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diff --git a/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/config.yaml b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e4a464d2672d97efa06e3f913ff28d0d202369a
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/config.yaml
@@ -0,0 +1,613 @@
+__cached__setup_devices:
+    value: cuda:0
+_n_gpu:
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+        e:
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+                    - /home/n5huang/dna_token/pretrain/models/len2_5120
+                    - --model_type
+                    - bert
+                    - --tokenizer_name
+                    - /home/n5huang/dna_token/tokenizer_evaluation/merge_bpe/vocab_5120/merge_tokenizer_unigram_len2.json
+                    - --config_name
+                    - /home/n5huang/dna_token/pretrain/models/len2_5120/config.json
+                    - --project_name
+                    - token_eval_len2_5120
+                    - --do_train
+                    - "True"
+                    - --model_max_length
+                    - "512"
+                    - --max_seq_length
+                    - "512"
+                    - --line_by_line
+                    - "True"
+                    - --pad_to_max_length
+                    - "True"
+                    - --train_file
+                    - /home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_train_chrOnly.tsv
+                    - --validation_file
+                    - /home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_val_chrOnly.tsv
+                    - --cache_dir
+                    - /home/n5huang/dna_token/output_tokens/cache
+                    - --use_fast_tokenizer
+                    - "True"
+                    - --do_eval
+                    - "True"
+                    - --gradient_accumulation_steps
+                    - "1"
+                    - --per_device_train_batch_size
+                    - "96"
+                    - --per_device_eval_batch_size
+                    - "96"
+                    - --save_steps
+                    - "1000"
+                    - --save_total_limit
+                    - "10"
+                    - --max_steps
+                    - "100000"
+                    - --logging_steps
+                    - "1000"
+                    - --learning_rate
+                    - "4e-5"
+                    - --adam_epsilon
+                    - "1e-6"
+                    - --weight_decay
+                    - "0.01"
+                    - --adam_beta1
+                    - "0.9"
+                    - --adam_beta2
+                    - "0.98"
+                    - --mlm_probability
+                    - "0.15"
+                    - --warmup_steps
+                    - "10000"
+                    - --seed
+                    - "42"
+                    - --preprocessing_num_workers
+                    - "8"
+                    - --resume_from_checkpoint
+                    - /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000
+                cpu_count: 128
+                cpu_count_logical: 255
+                cudaVersion: "12.2"
+                disk:
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+                        total: "1876350566400"
+                        used: "405230526464"
+                email: mtapiapacheco@ucsd.edu
+                executable: /home/n5huang/miniconda3/envs/bpe/bin/python
+                gpu: NVIDIA A100 80GB PCIe
+                gpu_count: 8
+                gpu_nvidia:
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+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-dc4eaee8-f907-9592-417e-e0dbe37f86a3
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+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-054a4d72-8520-5e4a-5cd3-7519a0966809
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-8d529af8-bcca-cbbc-fa00-78bf605d4eab
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-a18369da-b15a-c8af-72f1-7f6272e479af
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+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-fb2ffc63-6c8a-5e90-0466-bd865bdd8b10
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-b8dce99b-6337-0212-03f4-56e6e689fa2e
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-d097eb04-aaca-ea9a-2730-e29dca4737a0
+                    - architecture: Ampere
+                      cudaCores: 6912
+                      memoryTotal: "85899345920"
+                      name: NVIDIA A100 80GB PCIe
+                      uuid: GPU-889b7e9a-34bf-f9b3-1168-16a2b3798044
+                host: sn4622116170
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+                os: Linux-5.15.0-72-generic-x86_64-with-glibc2.35
+                program: /home/n5huang/dna-tokenizer/BPE_merge/run_mlm_200k.py
+                python: CPython 3.9.18
+                root: /data/n5huang/dna_token/output_tokens
+                startedAt: "2026-04-09T02:42:56.385921Z"
+                writerId: 8xva522u4d798tgf5m4gfomp910saafd
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+add_cross_attention:
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+ddp_timeout:
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+debug:
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+decoder_start_token_id:
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+dispatch_batches:
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+distributed_state:
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+        Num processes: 1
+        Process index: 0
+        Local process index: 0
+        Device: cuda
+diversity_penalty:
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+do_eval:
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+eval_accumulation_steps:
+    value: null
+eval_delay:
+    value: 0
+eval_steps:
+    value: null
+evaluation_strategy:
+    value: "no"
+exponential_decay_length_penalty:
+    value: null
+finetuning_task:
+    value: null
+forced_bos_token_id:
+    value: null
+forced_eos_token_id:
+    value: null
+fp16:
+    value: false
+fp16_backend:
+    value: auto
+fp16_full_eval:
+    value: false
+fp16_opt_level:
+    value: O1
+fsdp:
+    value: []
+fsdp_config:
+    value:
+        min_num_params: 0
+        xla: false
+        xla_fsdp_grad_ckpt: false
+fsdp_min_num_params:
+    value: 0
+fsdp_transformer_layer_cls_to_wrap:
+    value: null
+full_determinism:
+    value: false
+gradient_accumulation_steps:
+    value: 1
+gradient_checkpointing:
+    value: false
+gradient_checkpointing_kwargs:
+    value: null
+greater_is_better:
+    value: null
+group_by_length:
+    value: false
+half_precision_backend:
+    value: auto
+hidden_act:
+    value: gelu
+hidden_dropout_prob:
+    value: 0.1
+hidden_size:
+    value: 768
+hub_always_push:
+    value: false
+hub_model_id:
+    value: null
+hub_private_repo:
+    value: false
+hub_strategy:
+    value: every_save
+hub_token:
+    value: <HUB_TOKEN>
+id2label:
+    value:
+        "0": LABEL_0
+        "1": LABEL_1
+ignore_data_skip:
+    value: false
+include_inputs_for_metrics:
+    value: false
+include_tokens_per_second:
+    value: false
+initializer_range:
+    value: 0.02
+intermediate_size:
+    value: 3072
+is_decoder:
+    value: false
+is_encoder_decoder:
+    value: false
+jit_mode_eval:
+    value: false
+label_names:
+    value: null
+label_smoothing_factor:
+    value: 0
+label2id:
+    value:
+        LABEL_0: 0
+        LABEL_1: 1
+layer_norm_eps:
+    value: 1e-12
+learning_rate:
+    value: 4e-05
+length_column_name:
+    value: length
+length_penalty:
+    value: 1
+line_by_line:
+    value: true
+load_best_model_at_end:
+    value: false
+local_rank:
+    value: 0
+log_level:
+    value: passive
+log_level_replica:
+    value: warning
+log_on_each_node:
+    value: true
+logging_dir:
+    value: /home/n5huang/dna_token/pretrain/models/len2_5120/runs/Apr09_02-42-57_sn4622116170
+logging_first_step:
+    value: false
+logging_nan_inf_filter:
+    value: true
+logging_steps:
+    value: 1000
+logging_strategy:
+    value: steps
+low_cpu_mem_usage:
+    value: false
+lr_scheduler_type:
+    value: linear
+max_eval_samples:
+    value: null
+max_grad_norm:
+    value: 1
+max_length:
+    value: 512
+max_position_embeddings:
+    value: 512
+max_seq_length:
+    value: 512
+max_steps:
+    value: 100000
+max_train_samples:
+    value: null
+metric_for_best_model:
+    value: null
+min_length:
+    value: 0
+mlm_probability:
+    value: 0.15
+model_max_length:
+    value: 512
+model_name_or_path:
+    value: null
+model_revision:
+    value: main
+model_type:
+    value: bert
+mp_parameters:
+    value: ""
+neftune_noise_alpha:
+    value: null
+no_cuda:
+    value: false
+no_repeat_ngram_size:
+    value: 0
+num_attention_heads:
+    value: 12
+num_beam_groups:
+    value: 1
+num_beams:
+    value: 1
+num_hidden_layers:
+    value: 12
+num_return_sequences:
+    value: 1
+num_train_epochs:
+    value: 3
+optim:
+    value: adamw_torch
+optim_args:
+    value: null
+output_attentions:
+    value: false
+output_dir:
+    value: /home/n5huang/dna_token/pretrain/models/len2_5120
+output_hidden_states:
+    value: false
+output_scores:
+    value: false
+overwrite_cache:
+    value: false
+overwrite_output_dir:
+    value: false
+pad_to_max_length:
+    value: true
+pad_token_id:
+    value: 0
+past_index:
+    value: -1
+per_device_eval_batch_size:
+    value: 96
+per_device_train_batch_size:
+    value: 96
+per_gpu_eval_batch_size:
+    value: null
+per_gpu_train_batch_size:
+    value: null
+position_embedding_type:
+    value: absolute
+prediction_loss_only:
+    value: false
+prefix:
+    value: null
+preprocessing_num_workers:
+    value: 8
+problem_type:
+    value: null
+project_name:
+    value: token_eval_len2_5120
+push_to_hub:
+    value: false
+push_to_hub_model_id:
+    value: null
+push_to_hub_organization:
+    value: null
+push_to_hub_token:
+    value: <PUSH_TO_HUB_TOKEN>
+ray_scope:
+    value: last
+remove_invalid_values:
+    value: false
+remove_unused_columns:
+    value: true
+repetition_penalty:
+    value: 1
+report_to:
+    value:
+        - wandb
+resume_from_checkpoint:
+    value: /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000
+return_dict:
+    value: true
+return_dict_in_generate:
+    value: false
+run_name:
