INFO|callbacks.py:310] 2024-07-11 12:18:29,133 >> {'loss': 0.0380, 'learning_rate': 2.6833e-06, 'epoch': 4.14, 'throughput': 1103.71}
|
| 894 |
-
|
| 895 |
-
[INFO|callbacks.py:310] 2024-07-11 12:18:42,699 >> {'loss': 0.0194, 'learning_rate': 2.6917e-06, 'epoch': 4.15, 'throughput': 1103.55}
|
| 896 |
-
|
| 897 |
-
[INFO|callbacks.py:310] 2024-07-11 12:18:56,286 >> {'loss': 0.0262, 'learning_rate': 2.7000e-06, 'epoch': 4.17, 'throughput': 1103.64}
|
| 898 |
-
|
| 899 |
-
[INFO|callbacks.py:310] 2024-07-11 12:19:09,822 >> {'loss': 0.0151, 'learning_rate': 2.7083e-06, 'epoch': 4.18, 'throughput': 1103.50}
|
| 900 |
-
|
| 901 |
-
[INFO|callbacks.py:310] 2024-07-11 12:19:23,377 >> {'loss': 0.0267, 'learning_rate': 2.7167e-06, 'epoch': 4.19, 'throughput': 1103.52}
|
| 902 |
-
|
| 903 |
-
[INFO|callbacks.py:310] 2024-07-11 12:19:36,893 >> {'loss': 0.0041, 'learning_rate': 2.7250e-06, 'epoch': 4.21, 'throughput': 1103.49}
|
| 904 |
-
|
| 905 |
-
[INFO|callbacks.py:310] 2024-07-11 12:19:50,423 >> {'loss': 0.0365, 'learning_rate': 2.7333e-06, 'epoch': 4.22, 'throughput': 1103.40}
|
| 906 |
-
|
| 907 |
-
[INFO|callbacks.py:310] 2024-07-11 12:20:04,049 >> {'loss': 0.0151, 'learning_rate': 2.7417e-06, 'epoch': 4.23, 'throughput': 1103.57}
|
| 908 |
-
|
| 909 |
-
[INFO|callbacks.py:310] 2024-07-11 12:20:17,604 >> {'loss': 0.0060, 'learning_rate': 2.7500e-06, 'epoch': 4.24, 'throughput': 1103.59}
|
| 910 |
-
|
| 911 |
-
[INFO|callbacks.py:310] 2024-07-11 12:20:31,180 >> {'loss': 0.0247, 'learning_rate': 2.7583e-06, 'epoch': 4.26, 'throughput': 1103.51}
|
| 912 |
-
|
| 913 |
-
[INFO|callbacks.py:310] 2024-07-11 12:20:44,824 >> {'loss': 0.0147, 'learning_rate': 2.7667e-06, 'epoch': 4.27, 'throughput': 1103.61}
|
| 914 |
-
|
| 915 |
-
[INFO|callbacks.py:310] 2024-07-11 12:20:58,301 >> {'loss': 0.0413, 'learning_rate': 2.7750e-06, 'epoch': 4.28, 'throughput': 1103.45}
|
| 916 |
-
|
| 917 |
-
[INFO|callbacks.py:310] 2024-07-11 12:21:11,864 >> {'loss': 0.0318, 'learning_rate': 2.7833e-06, 'epoch': 4.30, 'throughput': 1103.40}
|
| 918 |
-
|
| 919 |
-
[INFO|callbacks.py:310] 2024-07-11 12:21:25,395 >> {'loss': 0.0513, 'learning_rate': 2.7917e-06, 'epoch': 4.31, 'throughput': 1103.30}
|
| 920 |
-
|
| 921 |
-
[INFO|callbacks.py:310] 2024-07-11 12:21:38,939 >> {'loss': 0.0165, 'learning_rate': 2.8000e-06, 'epoch': 4.32, 'throughput': 1103.44}
|
| 922 |
-
|
| 923 |
-
[INFO|callbacks.py:310] 2024-07-11 12:21:52,483 >> {'loss': 0.0131, 'learning_rate': 2.8083e-06, 'epoch': 4.33, 'throughput': 1103.48}
|
| 924 |
-
|
| 925 |
-
[INFO|callbacks.py:310] 2024-07-11 12:22:06,128 >> {'loss': 0.0246, 'learning_rate': 2.8167e-06, 'epoch': 4.35, 'throughput': 1103.52}
|
| 926 |
-
|
| 927 |
-
[INFO|callbacks.py:310] 2024-07-11 12:22:19,671 >> {'loss': 0.0465, 'learning_rate': 2.8250e-06, 'epoch': 4.36, 'throughput': 1103.46}
|
| 928 |
-
|
| 929 |
-
[INFO|callbacks.py:310] 2024-07-11 12:22:33,289 >> {'loss': 0.0084, 'learning_rate': 2.8333e-06, 'epoch': 4.37, 'throughput': 1103.49}
|
| 930 |
-
|
| 931 |
-