+    value: /home/n5huang/dna_token/pretrain/models/len2_5120
+save_on_each_node:
+    value: false
+save_safetensors:
+    value: true
+save_steps:
+    value: 1000
+save_strategy:
+    value: steps
+save_total_limit:
+    value: 10
+seed:
+    value: 42
+sep_token_id:
+    value: null
+skip_memory_metrics:
+    value: true
+split_batches:
+    value: false
+streaming:
+    value: false
+suppress_tokens:
+    value: null
+task_specific_params:
+    value: null
+temperature:
+    value: 1
+tf_legacy_loss:
+    value: false
+tf32:
+    value: null
+tie_encoder_decoder:
+    value: false
+tie_word_embeddings:
+    value: true
+token:
+    value: null
+tokenizer_class:
+    value: null
+tokenizer_name:
+    value: /home/n5huang/dna_token/tokenizer_evaluation/merge_bpe/vocab_5120/merge_tokenizer_unigram_len2.json
+top_k:
+    value: 50
+top_p:
+    value: 1
+torch_compile:
+    value: false
+torch_compile_backend:
+    value: null
+torch_compile_mode:
+    value: null
+torch_dtype:
+    value: float32
+torchdynamo:
+    value: null
+torchscript:
+    value: false
+tpu_metrics_debug:
+    value: false
+tpu_num_cores:
+    value: null
+train_file:
+    value: /home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_train_chrOnly.tsv
+transformers_version:
+    value: 4.35.2
+trust_remote_code:
+    value: false
+type_vocab_size:
+    value: 2
+typical_p:
+    value: 1
+use_bfloat16:
+    value: false
+use_cache:
+    value: true
+use_cpu:
+    value: false
+use_fast_tokenizer:
+    value: true
+use_ipex:
+    value: false
+use_legacy_prediction_loop:
+    value: false
+use_mps_device:
+    value: false
+validation_file:
+    value: /home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_val_chrOnly.tsv
+validation_split_percentage:
+    value: 5
+vocab_size:
+    value: 5120
+warmup_ratio:
+    value: 0
+warmup_steps:
+    value: 10000
+weight_decay:
+    value: 0.01
diff --git a/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/output.log b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..e6f842b62513c0e5483b62bb282b368c406747b3
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/output.log
@@ -0,0 +1,268 @@
+04/09/2026 02:42:57 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False
+04/09/2026 02:42:57 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.98,
+adam_epsilon=1e-06,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_pin_memory=True,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=None,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=True,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=no,
+fp16=False,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+gradient_accumulation_steps=1,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=4e-05,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=0,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/home/n5huang/dna_token/pretrain/models/len2_5120/runs/Apr09_02-42-57_sn4622116170,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=1000,
+logging_strategy=steps,
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=100000,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=3.0,
+optim=adamw_torch,
+optim_args=None,
+output_dir=/home/n5huang/dna_token/pretrain/models/len2_5120,
+overwrite_output_dir=False,
+past_index=-1,
+per_device_eval_batch_size=96,
+per_device_train_batch_size=96,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=['wandb'],
+resume_from_checkpoint=/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000,
+run_name=/home/n5huang/dna_token/pretrain/models/len2_5120,
+save_on_each_node=False,
+save_safetensors=True,
+save_steps=1000,
+save_strategy=steps,
+save_total_limit=10,
+seed=42,
+skip_memory_metrics=True,
+split_batches=False,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=10000,
+weight_decay=0.01,
+)
+Using custom data configuration default-9aa68439a1bf4533
+04/09/2026 02:42:58 - INFO - datasets.builder - Using custom data configuration default-9aa68439a1bf4533
+Generating dataset csv (/home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420)
+04/09/2026 02:42:58 - INFO - datasets.builder - Generating dataset csv (/home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420)
+Downloading and preparing dataset csv/default to /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420...
+04/09/2026 02:42:58 - INFO - datasets.builder - Downloading and preparing dataset csv/default to /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420...
+Downloading took 0.0 min
+04/09/2026 02:42:58 - INFO - datasets.download.download_manager - Downloading took 0.0 min
+Checksum Computation took 0.0 min
+04/09/2026 02:42:58 - INFO - datasets.download.download_manager - Checksum Computation took 0.0 min
+Generating train split
+04/09/2026 02:42:58 - INFO - datasets.builder - Generating train split
+Generating train split: 982503 examples [00:24, 40012.03 examples/s]
+Generating validation split
+04/09/2026 02:43:22 - INFO - datasets.builder - Generating validation split
+Generating validation split: 109173 examples [00:02, 40132.11 examples/s]
+Unable to verify splits sizes.
+04/09/2026 02:43:25 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.
+Dataset csv downloaded and prepared to /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420. Subsequent calls will reuse this data.
+04/09/2026 02:43:25 - INFO - datasets.builder - Dataset csv downloaded and prepared to /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420. Subsequent calls will reuse this data.
+[INFO|configuration_utils.py:715] 2026-04-09 02:43:25,496 >> loading configuration file /home/n5huang/dna_token/pretrain/models/len2_5120/config.json
+[INFO|configuration_utils.py:777] 2026-04-09 02:43:25,497 >> Model config BertConfig {
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 5120
+}
+04/09/2026 02:43:25 - INFO - __main__ - Training new model from scratch
+
+/home/n5huang/miniconda3/envs/bpe/lib/python3.9/site-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
+  _torch_pytree._register_pytree_node(
+[INFO|configuration_utils.py:791] 2026-04-09 02:43:25,507 >> Generate config GenerationConfig {
+  "max_length": 512,
+  "pad_token_id": 0
+}
+
+Process #1 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00000_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #1 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00000_of_00008.arrow
+Process #2 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00001_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #2 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00001_of_00008.arrow
+Process #3 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00002_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #3 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00002_of_00008.arrow
+Process #4 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00003_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #4 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00003_of_00008.arrow
+Process #5 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00004_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #5 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00004_of_00008.arrow
+Process #6 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00005_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #6 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00005_of_00008.arrow
+Process #7 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00006_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #7 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00006_of_00008.arrow
+Process #8 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00007_of_00008.arrow
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Process #8 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_00007_of_00008.arrow
+Running tokenizer on dataset line_by_line (num_proc=8):   0%|          | 0/982503 [00:00<?, ? examples/s]Spawning 8 processes
+04/09/2026 02:43:41 - INFO - datasets.arrow_dataset - Spawning 8 processes
+Running tokenizer on dataset line_by_line (num_proc=8): 100%|██████████| 982503/982503 [02:32<00:00, 6449.24 examples/s]
+Concatenating 8 shards
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Concatenating 8 shards
+Process #1 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00000_of_00008.arrow
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Process #1 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00000_of_00008.arrow
+Process #2 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00001_of_00008.arrow
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Process #2 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00001_of_00008.arrow
+Process #3 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00002_of_00008.arrow
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Process #3 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00002_of_00008.arrow
+Process #4 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00003_of_00008.arrow
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Process #4 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00003_of_00008.arrow
+Process #5 will write at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_00004_of_00008.arrow
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+Running tokenizer on dataset line_by_line (num_proc=8):   0%|          | 0/109173 [00:00<?, ? examples/s]Spawning 8 processes
+04/09/2026 02:46:14 - INFO - datasets.arrow_dataset - Spawning 8 processes
+Running tokenizer on dataset line_by_line (num_proc=8): 100%|██████████| 109173/109173 [00:17<00:00, 6162.84 examples/s]
+Concatenating 8 shards
+04/09/2026 02:46:31 - INFO - datasets.arrow_dataset - Concatenating 8 shards
+[INFO|trainer.py:544] 2026-04-09 02:46:37,686 >> max_steps is given, it will override any value given in num_train_epochs
+[INFO|trainer.py:2066] 2026-04-09 02:46:37,688 >> Loading model from /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000.
+[WARNING|trainer.py:2231] 2026-04-09 02:46:37,942 >> There were missing keys in the checkpoint model loaded: ['cls.predictions.decoder.weight', 'cls.predictions.decoder.bias'].
+[INFO|trainer.py:738] 2026-04-09 02:46:38,250 >> The following columns in the training set don't have a corresponding argument in `BertForMaskedLM.forward` and have been ignored: special_tokens_mask. If special_tokens_mask are not expected by `BertForMaskedLM.forward`,  you can safely ignore this message.