[INFO|callbacks.py:310] 2024-07-11 12:22:46,833 >> {'loss': 0.0105, 'learning_rate': 2.8417e-06, 'epoch': 4.39, 'throughput': 1103.35}
|
| 932 |
-
|
| 933 |
-
[INFO|callbacks.py:310] 2024-07-11 12:23:00,393 >> {'loss': 0.0388, 'learning_rate': 2.8500e-06, 'epoch': 4.40, 'throughput': 1103.48}
|
| 934 |
-
|
| 935 |
-
[INFO|callbacks.py:310] 2024-07-11 12:23:13,920 >> {'loss': 0.0486, 'learning_rate': 2.8583e-06, 'epoch': 4.41, 'throughput': 1103.54}
|
| 936 |
-
|
| 937 |
-
[INFO|callbacks.py:310] 2024-07-11 12:23:27,450 >> {'loss': 0.0354, 'learning_rate': 2.8667e-06, 'epoch': 4.42, 'throughput': 1103.53}
|
| 938 |
-
|
| 939 |
-
[INFO|callbacks.py:310] 2024-07-11 12:23:41,045 >> {'loss': 0.0464, 'learning_rate': 2.8750e-06, 'epoch': 4.44, 'throughput': 1103.38}
|
| 940 |
-
|
| 941 |
-
[INFO|callbacks.py:310] 2024-07-11 12:23:54,593 >> {'loss': 0.0312, 'learning_rate': 2.8833e-06, 'epoch': 4.45, 'throughput': 1103.41}
|
| 942 |
-
|
| 943 |
-
[INFO|callbacks.py:310] 2024-07-11 12:24:08,195 >> {'loss': 0.0352, 'learning_rate': 2.8917e-06, 'epoch': 4.46, 'throughput': 1103.50}
|
| 944 |
-
|
| 945 |
-
[INFO|callbacks.py:310] 2024-07-11 12:24:21,721 >> {'loss': 0.0116, 'learning_rate': 2.9000e-06, 'epoch': 4.48, 'throughput': 1103.46}
|
| 946 |
-
|
| 947 |
-
[INFO|callbacks.py:310] 2024-07-11 12:24:35,342 >> {'loss': 0.0118, 'learning_rate': 2.9083e-06, 'epoch': 4.49, 'throughput': 1103.57}
|
| 948 |
-
|
| 949 |
-
[INFO|callbacks.py:310] 2024-07-11 12:24:48,908 >> {'loss': 0.0138, 'learning_rate': 2.9167e-06, 'epoch': 4.50, 'throughput': 1103.54}
|
| 950 |
-
|
| 951 |
-
[INFO|callbacks.py:310] 2024-07-11 12:25:02,509 >> {'loss': 0.0344, 'learning_rate': 2.9250e-06, 'epoch': 4.51, 'throughput': 1103.54}
|
| 952 |
-
|
| 953 |
-
[INFO|callbacks.py:310] 2024-07-11 12:25:16,091 >> {'loss': 0.0231, 'learning_rate': 2.9333e-06, 'epoch': 4.53, 'throughput': 1103.62}
|
| 954 |
-
|
| 955 |
-
[INFO|callbacks.py:310] 2024-07-11 12:25:29,594 >> {'loss': 0.0267, 'learning_rate': 2.9417e-06, 'epoch': 4.54, 'throughput': 1103.62}
|
| 956 |
-
|
| 957 |
-
[INFO|callbacks.py:310] 2024-07-11 12:25:43,090 >> {'loss': 0.0109, 'learning_rate': 2.9500e-06, 'epoch': 4.55, 'throughput': 1103.38}
|
| 958 |
-
|
| 959 |
-
[INFO|callbacks.py:310] 2024-07-11 12:25:56,700 >> {'loss': 0.0444, 'learning_rate': 2.9583e-06, 'epoch': 4.57, 'throughput': 1103.26}
|
| 960 |
-
|
| 961 |
-
[INFO|callbacks.py:310] 2024-07-11 12:26:10,257 >> {'loss': 0.0142, 'learning_rate': 2.9667e-06, 'epoch': 4.58, 'throughput': 1103.35}
|
| 962 |
-
|
| 963 |
-
[INFO|callbacks.py:310] 2024-07-11 12:26:23,765 >> {'loss': 0.0361, 'learning_rate': 2.9750e-06, 'epoch': 4.59, 'throughput': 1103.37}
|
| 964 |
-
|
| 965 |
-
[INFO|callbacks.py:310] 2024-07-11 12:26:37,356 >> {'loss': 0.0197, 'learning_rate': 2.9833e-06, 'epoch': 4.60, 'throughput': 1103.36}
|
| 966 |
-
|
| 967 |
-
[INFO|callbacks.py:310] 2024-07-11 12:26:50,915 >> {'loss': 0.0373, 'learning_rate': 2.9917e-06, 'epoch': 4.62, 'throughput': 1103.44}
|
| 968 |
-
|
| 969 |
-
[INFO|callbacks.py:310] 2024-07-11 12:27:04,473 >> {'loss': 0.0469, 'learning_rate': 3.0000e-06, 'epoch': 4.63, 'throughput': 1103.59}
|
| 970 |
-
|
| 971 |
-
[INFO|callbacks.py:310] 2024-07-11 12:27:17,984 >> {'loss': 0.0283, 'learning_rate': 3.0083e-06, 'epoch': 4.64, 'throughput': 1103.61}
|
| 972 |
-
|
| 973 |
-
[INFO|callbacks.py:310] 2024-07-11 12:27:31,534 >> {'loss': 0.0271, 'learning_rate': 3.0167e-06, 'epoch': 4.66, 'throughput': 1103.55}
|
| 974 |
-
|
| 975 |
-
[INFO|callbacks.py:310] 2024-07-11 12:27:45,113 >> {'loss': 0.0303, 'learning_rate': 3.0250e-06, 'epoch': 4.67, 'throughput': 1103.54}
|
| 976 |
-
|
| 977 |
-
[INFO|callbacks.py:310] 2024-07-11 12:27:58,741 >> {'loss': 0.0207, 'learning_rate': 3.0333e-06, 'epoch': 4.68, 'throughput': 1103.69}
|
| 978 |
-
|
| 979 |
-
[INFO|callbacks.py:310] 2024-07-11 12:28:12,347 >> {'loss': 0.0157, 'learning_rate': 3.0417e-06, 'epoch': 4.69, 'throughput': 1103.80}
|
| 980 |
-
|
| 981 |
-
[INFO|callbacks.py:310] 2024-07-11 12:28:25,988 >> {'loss': 0.0128, 'learning_rate': 3.0500e-06, 'epoch': 4.71, 'throughput': 1103.71}
|
| 982 |
-
|
| 983 |
-
[INFO|callbacks.py:310] 2024-07-11 12:28:39,517 >> {'loss': 0.0293, 'learning_rate': 3.0583e-06, 'epoch': 4.72, 'throughput': 1103.92}
|
| 984 |
-
|
| 985 |
-
[INFO|callbacks.py:310] 2024-07-11 12:28:53,124 >> {'loss': 0.0211, 'learning_rate': 3.0667e-06, 'epoch': 4.73, 'throughput': 1103.87}
|
| 986 |
-
|
| 987 |
-
[INFO|callbacks.py:310] 2024-07-11 12:29:06,615 >> {'loss': 0.0929, 'learning_rate': 3.0750e-06, 'epoch': 4.75, 'throughput': 1103.90}
|
| 988 |
-
|
| 989 |
-
[INFO|callbacks.py:310] 2024-07-11 12:29:20,204 >> {'loss': 0.0195, 'learning_rate': 3.0833e-06, 'epoch': 4.76, 'throughput': 1103.97}
|
| 990 |
-
|
| 991 |
-
[INFO|callbacks.py:310] 2024-07-11 12:29:33,779 >> {'loss': 0.0114, 'learning_rate': 3.0917e-06, 'epoch': 4.77, 'throughput': 1104.05}
|
| 992 |
-
|
| 993 |
-
[INFO|callbacks.py:310] 2024-07-11 12:29:47,300 >> {'loss': 0.0207, 'learning_rate': 3.1000e-06, 'epoch': 4.78, 'throughput': 1104.23}
|
| 994 |
-
|
| 995 |
-
[INFO|callbacks.py:310] 2024-07-11 12:30:00,871 >> {'loss': 0.0144, 'learning_rate': 3.1083e-06, 'epoch': 4.80, 'throughput': 1104.22}
|
| 996 |
-
|
| 997 |
-
[INFO|callbacks.py:310] 2024-07-11 12:30:14,485 >> {'loss': 0.0197, 'learning_rate': 3.1167e-06, 'epoch': 4.81, 'throughput': 1104.27}
|
| 998 |
-
|
| 999 |
-
[INFO|callbacks.py:310] 2024-07-11 12:30:28,105 >> {'loss': 0.0669, 'learning_rate': 3.1250e-06, 'epoch': 4.82, 'throughput': 1104.49}
|
| 1000 |
-
|
| 1001 |
-
[INFO|callbacks.py:310] 2024-07-11 12:30:41,666 >> {'loss': 0.0156, 'learning_rate': 3.1333e-06, 'epoch': 4.84, 'throughput': 1104.45}
|
| 1002 |
-
|
| 1003 |
-
[INFO|callbacks.py:310] 2024-07-11 12:30:55,231 >> {'loss': 0.0180, 'learning_rate': 3.1417e-06, 'epoch': 4.85, 'throughput': 1104.57}
|
| 1004 |
|
| 1005 |
-
[INFO|
|
| 1006 |
|
| 1007 |
-
[INFO|
|
|
|
|
| 1008 |
|
| 1009 |
-
[INFO|
|
| 1010 |
|
| 1011 |
-
[INFO|
|
| 1012 |
|
| 1013 |
-
[
|
| 1014 |
|
| 1015 |
-
|
| 1016 |
|
| 1017 |
-
|
| 1018 |
|
| 1019 |
-
|
| 1020 |
|
| 1021 |
-
|
| 1022 |
|
| 1023 |
-
|
| 1024 |
|
| 1025 |
-
|
| 1026 |
|
| 1027 |
-
|
| 1028 |