+[INFO|trainer.py:1723] 2026-04-09 02:46:38,922 >> ***** Running training *****
+[INFO|trainer.py:1724] 2026-04-09 02:46:38,923 >>   Num examples = 982,503
+[INFO|trainer.py:1725] 2026-04-09 02:46:38,923 >>   Num Epochs = 10
+[INFO|trainer.py:1726] 2026-04-09 02:46:38,924 >>   Instantaneous batch size per device = 96
+[INFO|trainer.py:1729] 2026-04-09 02:46:38,924 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
+[INFO|trainer.py:1730] 2026-04-09 02:46:38,925 >>   Gradient Accumulation steps = 1
+[INFO|trainer.py:1731] 2026-04-09 02:46:38,925 >>   Total optimization steps = 100,000
+[INFO|trainer.py:1732] 2026-04-09 02:46:38,926 >>   Number of trainable parameters = 89,980,160
+[INFO|trainer.py:1752] 2026-04-09 02:46:38,927 >>   Continuing training from checkpoint, will skip to saved global_step
+[INFO|trainer.py:1753] 2026-04-09 02:46:38,927 >>   Continuing training from epoch 9
+[INFO|trainer.py:1754] 2026-04-09 02:46:38,927 >>   Continuing training from global step 100000
+[INFO|trainer.py:1756] 2026-04-09 02:46:38,927 >>   Will skip the first 9 epochs then the first 7885 batches in the first epoch.
+[INFO|integration_utils.py:718] 2026-04-09 02:46:38,928 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
+  0%|          | 0/100000 [00:00<?, ?it/s][WARNING|logging.py:314] 2026-04-09 02:46:40,535 >> You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+[INFO|trainer.py:2302] 2026-04-09 02:46:41,283 >> Didn't find an RNG file, if you are resuming a training that was launched in a distributed fashion, reproducibility is not guaranteed.
+100001it [00:04, 20981.36it/s]            [INFO|trainer.py:1955] 2026-04-09 02:46:43,723 >>
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+100001it [00:04, 20868.88it/s]
+{'train_runtime': 4.7982, 'train_samples_per_second': 2000761.461, 'train_steps_per_second': 20841.265, 'train_loss': 4.9309695196505064e-05, 'epoch': 9.77}
+[INFO|trainer.py:2881] 2026-04-09 02:46:43,749 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120
+[INFO|configuration_utils.py:461] 2026-04-09 02:46:43,751 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 02:46:43,752 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 02:46:44,597 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/pytorch_model.bin
+***** train metrics *****
+  epoch                    =        9.77
+  train_loss               =         0.0
+  train_runtime            =  0:00:04.79
+  train_samples            =      982503
+  train_samples_per_second = 2000761.461
+  train_steps_per_second   =   20841.265
+04/09/2026 02:46:44 - INFO - __main__ - *** Evaluate ***
+[INFO|trainer.py:738] 2026-04-09 02:46:44,604 >> The following columns in the evaluation set don't have a corresponding argument in `BertForMaskedLM.forward` and have been ignored: special_tokens_mask. If special_tokens_mask are not expected by `BertForMaskedLM.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3158] 2026-04-09 02:46:44,623 >> ***** Running Evaluation *****
+[INFO|trainer.py:3160] 2026-04-09 02:46:44,623 >>   Num examples = 109173
+[INFO|trainer.py:3163] 2026-04-09 02:46:44,623 >>   Batch size = 96
+100%|██████████| 1138/1138 [20:09<00:00,  1.06s/it]
+***** eval metrics *****
+  epoch                   =       9.77
+  eval_accuracy           =     0.2987
+  eval_loss               =     4.9606
+  eval_runtime            = 0:20:10.53
+  eval_samples            =     109173
+  eval_samples_per_second =     90.186
+  eval_step               =     100001
+  eval_steps_per_second   =       0.94
+  perplexity              =   142.6779
+[INFO|modelcard.py:452] 2026-04-09 03:06:55,252 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Masked Language Modeling', 'type': 'fill-mask'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.2987349408299944}]}
diff --git a/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/requirements.txt b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1de7237af3385ead30af1186a070d573708d520
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/requirements.txt
@@ -0,0 +1,148 @@
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+charset-normalizer==3.4.4
+nvidia-cublas-cu12==12.8.4.1
+PyQt5_sip==12.13.0
+Bottleneck==1.4.2
+networkx==3.2.1
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+datasets==4.4.1
+huggingface_hub==0.34.4
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+nvidia-cuda-runtime-cu12==12.8.90
+filelock==3.17.0
+aiohappyeyeballs==2.6.1
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diff --git a/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/wandb-metadata.json b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..604411f38f2641404e086ca9ed84a5aaa2f86511
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_024256-4yonu3x2/files/wandb-metadata.json
@@ -0,0 +1,149 @@
+{
+  "os":  "Linux-5.15.0-72-generic-x86_64-with-glibc2.35",
+  "python":  "CPython 3.9.18",
+  "startedAt":  "2026-04-09T02:42:56.385921Z",
+  "args":  [
+    "--output_dir",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120",
+    "--model_type",
+    "bert",
+    "--tokenizer_name",
+    "/home/n5huang/dna_token/tokenizer_evaluation/merge_bpe/vocab_5120/merge_tokenizer_unigram_len2.json",
+    "--config_name",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120/config.json",
+    "--project_name",
+    "token_eval_len2_5120",
+    "--do_train",
+    "True",
+    "--model_max_length",
+    "512",
+    "--max_seq_length",
+    "512",
+    "--line_by_line",
+    "True",
+    "--pad_to_max_length",
+    "True",
+    "--train_file",
+    "/home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_train_chrOnly.tsv",
+    "--validation_file",
+    "/home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_val_chrOnly.tsv",
+    "--cache_dir",
+    "/home/n5huang/dna_token/output_tokens/cache",
+    "--use_fast_tokenizer",
+    "True",
+    "--do_eval",
+    "True",
+    "--gradient_accumulation_steps",
+    "1",
+    "--per_device_train_batch_size",
+    "96",
+    "--per_device_eval_batch_size",
+    "96",
+    "--save_steps",
+    "1000",
+    "--save_total_limit",
+    "10",
+    "--max_steps",
+    "100000",
+    "--logging_steps",
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+    "--learning_rate",
+    "4e-5",
+    "--adam_epsilon",
+    "1e-6",
+    "--weight_decay",
+    "0.01",
+    "--adam_beta1",
+    "0.9",
+    "--adam_beta2",
+    "0.98",
+    "--mlm_probability",
+    "0.15",
+    "--warmup_steps",
+    "10000",
+    "--seed",
+    "42",
+    "--preprocessing_num_workers",
+    "8",
+    "--resume_from_checkpoint",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000"
+  ],
+  "program":  "/home/n5huang/dna-tokenizer/BPE_merge/run_mlm_200k.py",
+  "email":  "mtapiapacheco@ucsd.edu",
+  "root":  "/data/n5huang/dna_token/output_tokens",
+  "host":  "sn4622116170",
+  "executable":  "/home/n5huang/miniconda3/envs/bpe/bin/python",
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+  "gpu":  "NVIDIA A100 80GB PCIe",
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+    },
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+      "architecture":  "Ampere",
+      "uuid":  "GPU-a18369da-b15a-c8af-72f1-7f6272e479af"
+    },
+    {
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+    },
+    {
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+    value: /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000
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+    value: /home/n5huang/dna_token/pretrain/models/len2_5120
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+    value: false
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+    value: /home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_val_chrOnly.tsv
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+    value: 5
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+    value: 5120
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+    value: 0
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+    value: 10000
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+    value: 0.01
diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/output.log b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..c79c35f1fab38c8cf073253cfc9d5962d6cd37dd
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/output.log
@@ -0,0 +1,818 @@
+04/09/2026 03:19:29 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False
+04/09/2026 03:19:29 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
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+overwrite_output_dir=False,
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+resume_from_checkpoint=/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000,
+run_name=/home/n5huang/dna_token/pretrain/models/len2_5120,
+save_on_each_node=False,
+save_safetensors=True,
+save_steps=1000,
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+seed=42,
+skip_memory_metrics=True,
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+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=10000,
+weight_decay=0.01,
+)
+Using custom data configuration default-9aa68439a1bf4533
+04/09/2026 03:19:29 - INFO - datasets.builder - Using custom data configuration default-9aa68439a1bf4533
+Found cached dataset csv (/home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420)
+04/09/2026 03:19:29 - INFO - datasets.builder - Found cached dataset csv (/home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420)
+[INFO|configuration_utils.py:715] 2026-04-09 03:19:29,834 >> loading configuration file /home/n5huang/dna_token/pretrain/models/len2_5120/config.json
+[INFO|configuration_utils.py:777] 2026-04-09 03:19:29,835 >> Model config BertConfig {
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 5120
+}
+04/09/2026 03:19:29 - INFO - __main__ - Training new model from scratch
+
+/home/n5huang/miniconda3/envs/bpe/lib/python3.9/site-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
+  _torch_pytree._register_pytree_node(
+[INFO|configuration_utils.py:791] 2026-04-09 03:19:29,850 >> Generate config GenerationConfig {
+  "max_length": 512,
+  "pad_token_id": 0
+}
+
+Loading cached processed dataset at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_*_of_00008.arrow
+04/09/2026 03:19:48 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-b7e4e83b1e5bb0f0_*_of_00008.arrow
+Concatenating 8 shards
+04/09/2026 03:19:48 - INFO - datasets.arrow_dataset - Concatenating 8 shards
+Loading cached processed dataset at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_*_of_00008.arrow
+04/09/2026 03:19:48 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/n5huang/dna_token/output_tokens/cache/csv/default-9aa68439a1bf4533/0.0.0/a43390c7ecea6519ff2ce9d10005c8750601c9e456069be5efbd2747df45f420/cache-e09c066770dc1f08_*_of_00008.arrow
+Concatenating 8 shards
+04/09/2026 03:19:48 - INFO - datasets.arrow_dataset - Concatenating 8 shards
+[INFO|trainer.py:544] 2026-04-09 03:19:49,478 >> max_steps is given, it will override any value given in num_train_epochs
+[INFO|trainer.py:2066] 2026-04-09 03:19:49,483 >> Loading model from /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000.