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[INFO|
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{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
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| 1057 |
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| 1 |
+
[INFO|parser.py:325] 2024-07-11 13:12:32,834 >> Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, compute dtype: None
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| 2 |
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| 3 |
+
[INFO|tokenization_utils_base.py:2159] 2024-07-11 13:12:32,836 >> loading file tokenizer.json
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| 4 |
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+
07/11/2024 13:12:32 - INFO - llamafactory.hparams.parser - Process rank: 5, device: cuda:5, n_gpu: 1, distributed training: True, compute dtype: None
|
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| 7 |
+
07/11/2024 13:12:32 - INFO - llamafactory.hparams.parser - Process rank: 4, device: cuda:4, n_gpu: 1, distributed training: True, compute dtype: None
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+
07/11/2024 13:12:32 - INFO - llamafactory.hparams.parser - Process rank: 6, device: cuda:6, n_gpu: 1, distributed training: True, compute dtype: None
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+
07/11/2024 13:12:32 - INFO - llamafactory.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1, distributed training: True, compute dtype: None
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+
07/11/2024 13:12:33 - INFO - llamafactory.hparams.parser - Process rank: 2, device: cuda:2, n_gpu: 1, distributed training: True, compute dtype: None
|
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+
07/11/2024 13:12:33 - INFO - llamafactory.hparams.parser - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, compute dtype: None
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| 16 |
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| 17 |
+
[INFO|tokenization_utils_base.py:2159] 2024-07-11 13:12:32,836 >> loading file added_tokens.json
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| 18 |
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| 19 |
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[INFO|tokenization_utils_base.py:2159] 2024-07-11 13:12:32,836 >> loading file special_tokens_map.json
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[INFO|tokenization_utils_base.py:2159] 2024-07-11 13:12:32,836 >> loading file tokenizer_config.json
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| 22 |
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+
[WARNING|logging.py:313] 2024-07-11 13:12:33,150 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 24 |
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| 25 |
+
[INFO|template.py:270] 2024-07-11 13:12:33,150 >> Replace eos token: <|eot_id|>
|
| 26 |
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| 27 |
+
[INFO|loader.py:50] 2024-07-11 13:12:33,151 >> Loading dataset dev_output.json...