+[WARNING|trainer.py:2231] 2026-04-09 03:19:49,780 >> There were missing keys in the checkpoint model loaded: ['cls.predictions.decoder.weight', 'cls.predictions.decoder.bias'].
+[INFO|trainer.py:738] 2026-04-09 03:19:50,098 >> The following columns in the training set don't have a corresponding argument in `BertForMaskedLM.forward` and have been ignored: special_tokens_mask. If special_tokens_mask are not expected by `BertForMaskedLM.forward`,  you can safely ignore this message.
+[INFO|trainer.py:1723] 2026-04-09 03:19:50,767 >> ***** Running training *****
+[INFO|trainer.py:1724] 2026-04-09 03:19:50,768 >>   Num examples = 982,503
+[INFO|trainer.py:1725] 2026-04-09 03:19:50,768 >>   Num Epochs = 20
+[INFO|trainer.py:1726] 2026-04-09 03:19:50,768 >>   Instantaneous batch size per device = 96
+[INFO|trainer.py:1729] 2026-04-09 03:19:50,768 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
+[INFO|trainer.py:1730] 2026-04-09 03:19:50,769 >>   Gradient Accumulation steps = 1
+[INFO|trainer.py:1731] 2026-04-09 03:19:50,769 >>   Total optimization steps = 200,000
+[INFO|trainer.py:1732] 2026-04-09 03:19:50,770 >>   Number of trainable parameters = 89,980,160
+[INFO|trainer.py:1752] 2026-04-09 03:19:50,770 >>   Continuing training from checkpoint, will skip to saved global_step
+[INFO|trainer.py:1753] 2026-04-09 03:19:50,771 >>   Continuing training from epoch 9
+[INFO|trainer.py:1754] 2026-04-09 03:19:50,771 >>   Continuing training from global step 100000
+[INFO|trainer.py:1756] 2026-04-09 03:19:50,771 >>   Will skip the first 9 epochs then the first 7885 batches in the first epoch.
+[INFO|integration_utils.py:718] 2026-04-09 03:19:50,772 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
+  0%|          | 0/200000 [00:00<?, ?it/s][WARNING|logging.py:314] 2026-04-09 03:19:52,558 >> You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+[INFO|trainer.py:2302] 2026-04-09 03:19:53,313 >> Didn't find an RNG file, if you are resuming a training that was launched in a distributed fashion, reproducibility is not guaranteed.
+ 50%|█████     | 101000/200000 [40:44<67:07:24,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 04:00:35,653 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-101000
+{'loss': 5.0155, 'learning_rate': 2.0842105263157895e-05, 'epoch': 9.87}
+[INFO|configuration_utils.py:461] 2026-04-09 04:00:35,654 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-101000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 04:00:35,655 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-101000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 04:00:36,058 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-101000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 04:00:36,791 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-94000] due to args.save_total_limit
+ 51%|█████     | 102000/200000 [1:21:37<72:20:53,  2.66s/it][INFO|trainer.py:2881] 2026-04-09 04:41:28,685 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-102000
+{'loss': 5.024, 'learning_rate': 2.0631578947368425e-05, 'epoch': 9.97}
+[INFO|configuration_utils.py:461] 2026-04-09 04:41:28,698 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-102000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 04:41:28,699 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-102000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 04:41:29,259 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-102000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 04:41:30,045 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-96000] due to args.save_total_limit
+ 52%|█████▏    | 103000/200000 [2:02:36<65:46:04,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 05:22:26,816 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-103000
+{'loss': 5.0241, 'learning_rate': 2.0421052631578952e-05, 'epoch': 10.06}
+[INFO|configuration_utils.py:461] 2026-04-09 05:22:26,818 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-103000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 05:22:26,819 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-103000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 05:22:27,211 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-103000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 05:22:27,970 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-97000] due to args.save_total_limit
+ 52%|█████▏    | 104000/200000 [2:43:23<65:05:24,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 06:03:14,097 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-104000
+{'loss': 5.0218, 'learning_rate': 2.0210526315789475e-05, 'epoch': 10.16}
+[INFO|configuration_utils.py:461] 2026-04-09 06:03:14,099 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-104000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 06:03:14,099 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-104000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 06:03:14,535 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-104000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 06:03:15,296 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-91000] due to args.save_total_limit
+ 52%|█████▎    | 105000/200000 [3:24:07<64:29:01,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 06:43:58,058 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-105000
+{'loss': 5.0231, 'learning_rate': 2e-05, 'epoch': 10.26}
+[INFO|configuration_utils.py:461] 2026-04-09 06:43:58,059 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-105000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 06:43:58,060 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-105000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 06:43:58,464 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-105000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 06:43:59,223 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-95000] due to args.save_total_limit
+ 53%|█████▎    | 106000/200000 [4:04:49<63:43:26,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 07:24:40,060 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-106000
+{'loss': 5.0231, 'learning_rate': 1.9789473684210528e-05, 'epoch': 10.36}
+[INFO|configuration_utils.py:461] 2026-04-09 07:24:40,062 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-106000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 07:24:40,062 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-106000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 07:24:40,480 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-106000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 07:24:41,226 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-98000] due to args.save_total_limit
+ 54%|█████▎    | 107000/200000 [4:45:35<62:59:42,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 08:05:26,739 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-107000
+{'loss': 5.0198, 'learning_rate': 1.9578947368421055e-05, 'epoch': 10.45}
+[INFO|configuration_utils.py:461] 2026-04-09 08:05:26,742 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-107000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 08:05:26,742 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-107000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 08:05:27,195 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-107000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 08:05:28,021 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000] due to args.save_total_limit
+ 54%|█████▍    | 108000/200000 [5:26:40<62:28:00,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 08:46:31,605 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-108000
+{'loss': 5.0158, 'learning_rate': 1.936842105263158e-05, 'epoch': 10.55}
+[INFO|configuration_utils.py:461] 2026-04-09 08:46:31,607 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-108000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 08:46:31,607 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-108000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 08:46:32,105 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-108000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 08:46:32,861 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-92000] due to args.save_total_limit
+ 55%|█████▍    | 109000/200000 [6:07:40<61:37:11,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 09:27:31,137 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-109000
+{'loss': 5.0122, 'learning_rate': 1.9157894736842108e-05, 'epoch': 10.65}
+[INFO|configuration_utils.py:461] 2026-04-09 09:27:31,139 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-109000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 09:27:31,139 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-109000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 09:27:31,591 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-109000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 09:27:32,411 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-93000] due to args.save_total_limit
+ 55%|█████▌    | 110000/200000 [6:48:21<60:59:38,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 10:08:12,011 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-110000
+{'loss': 5.0165, 'learning_rate': 1.894736842105263e-05, 'epoch': 10.75}
+[INFO|configuration_utils.py:461] 2026-04-09 10:08:12,012 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-110000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 10:08:12,013 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-110000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 10:08:12,441 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-110000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 10:08:13,239 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-99000] due to args.save_total_limit
+ 56%|█████▌    | 111000/200000 [7:29:02<60:25:39,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 10:48:53,331 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-111000
+{'loss': 5.0183, 'learning_rate': 1.873684210526316e-05, 'epoch': 10.85}
+[INFO|configuration_utils.py:461] 2026-04-09 10:48:53,332 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-111000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 10:48:53,333 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-111000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 10:48:53,728 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-111000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 10:48:54,454 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-101000] due to args.save_total_limit
+ 56%|█████▌    | 112000/200000 [8:09:43<59:38:19,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 11:29:34,739 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-112000
+{'loss': 5.01, 'learning_rate': 1.8526315789473684e-05, 'epoch': 10.94}
+[INFO|configuration_utils.py:461] 2026-04-09 11:29:34,740 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-112000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 11:29:34,741 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-112000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 11:29:35,140 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-112000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 11:29:35,857 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-102000] due to args.save_total_limit
+ 56%|█████▋    | 113000/200000 [8:50:24<58:52:08,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 12:10:14,913 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-113000
+{'loss': 5.007, 'learning_rate': 1.831578947368421e-05, 'epoch': 11.04}
+[INFO|configuration_utils.py:461] 2026-04-09 12:10:14,914 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-113000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 12:10:14,915 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-113000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 12:10:15,317 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-113000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 12:10:16,132 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-103000] due to args.save_total_limit
+ 57%|█████▋    | 114000/200000 [9:31:04<58:19:26,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 12:50:55,563 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-114000
+{'loss': 5.0059, 'learning_rate': 1.810526315789474e-05, 'epoch': 11.14}
+[INFO|configuration_utils.py:461] 2026-04-09 12:50:55,565 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-114000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 12:50:55,566 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-114000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 12:50:55,973 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-114000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 12:50:56,703 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-104000] due to args.save_total_limit
+ 57%|█████▊    | 115000/200000 [10:11:46<57:32:08,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 13:31:37,626 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-115000
+{'loss': 5.0089, 'learning_rate': 1.7894736842105264e-05, 'epoch': 11.24}
+[INFO|configuration_utils.py:461] 2026-04-09 13:31:37,628 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-115000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 13:31:37,628 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-115000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 13:31:38,040 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-115000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 13:31:38,837 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-105000] due to args.save_total_limit
+ 58%|█████▊    | 116000/200000 [10:52:27<56:55:54,  2.44s/it][INFO|trainer.py:2881] 2026-04-09 14:12:18,728 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-116000
+{'loss': 5.0081, 'learning_rate': 1.768421052631579e-05, 'epoch': 11.33}
+[INFO|configuration_utils.py:461] 2026-04-09 14:12:18,730 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-116000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 14:12:18,730 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-116000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 14:12:19,139 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-116000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 14:12:19,967 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-106000] due to args.save_total_limit
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+{'loss': 5.002, 'learning_rate': 1.7473684210526317e-05, 'epoch': 11.43}
+[INFO|configuration_utils.py:461] 2026-04-09 14:53:00,999 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-117000/config.json
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+{'loss': 5.0052, 'learning_rate': 1.7263157894736843e-05, 'epoch': 11.53}
+[INFO|configuration_utils.py:461] 2026-04-09 15:34:47,554 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-118000/config.json
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+{'loss': 5.0079, 'learning_rate': 1.705263157894737e-05, 'epoch': 11.63}
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+{'loss': 4.9985, 'learning_rate': 1.6842105263157896e-05, 'epoch': 11.72}
+[INFO|configuration_utils.py:461] 2026-04-09 16:57:56,957 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-120000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 16:57:57,334 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-120000/pytorch_model.bin
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+{'loss': 5.006, 'learning_rate': 1.6631578947368423e-05, 'epoch': 11.82}
+[INFO|configuration_utils.py:461] 2026-04-09 17:40:05,809 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-121000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 17:40:06,334 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-121000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 17:40:07,153 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-111000] due to args.save_total_limit
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+{'loss': 4.9956, 'learning_rate': 1.642105263157895e-05, 'epoch': 11.92}
+[INFO|configuration_utils.py:461] 2026-04-09 18:21:20,511 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-122000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 18:21:20,511 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-122000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 18:21:21,079 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-122000/pytorch_model.bin
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+{'loss': 4.9973, 'learning_rate': 1.6210526315789473e-05, 'epoch': 12.02}
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+[INFO|modeling_utils.py:2193] 2026-04-09 19:02:39,240 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-123000/pytorch_model.bin
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+{'loss': 4.9976, 'learning_rate': 1.6000000000000003e-05, 'epoch': 12.12}
+[INFO|configuration_utils.py:461] 2026-04-09 19:43:34,168 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-124000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 19:43:34,721 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-124000/pytorch_model.bin
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+{'loss': 4.9882, 'learning_rate': 1.578947368421053e-05, 'epoch': 12.21}
+[INFO|configuration_utils.py:461] 2026-04-09 20:26:27,990 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-125000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 20:26:28,497 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-125000/pytorch_model.bin
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+{'loss': 4.9908, 'learning_rate': 1.5578947368421052e-05, 'epoch': 12.31}
+[INFO|configuration_utils.py:461] 2026-04-09 21:08:38,652 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-126000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 21:08:39,399 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-126000/pytorch_model.bin
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+{'loss': 5.0038, 'learning_rate': 1.536842105263158e-05, 'epoch': 12.41}
+[INFO|configuration_utils.py:461] 2026-04-09 21:49:26,300 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-127000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 21:49:26,696 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-127000/pytorch_model.bin
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+{'loss': 4.9923, 'learning_rate': 1.5157894736842107e-05, 'epoch': 12.51}
+[INFO|configuration_utils.py:461] 2026-04-09 22:30:08,374 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-128000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-09 22:30:08,839 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-128000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 22:30:09,725 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-118000] due to args.save_total_limit
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+{'loss': 4.9847, 'learning_rate': 1.4947368421052632e-05, 'epoch': 12.6}
+[INFO|configuration_utils.py:461] 2026-04-09 23:10:55,495 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-129000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 23:10:55,496 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-129000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 23:10:56,009 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-129000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 23:10:56,990 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-119000] due to args.save_total_limit
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+{'loss': 4.9864, 'learning_rate': 1.4736842105263159e-05, 'epoch': 12.7}
+[INFO|configuration_utils.py:461] 2026-04-09 23:53:25,295 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-130000/config.json
+[INFO|configuration_utils.py:564] 2026-04-09 23:53:25,295 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-130000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-09 23:53:25,674 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-130000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-09 23:53:26,493 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-120000] due to args.save_total_limit
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+{'loss': 4.9903, 'learning_rate': 1.4526315789473687e-05, 'epoch': 12.8}
+[INFO|configuration_utils.py:461] 2026-04-10 00:35:32,118 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-131000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 00:35:32,521 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-131000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 00:35:33,330 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-121000] due to args.save_total_limit
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+{'loss': 4.9812, 'learning_rate': 1.4315789473684212e-05, 'epoch': 12.9}
+[INFO|configuration_utils.py:461] 2026-04-10 01:16:50,873 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-132000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 01:16:51,439 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-132000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 01:16:52,343 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-122000] due to args.save_total_limit
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+{'loss': 4.9923, 'learning_rate': 1.4105263157894738e-05, 'epoch': 12.99}
+[INFO|configuration_utils.py:461] 2026-04-10 01:58:09,148 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-133000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 01:58:09,609 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-133000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 01:58:10,583 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-123000] due to args.save_total_limit
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+{'loss': 4.9838, 'learning_rate': 1.3894736842105265e-05, 'epoch': 13.09}
+[INFO|configuration_utils.py:461] 2026-04-10 02:39:43,029 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-134000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 02:39:43,579 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-134000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 02:39:44,519 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-124000] due to args.save_total_limit
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+{'loss': 4.977, 'learning_rate': 1.3684210526315791e-05, 'epoch': 13.19}
+[INFO|configuration_utils.py:461] 2026-04-10 03:21:18,469 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-135000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 03:21:19,066 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-135000/pytorch_model.bin
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+{'loss': 4.9743, 'learning_rate': 1.3473684210526316e-05, 'epoch': 13.29}
+[INFO|configuration_utils.py:461] 2026-04-10 04:03:56,796 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-136000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 04:03:57,293 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-136000/pytorch_model.bin
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+{'loss': 4.9797, 'learning_rate': 1.3263157894736843e-05, 'epoch': 13.39}
+[INFO|configuration_utils.py:461] 2026-04-10 04:46:50,811 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-137000/config.json
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+{'loss': 4.9915, 'learning_rate': 1.305263157894737e-05, 'epoch': 13.48}
+[INFO|configuration_utils.py:461] 2026-04-10 05:30:29,192 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-138000/config.json
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+{'loss': 4.9762, 'learning_rate': 1.2842105263157896e-05, 'epoch': 13.58}