|
| 28 |
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| 29 |
+
07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 30 |
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| 31 |
+
07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
| 32 |
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| 33 |
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07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 34 |
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07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
| 36 |
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| 37 |
+
07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 38 |
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| 39 |
+
07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
| 40 |
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| 41 |
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07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 42 |
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| 43 |
+
07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
| 44 |
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| 45 |
+
07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
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07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
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07/11/2024 13:12:33 - INFO - llamafactory.hparams.parser - Process rank: 7, device: cuda:7, n_gpu: 1, distributed training: True, compute dtype: None
|
| 50 |
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| 51 |
+
07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
| 52 |
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| 53 |
+
07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
| 54 |
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| 55 |
+
07/11/2024 13:12:33 - WARNING - transformers.tokenization_utils_base - Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
|
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+
07/11/2024 13:12:33 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
|
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| 59 |
+
07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 60 |
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+
07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 62 |
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07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
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07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 66 |
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07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 68 |
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| 69 |
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07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 70 |
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07/11/2024 13:12:34 - INFO - llamafactory.data.loader - Loading dataset dev_output.json...
|
| 72 |
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| 73 |
+
[INFO|configuration_utils.py:731] 2024-07-11 13:12:38,208 >> loading configuration file saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3/config.json
|
| 74 |
|
| 75 |
+
[INFO|configuration_utils.py:800] 2024-07-11 13:12:38,209 >> Model config LlamaConfig {
|
| 76 |
+
"_name_or_path": "saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3",
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| 77 |
"architectures": [
|
| 78 |
"LlamaForCausalLM"
|
| 79 |
],
|
|
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|
| 98 |
"tie_word_embeddings": false,
|
| 99 |
"torch_dtype": "bfloat16",
|
| 100 |
"transformers_version": "4.42.3",
|
| 101 |
+
"use_cache": false,
|
| 102 |
"vocab_size": 128256
|
| 103 |
}
|
| 104 |
|
| 105 |
|
| 106 |
+
[INFO|patcher.py:81] 2024-07-11 13:12:38,210 >> Using KV cache for faster generation.
|
| 107 |
+
|
| 108 |
+
[INFO|modeling_utils.py:3553] 2024-07-11 13:12:38,232 >> loading weights file saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3/model.safetensors.index.json
|
| 109 |
|
| 110 |
+
[INFO|modeling_utils.py:1531] 2024-07-11 13:12:38,232 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
|
| 111 |
|
| 112 |
+
[INFO|configuration_utils.py:1000] 2024-07-11 13:12:38,233 >> Generate config GenerationConfig {
|
| 113 |
"bos_token_id": 128000,
|
| 114 |
"eos_token_id": 128009
|
| 115 |
}
|
| 116 |
|
| 117 |
|
| 118 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 119 |
+
|
| 120 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 121 |
+
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| 122 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 123 |
|
| 124 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 125 |
|
| 126 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 127 |
|
| 128 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 129 |
|
| 130 |
+
07/11/2024 13:12:38 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.
|
| 131 |
|
| 132 |
+
[INFO|modeling_utils.py:4364] 2024-07-11 13:12:42,279 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
|
| 133 |
|
| 134 |
|
| 135 |
+
[INFO|modeling_utils.py:4372] 2024-07-11 13:12:42,279 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3.
|
| 136 |
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
|
| 137 |
|
| 138 |
+
[INFO|configuration_utils.py:953] 2024-07-11 13:12:42,283 >> loading configuration file saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3/generation_config.json
|
| 139 |
|
| 140 |
+
[INFO|configuration_utils.py:1000] 2024-07-11 13:12:42,283 >> Generate config GenerationConfig {
|
| 141 |
"bos_token_id": 128000,
|
| 142 |
"do_sample": true,
|
| 143 |
"eos_token_id": [
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|
| 150 |
}
|
| 151 |
|
| 152 |
|
| 153 |
+
[INFO|attention.py:80] 2024-07-11 13:12:42,289 >> Using torch SDPA for faster training and inference.