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+{'loss': 4.9771, 'learning_rate': 1.263157894736842e-05, 'epoch': 13.68}
+[INFO|configuration_utils.py:461] 2026-04-10 06:54:48,734 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-140000/config.json
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+{'loss': 4.9763, 'learning_rate': 1.2421052631578949e-05, 'epoch': 13.78}
+[INFO|configuration_utils.py:461] 2026-04-10 07:35:34,994 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-141000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 07:35:35,487 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-141000/pytorch_model.bin
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+{'loss': 4.9706, 'learning_rate': 1.2210526315789475e-05, 'epoch': 13.87}
+[INFO|configuration_utils.py:461] 2026-04-10 08:16:20,634 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-142000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 08:16:21,053 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-142000/pytorch_model.bin
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+{'loss': 4.9731, 'learning_rate': 1.2e-05, 'epoch': 13.97}
+[INFO|configuration_utils.py:461] 2026-04-10 08:57:08,496 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-143000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 08:57:09,137 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-143000/pytorch_model.bin
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+{'loss': 4.9736, 'learning_rate': 1.1789473684210527e-05, 'epoch': 14.07}
+[INFO|configuration_utils.py:461] 2026-04-10 09:37:54,006 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-144000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 09:37:54,463 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-144000/pytorch_model.bin
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+{'loss': 4.9704, 'learning_rate': 1.1578947368421053e-05, 'epoch': 14.17}
+[INFO|configuration_utils.py:461] 2026-04-10 10:18:39,575 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-145000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 10:18:40,031 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-145000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 10:18:40,905 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-135000] due to args.save_total_limit
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+{'loss': 4.9762, 'learning_rate': 1.136842105263158e-05, 'epoch': 14.26}
+[INFO|configuration_utils.py:461] 2026-04-10 10:59:24,593 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-146000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 10:59:24,989 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-146000/pytorch_model.bin
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+{'loss': 4.9709, 'learning_rate': 1.1157894736842105e-05, 'epoch': 14.36}
+[INFO|configuration_utils.py:461] 2026-04-10 11:40:07,876 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-147000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 11:40:08,277 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-147000/pytorch_model.bin
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+{'loss': 4.957, 'learning_rate': 1.0947368421052633e-05, 'epoch': 14.46}
+[INFO|configuration_utils.py:461] 2026-04-10 12:20:52,235 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-148000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 12:20:52,649 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-148000/pytorch_model.bin
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+{'loss': 4.9666, 'learning_rate': 1.073684210526316e-05, 'epoch': 14.56}
+[INFO|configuration_utils.py:461] 2026-04-10 13:01:38,003 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-149000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 13:01:38,445 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-149000/pytorch_model.bin
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+{'loss': 4.9623, 'learning_rate': 1.0526315789473684e-05, 'epoch': 14.66}
+[INFO|configuration_utils.py:461] 2026-04-10 13:42:24,624 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-150000/config.json
+[INFO|configuration_utils.py:564] 2026-04-10 13:42:24,624 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-150000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-10 13:42:25,021 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-150000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 13:42:25,816 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-140000] due to args.save_total_limit
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+{'loss': 4.9613, 'learning_rate': 1.0315789473684213e-05, 'epoch': 14.75}
+[INFO|configuration_utils.py:461] 2026-04-10 14:23:23,737 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-151000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 14:23:24,166 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-151000/pytorch_model.bin
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+{'loss': 4.9642, 'learning_rate': 1.0105263157894738e-05, 'epoch': 14.85}
+[INFO|configuration_utils.py:461] 2026-04-10 18:28:18,286 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-152000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 18:28:18,737 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-152000/pytorch_model.bin
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+{'loss': 4.9605, 'learning_rate': 9.894736842105264e-06, 'epoch': 14.95}
+[INFO|configuration_utils.py:461] 2026-04-10 19:10:12,103 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-153000/config.json
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+{'loss': 4.9688, 'learning_rate': 9.68421052631579e-06, 'epoch': 15.05}
+[INFO|configuration_utils.py:461] 2026-04-10 19:50:49,445 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-154000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 19:50:49,882 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-154000/pytorch_model.bin
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+{'loss': 4.9635, 'learning_rate': 9.473684210526315e-06, 'epoch': 15.14}
+[INFO|configuration_utils.py:461] 2026-04-10 20:31:30,686 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-155000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 20:31:31,069 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-155000/pytorch_model.bin
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+{'loss': 4.9587, 'learning_rate': 9.263157894736842e-06, 'epoch': 15.24}
+[INFO|configuration_utils.py:461] 2026-04-10 21:12:12,058 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-156000/config.json
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+[INFO|trainer.py:2971] 2026-04-10 21:12:13,235 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-146000] due to args.save_total_limit
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+{'loss': 4.9633, 'learning_rate': 9.05263157894737e-06, 'epoch': 15.34}
+[INFO|configuration_utils.py:461] 2026-04-10 21:52:52,516 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-157000/config.json
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+[INFO|trainer.py:2971] 2026-04-10 21:52:53,705 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-147000] due to args.save_total_limit
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+{'loss': 4.9613, 'learning_rate': 8.842105263157895e-06, 'epoch': 15.44}
+[INFO|configuration_utils.py:461] 2026-04-10 22:33:32,661 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-158000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 22:33:33,031 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-158000/pytorch_model.bin
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+{'loss': 4.9629, 'learning_rate': 8.631578947368422e-06, 'epoch': 15.53}
+[INFO|configuration_utils.py:461] 2026-04-10 23:14:12,645 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-159000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-10 23:14:13,042 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-159000/pytorch_model.bin
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+{'loss': 4.9547, 'learning_rate': 8.421052631578948e-06, 'epoch': 15.63}
+[INFO|configuration_utils.py:461] 2026-04-10 23:54:52,757 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-160000/config.json
+[INFO|configuration_utils.py:564] 2026-04-10 23:54:52,758 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-160000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-10 23:54:53,158 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-160000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-10 23:54:53,955 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-150000] due to args.save_total_limit
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+{'loss': 4.9541, 'learning_rate': 8.210526315789475e-06, 'epoch': 15.73}
+[INFO|configuration_utils.py:461] 2026-04-11 00:35:33,626 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-161000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 00:35:33,627 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-161000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 00:35:34,070 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-161000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 00:35:34,905 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-151000] due to args.save_total_limit
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+{'loss': 4.9532, 'learning_rate': 8.000000000000001e-06, 'epoch': 15.83}
+[INFO|configuration_utils.py:461] 2026-04-11 01:16:14,574 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-162000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 01:16:14,574 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-162000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 01:16:15,012 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-162000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 01:16:15,826 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-152000] due to args.save_total_limit
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+{'loss': 4.9461, 'learning_rate': 7.789473684210526e-06, 'epoch': 15.93}
+[INFO|configuration_utils.py:461] 2026-04-11 01:56:55,266 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-163000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 01:56:55,267 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-163000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 01:56:55,661 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-163000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 01:56:56,473 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-153000] due to args.save_total_limit
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+{'loss': 4.9518, 'learning_rate': 7.578947368421054e-06, 'epoch': 16.02}
+[INFO|configuration_utils.py:461] 2026-04-11 02:37:55,627 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-164000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 02:37:55,628 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-164000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 02:37:56,113 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-164000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 02:37:56,903 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-154000] due to args.save_total_limit
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+{'loss': 4.9543, 'learning_rate': 7.368421052631579e-06, 'epoch': 16.12}
+[INFO|configuration_utils.py:461] 2026-04-11 03:19:37,627 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-165000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 03:19:37,628 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-165000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 03:19:37,978 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-165000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 03:19:39,058 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-155000] due to args.save_total_limit