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| 154 |
|
| 155 |
+
[INFO|loader.py:196] 2024-07-11 13:12:42,294 >> all params: 8,030,261,248
|
| 156 |
|
| 157 |
+
[INFO|trainer.py:3788] 2024-07-11 13:12:42,401 >>
|
| 158 |
+
***** Running Prediction *****
|
| 159 |
|
| 160 |
+
[INFO|trainer.py:3790] 2024-07-11 13:12:42,401 >> Num examples = 2554
|
| 161 |
|
| 162 |
+
[INFO|trainer.py:3793] 2024-07-11 13:12:42,401 >> Batch size = 2
|
| 163 |
|
| 164 |
+
[WARNING|logging.py:328] 2024-07-11 13:12:43,055 >> We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 165 |
|
| 166 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 167 |
|
| 168 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 169 |
|
| 170 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 171 |
|
| 172 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 173 |
|
| 174 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 175 |
|
| 176 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 177 |
|
| 178 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 179 |
|
| 180 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 181 |
|
| 182 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 183 |
|
| 184 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 185 |
|
| 186 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 187 |
|
| 188 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 189 |
|
| 190 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
|
| 191 |
|
| 192 |
+
07/11/2024 13:12:43 - INFO - llamafactory.model.loader - all params: 8,030,261,248
|
| 193 |
|
| 194 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 195 |
|
| 196 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 197 |
|
| 198 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 199 |
|
| 200 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 201 |
|
| 202 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 203 |
|
| 204 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 205 |
|
| 206 |
+
07/11/2024 13:12:44 - WARNING - transformers.models.llama.modeling_llama - We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
|
| 207 |
|
| 208 |
+
[INFO|trainer.py:127] 2024-07-11 13:12:59,490 >> Saving prediction results to saves/LLaMA3-8B-Chat/full/eval_2024-07-11-10-49-45/generated_predictions.jsonl
|
|
|
|
| 209 |
|
trainer_log.jsonl
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
training_args.yaml
CHANGED
|
@@ -1,30 +1,18 @@
|
|
| 1 |
-
bf16: true
|
| 2 |
cutoff_len: 1024
|
| 3 |
-
dataset:
|
| 4 |
dataset_dir: data
|
| 5 |
-
|
| 6 |
-
deepspeed: cache/ds_z2_config.json
|
| 7 |
-
do_train: true
|
| 8 |
finetuning_type: full
|
| 9 |
flash_attn: auto
|
| 10 |
-
|
| 11 |
-
include_num_input_tokens_seen: true
|
| 12 |
-
learning_rate: 5.0e-06
|
| 13 |
-
logging_steps: 1
|
| 14 |
-
lr_scheduler_type: cosine
|
| 15 |
-
max_grad_norm: 1.0
|
| 16 |
max_samples: 100000
|
| 17 |
-
model_name_or_path:
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
packing: false
|
| 22 |
-
per_device_train_batch_size: 4
|
| 23 |
-
plot_loss: true
|
| 24 |
preprocessing_num_workers: 16
|
| 25 |
quantization_method: bitsandbytes
|
| 26 |
-
report_to: none
|
| 27 |
-
save_steps: 1000
|
| 28 |
stage: sft
|
|
|
|
| 29 |
template: llama3
|
| 30 |
-
|
|
|
|
|
|
|
| 1 |
cutoff_len: 1024
|
| 2 |
+
dataset: truth_dev
|
| 3 |
dataset_dir: data
|
| 4 |
+
do_predict: true
|
|
|
|
|
|
|
| 5 |
finetuning_type: full
|
| 6 |
flash_attn: auto
|
| 7 |
+
max_new_tokens: 512
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
max_samples: 100000
|
| 9 |
+
model_name_or_path: saves/LLaMA3-8B-Chat/full/train_2024-07-11-10-49-45_inst_llama3
|
| 10 |
+
output_dir: saves/LLaMA3-8B-Chat/full/eval_2024-07-11-10-49-45
|
| 11 |
+
per_device_eval_batch_size: 2
|
| 12 |
+
predict_with_generate: true
|
|
|
|
|
|
|
|
|
|
| 13 |
preprocessing_num_workers: 16
|
| 14 |
quantization_method: bitsandbytes
|
|
|
|
|
|
|
| 15 |
stage: sft
|
| 16 |
+
temperature: 0.95
|
| 17 |
template: llama3
|
| 18 |
+
top_p: 0.7
|