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+{'loss': 4.9526, 'learning_rate': 7.157894736842106e-06, 'epoch': 16.22}
+[INFO|configuration_utils.py:461] 2026-04-11 04:01:22,572 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-166000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 04:01:22,573 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-166000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 04:01:22,937 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-166000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 04:01:23,733 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-156000] due to args.save_total_limit
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+{'loss': 4.9447, 'learning_rate': 6.947368421052632e-06, 'epoch': 16.32}
+[INFO|configuration_utils.py:461] 2026-04-11 04:42:34,827 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-167000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 04:42:34,827 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-167000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 04:42:35,180 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-167000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 04:42:36,144 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-157000] due to args.save_total_limit
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+{'loss': 4.9545, 'learning_rate': 6.736842105263158e-06, 'epoch': 16.41}
+[INFO|configuration_utils.py:461] 2026-04-11 05:23:43,268 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-168000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 05:23:43,269 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-168000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 05:23:43,641 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-168000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 05:23:44,415 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-158000] due to args.save_total_limit
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+{'loss': 4.9448, 'learning_rate': 6.526315789473685e-06, 'epoch': 16.51}
+[INFO|configuration_utils.py:461] 2026-04-11 06:05:33,584 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-169000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-11 06:05:34,051 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-169000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 06:05:34,864 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-159000] due to args.save_total_limit
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+{'loss': 4.9521, 'learning_rate': 6.31578947368421e-06, 'epoch': 16.61}
+[INFO|configuration_utils.py:461] 2026-04-11 06:47:11,058 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-170000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 06:47:11,058 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-170000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 06:47:11,453 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-170000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 06:47:12,148 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-160000] due to args.save_total_limit
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+{'loss': 4.9503, 'learning_rate': 6.105263157894738e-06, 'epoch': 16.71}
+[INFO|configuration_utils.py:461] 2026-04-11 07:27:53,369 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-171000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 07:27:53,370 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-171000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 07:27:53,731 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-171000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 07:27:54,518 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-161000] due to args.save_total_limit
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+{'loss': 4.9537, 'learning_rate': 5.8947368421052634e-06, 'epoch': 16.81}
+[INFO|configuration_utils.py:461] 2026-04-11 08:08:33,832 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-172000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 08:08:33,833 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-172000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 08:08:34,267 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-172000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 08:08:35,078 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-162000] due to args.save_total_limit
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+{'loss': 4.9446, 'learning_rate': 5.68421052631579e-06, 'epoch': 16.9}
+[INFO|configuration_utils.py:461] 2026-04-11 08:49:15,296 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-173000/config.json
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+[INFO|modeling_utils.py:2193] 2026-04-11 08:49:15,652 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-173000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 08:49:16,415 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-163000] due to args.save_total_limit
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+{'loss': 4.9397, 'learning_rate': 5.4736842105263165e-06, 'epoch': 17.0}
+[INFO|configuration_utils.py:461] 2026-04-11 09:29:55,906 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-174000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 09:29:55,906 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-174000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 09:29:56,357 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-174000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 09:29:57,122 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-164000] due to args.save_total_limit
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+{'loss': 4.9541, 'learning_rate': 5.263157894736842e-06, 'epoch': 17.1}
+[INFO|configuration_utils.py:461] 2026-04-11 10:10:35,831 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-175000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 10:10:35,832 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-175000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 10:10:36,197 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-175000/pytorch_model.bin
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+{'loss': 4.9449, 'learning_rate': 5.052631578947369e-06, 'epoch': 17.2}
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+{'loss': 4.9431, 'learning_rate': 4.000000000000001e-06, 'epoch': 17.68}
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+{'loss': 4.9399, 'learning_rate': 3.578947368421053e-06, 'epoch': 17.88}
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+{'loss': 4.9481, 'learning_rate': 3.368421052631579e-06, 'epoch': 17.98}
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+{'loss': 4.9399, 'learning_rate': 3.157894736842105e-06, 'epoch': 18.08}
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+{'loss': 4.9307, 'learning_rate': 2.7368421052631583e-06, 'epoch': 18.27}
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+{'loss': 4.9391, 'learning_rate': 2.5263157894736844e-06, 'epoch': 18.37}
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+[INFO|modeling_utils.py:2193] 2026-04-11 18:59:49,039 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-188000/pytorch_model.bin
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+{'loss': 4.9331, 'learning_rate': 2.3157894736842105e-06, 'epoch': 18.47}
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+{'loss': 4.948, 'learning_rate': 2.105263157894737e-06, 'epoch': 18.56}
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+{'loss': 4.944, 'learning_rate': 1.6842105263157895e-06, 'epoch': 18.76}
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+{'loss': 4.9286, 'learning_rate': 1.4736842105263159e-06, 'epoch': 18.86}
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+{'loss': 4.9338, 'learning_rate': 1.2631578947368422e-06, 'epoch': 18.95}
+[INFO|configuration_utils.py:461] 2026-04-11 23:04:24,645 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-194000/config.json
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+{'loss': 4.9357, 'learning_rate': 1.0526315789473685e-06, 'epoch': 19.05}
+[INFO|configuration_utils.py:461] 2026-04-11 23:45:08,642 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-195000/config.json
+[INFO|configuration_utils.py:564] 2026-04-11 23:45:08,643 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-195000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-11 23:45:09,078 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-195000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-11 23:45:09,973 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-185000] due to args.save_total_limit
+ 98%|█████████▊| 196000/200000 [69:05:59<2:42:32,  2.44s/it][INFO|trainer.py:2881] 2026-04-12 00:25:50,682 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-196000
+{'loss': 4.9327, 'learning_rate': 8.421052631578948e-07, 'epoch': 19.15}
+[INFO|configuration_utils.py:461] 2026-04-12 00:25:50,684 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-196000/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 00:25:50,685 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-196000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 00:25:51,071 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-196000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-12 00:25:51,858 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-186000] due to args.save_total_limit
+ 98%|█████████▊| 197000/200000 [69:46:44<2:02:12,  2.44s/it][INFO|trainer.py:2881] 2026-04-12 01:06:35,637 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-197000
+{'loss': 4.9352, 'learning_rate': 6.315789473684211e-07, 'epoch': 19.25}
+[INFO|configuration_utils.py:461] 2026-04-12 01:06:35,638 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-197000/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 01:06:35,639 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-197000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 01:06:36,031 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-197000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-12 01:06:36,849 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-187000] due to args.save_total_limit
+ 99%|█████████▉| 198000/200000 [70:27:31<1:21:23,  2.44s/it][INFO|trainer.py:2881] 2026-04-12 01:47:21,869 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-198000
+{'loss': 4.9362, 'learning_rate': 4.210526315789474e-07, 'epoch': 19.35}
+[INFO|configuration_utils.py:461] 2026-04-12 01:47:21,870 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-198000/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 01:47:21,871 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-198000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 01:47:22,263 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-198000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-12 01:47:23,095 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-188000] due to args.save_total_limit
+100%|█████████▉| 199000/200000 [71:08:17<40:46,  2.45s/it][INFO|trainer.py:2881] 2026-04-12 02:28:08,270 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-199000
+{'loss': 4.9306, 'learning_rate': 2.105263157894737e-07, 'epoch': 19.44}
+[INFO|configuration_utils.py:461] 2026-04-12 02:28:08,272 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-199000/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 02:28:08,272 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-199000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 02:28:08,686 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-199000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-12 02:28:09,527 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-189000] due to args.save_total_limit
+100%|██████████| 200000/200000 [71:49:03<00:00,  2.44s/it][INFO|trainer.py:2881] 2026-04-12 03:08:54,444 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-200000
+{'loss': 4.9315, 'learning_rate': 0.0, 'epoch': 19.54}
+[INFO|configuration_utils.py:461] 2026-04-12 03:08:54,445 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-200000/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 03:08:54,446 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-200000/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 03:08:54,828 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-200000/pytorch_model.bin
+[INFO|trainer.py:2971] 2026-04-12 03:08:55,669 >> Deleting older checkpoint [/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-190000] due to args.save_total_limit
+[INFO|trainer.py:1955] 2026-04-12 03:08:55,820 >>
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+100%|██████████| 200000/200000 [71:49:05<00:00,  1.29s/it]
+{'train_runtime': 258545.0504, 'train_samples_per_second': 74.262, 'train_steps_per_second': 0.774, 'train_loss': 2.48521353515625, 'epoch': 19.54}
+[INFO|trainer.py:2881] 2026-04-12 03:08:55,860 >> Saving model checkpoint to /home/n5huang/dna_token/pretrain/models/len2_5120
+[INFO|configuration_utils.py:461] 2026-04-12 03:08:55,862 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/config.json
+[INFO|configuration_utils.py:564] 2026-04-12 03:08:55,863 >> Configuration saved in /home/n5huang/dna_token/pretrain/models/len2_5120/generation_config.json
+[INFO|modeling_utils.py:2193] 2026-04-12 03:08:56,630 >> Model weights saved in /home/n5huang/dna_token/pretrain/models/len2_5120/pytorch_model.bin
+***** train metrics *****
+  epoch                    =               19.54
+  train_loss               =              2.4852
+  train_runtime            = 2 days, 23:49:05.05
+  train_samples            =              982503
+  train_samples_per_second =              74.262
+  train_steps_per_second   =               0.774
+04/12/2026 03:08:56 - INFO - __main__ - *** Evaluate ***
+[INFO|trainer.py:738] 2026-04-12 03:08:56,638 >> The following columns in the evaluation set don't have a corresponding argument in `BertForMaskedLM.forward` and have been ignored: special_tokens_mask. If special_tokens_mask are not expected by `BertForMaskedLM.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3158] 2026-04-12 03:08:56,644 >> ***** Running Evaluation *****
+[INFO|trainer.py:3160] 2026-04-12 03:08:56,645 >>   Num examples = 109173
+[INFO|trainer.py:3163] 2026-04-12 03:08:56,645 >>   Batch size = 96
+100%|██████████| 1138/1138 [20:58<00:00,  1.11s/it]
+***** eval metrics *****
+  epoch                   =      19.54
+  eval_accuracy           =     0.3043
+  eval_loss               =     4.9009
+  eval_runtime            = 0:20:59.82
+  eval_samples            =     109173
+  eval_samples_per_second =     86.657
+  eval_step               =     200000
+  eval_steps_per_second   =      0.903
+  perplexity              =   134.4083
+[INFO|modelcard.py:452] 2026-04-12 03:29:56,513 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Masked Language Modeling', 'type': 'fill-mask'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.30433709022367145}]}
diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/requirements.txt b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1de7237af3385ead30af1186a070d573708d520
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/requirements.txt
@@ -0,0 +1,148 @@
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+attrs==25.4.0
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+nvidia-cusparse-cu12==12.5.8.93
+GitPython==3.1.45
+PyQt6_sip==13.10.2
+nvidia-nvtx-cu12==12.8.90
+nest_asyncio==1.6.0
+cffi==2.0.0
+tqdm==4.67.1
+tzdata==2025.2
+dill==0.4.0
+cycler==0.11.0
+aiohttp==3.13.2
+pydantic==2.11.7
+pickleshare==0.7.5
+nvidia-cusparselt-cu12==0.7.1
+contourpy==1.2.1
+platformdirs==4.3.8
+importlib_metadata==8.7.0
+biopython==1.85
+idna==3.11
+stack_data==0.6.3
+pycparser==2.23
+python-dateutil==2.9.0.post0
+wheel==0.45.1
+psutil==7.0.0
+importlib_resources==6.5.2
+pyparsing==3.2.0
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+gitdb==4.0.12
+pip==25.3
+pillow==11.3.0
+jupyter_core==5.8.1
+transformers==4.29.2
+matplotlib-inline==0.1.7
+packaging==25.0
+nvidia-nccl-cu12==2.27.3
+debugpy==1.8.16
+ipython==8.18.1
+anyio==4.12.0
+prompt_toolkit==3.0.51
+mkl_fft==1.3.11
+httpcore==1.0.9
+Jinja2==3.1.6
+ply==3.11
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+setproctitle==1.3.6
+docker-pycreds==0.4.0
+nvidia-cuda-nvrtc-cu12==12.8.93
+urllib3==2.5.0
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+llvmlite==0.43.0
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+wcwidth==0.2.13
+numexpr==2.10.1
+click==8.1.8
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+threadpoolctl==3.6.0
+typing_extensions==4.15.0
+decorator==5.2.1
+sip==6.7.12
+pure_eval==0.2.3
+async-timeout==5.0.1
+Pygments==2.19.2
+mkl-service==2.4.0
+transformers==4.35.2
+numba==0.60.0
+pyzmq==27.0.2
+mkl_random==1.2.8
+ptyprocess==0.7.0
+nvidia-cuda-cupti-cu12==12.8.90
+charset-normalizer==3.4.4
+nvidia-cublas-cu12==12.8.4.1
+PyQt5_sip==12.13.0
+Bottleneck==1.4.2
+networkx==3.2.1
+PySocks==1.7.1
+pysam==0.9.1
+tornado==6.5.2
+pandas==2.3.3
+smmap==5.0.2
+PyQt5==5.15.10
+nvidia-cufft-cu12==11.3.3.83
+safetensors==0.7.0
+requests==2.32.5
+pytz==2025.2
+fsspec==2025.7.0
+certifi==2025.10.5
+sentry-sdk==2.35.0
+parso==0.8.4
+aiosignal==1.4.0
+traitlets==5.14.3
+hf-xet==1.1.8
+nvidia-cudnn-cu12==9.10.2.21
+accelerate==0.25.0
+eval_type_backport==0.2.2
+datasets==4.4.1
+huggingface_hub==0.34.4
+wandb==0.23.1
+tomli==2.2.1
+comm==0.2.3
+executing==2.2.0
+PyYAML==6.0.2
+scipy==1.13.1
+pexpect==4.9.0
+matplotlib==3.9.2
+annotated-types==0.7.0
+asttokens==3.0.0
+tokenizers==0.15.2
+nvidia-cufile-cu12==1.13.1.3
+appdirs==1.4.4
+h11==0.16.0
+numpy==2.0.2
+jupyter_client==8.6.3
+joblib==1.5.1
+joblib==1.5.3
+jedi==0.19.2
+MarkupSafe==3.0.3
+nvidia-nvjitlink-cu12==12.8.93
+pyarrow==21.0.0
+torch==2.8.0
+protobuf==3.20.3
+propcache==0.4.1
+multidict==6.7.0
+nvidia-cusolver-cu12==11.7.3.90
+umap-learn==0.5.9.post2
+ipykernel==6.30.1
+triton==3.4.0
+sympy==1.14.0
+scikit-learn==1.6.1
+brotlicffi==1.0.9.2
+xxhash==3.6.0
+seaborn==0.13.2
+pynndescent==0.5.13
+regex==2025.11.3
+typing-inspection==0.4.1
+evaluate==0.4.6
+nvidia-cuda-runtime-cu12==12.8.90
+filelock==3.17.0
+aiohappyeyeballs==2.6.1
+setuptools==80.9.0
diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-metadata.json b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..7355534ebd8a80cf6d598f872e2fc97f1cf8c5d2
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-metadata.json
@@ -0,0 +1,149 @@
+{
+  "os":  "Linux-5.15.0-72-generic-x86_64-with-glibc2.35",
+  "python":  "CPython 3.9.18",
+  "startedAt":  "2026-04-09T03:19:27.923698Z",
+  "args":  [
+    "--output_dir",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120",
+    "--model_type",
+    "bert",
+    "--tokenizer_name",
+    "/home/n5huang/dna_token/tokenizer_evaluation/merge_bpe/vocab_5120/merge_tokenizer_unigram_len2.json",
+    "--config_name",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120/config.json",
+    "--project_name",
+    "token_eval_len2_5120",
+    "--do_train",
+    "True",
+    "--model_max_length",
+    "512",
+    "--max_seq_length",
+    "512",
+    "--line_by_line",
+    "True",
+    "--pad_to_max_length",
+    "True",
+    "--train_file",
+    "/home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_train_chrOnly.tsv",
+    "--validation_file",
+    "/home/n5huang/dna_token/output_tokens/merge_bpe_5120_allchr_all_tokenized_val_chrOnly.tsv",
+    "--cache_dir",
+    "/home/n5huang/dna_token/output_tokens/cache",
+    "--use_fast_tokenizer",
+    "True",
+    "--do_eval",
+    "True",
+    "--gradient_accumulation_steps",
+    "1",
+    "--per_device_train_batch_size",
+    "96",
+    "--per_device_eval_batch_size",
+    "96",
+    "--save_steps",
+    "1000",
+    "--save_total_limit",
+    "10",
+    "--max_steps",
+    "200000",
+    "--logging_steps",
+    "1000",
+    "--learning_rate",
+    "4e-5",
+    "--adam_epsilon",
+    "1e-6",
+    "--weight_decay",
+    "0.01",
+    "--adam_beta1",
+    "0.9",
+    "--adam_beta2",
+    "0.98",
+    "--mlm_probability",
+    "0.15",
+    "--warmup_steps",
+    "10000",
+    "--seed",
+    "42",
+    "--preprocessing_num_workers",
+    "8",
+    "--resume_from_checkpoint",
+    "/home/n5huang/dna_token/pretrain/models/len2_5120/checkpoint-100000"
+  ],
+  "program":  "/home/n5huang/dna-tokenizer/BPE_merge/run_mlm_200k.py",
+  "email":  "mtapiapacheco@ucsd.edu",
+  "root":  "/data/n5huang/dna_token/output_tokens",
+  "host":  "sn4622116170",
+  "executable":  "/home/n5huang/miniconda3/envs/bpe/bin/python",
+  "cpu_count":  128,
+  "cpu_count_logical":  255,
+  "gpu":  "NVIDIA A100 80GB PCIe",
+  "gpu_count":  8,
+  "disk":  {
+    "/":  {
+      "total":  "1876350566400",
+      "used":  "405397532672"
+    }
+  },
+  "memory":  {
+    "total":  "4316310355968"
+  },
+  "gpu_nvidia":  [
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-dc4eaee8-f907-9592-417e-e0dbe37f86a3"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-054a4d72-8520-5e4a-5cd3-7519a0966809"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-8d529af8-bcca-cbbc-fa00-78bf605d4eab"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-a18369da-b15a-c8af-72f1-7f6272e479af"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-fb2ffc63-6c8a-5e90-0466-bd865bdd8b10"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-b8dce99b-6337-0212-03f4-56e6e689fa2e"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-d097eb04-aaca-ea9a-2730-e29dca4737a0"
+    },
+    {
+      "name":  "NVIDIA A100 80GB PCIe",
+      "memoryTotal":  "85899345920",
+      "cudaCores":  6912,
+      "architecture":  "Ampere",
+      "uuid":  "GPU-889b7e9a-34bf-f9b3-1168-16a2b3798044"
+    }
+  ],
+  "cudaVersion":  "12.2",
+  "writerId":  "shyl6ozoxin31qqkhj4f84yadhwytjka"
+}
\ No newline at end of file
diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-summary.json b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5980af703accdda69d3baaf22faa9c6ffe4f639b
--- /dev/null
+++ b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/files/wandb-summary.json
@@ -0,0 +1 @@
+{"train/train_samples_per_second":74.262,"train/total_flos":2.2734004322427208e+19,"train/train_runtime":258545.0504,"train/global_step":200000,"perplexity":134.40830833002153,"step":200000,"eval_samples_per_second":86.657,"epoch":19.54,"eval_step":200000,"eval_steps_per_second":0.903,"eval/runtime":1259.8218,"eval/step":200000,"eval/accuracy":0.30433709022367145,"eval/steps_per_second":0.903,"train/epoch":19.54,"train/train_steps_per_second":0.774,"eval_samples":109173,"_step":103,"_wandb":{"runtime":259827},"eval_accuracy":0.30433709022367145,"train/loss":4.9315,"eval/samples_per_second":86.657,"eval/loss":4.900882244110107,"eval_loss":4.900882244110107,"train/train_loss":2.48521353515625,"_timestamp":1.7759645965117583e+09,"eval_runtime":1259.8218,"train/learning_rate":0,"accuracy":0.30433709022367145,"_runtime":259827}
\ No newline at end of file
diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/logs/debug-core.log b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/logs/debug-core.log
new file mode 100644
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diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/logs/debug.log b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/logs/debug.log
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diff --git a/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/run-9ph1pkcx.wandb b/pretrain_tokens/wandb/run-20260409_031927-9ph1pkcx/run-9ph1pkcx.wandb
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