2024-06-04 01:01:39.001875: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-04 01:01:39.054364: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-06-04 01:01:39.054415: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-06-04 01:01:39.055904: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-06-04 01:01:39.063872: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-04 01:01:41.050561: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
06/04/2024 01:01:46 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 6, distributed training: False, 16-bits training: False
06/04/2024 01:01:46 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=6,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
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_do_concat_batches=True,
eval_steps=1000,
eval_strategy=steps,
evaluation_strategy=None,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': 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=DorinSht/recreate_llama_68M_vanilla,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.0001,
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=./training_outputs_job_116987_1_04-06_01-01,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
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,
optim_target_modules=None,
output_dir=./training_outputs_job_116987_1_04-06_01-01,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=4,
prediction_loss_only=False,
push_to_hub=True,
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=['tensorboard'],
restore_callback_states_from_checkpoint=False,
resume_from_checkpoint=None,
run_name=/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=None,
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=0.05,
warmup_steps=0,
weight_decay=0.01,
)
06/04/2024 01:01:47 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
Using custom data configuration default-afe4b27d28cbdcb1
Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:47 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
Overwrite dataset info from restored data version if exists.
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
Using custom data configuration default-afe4b27d28cbdcb1
Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:47 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
Overwrite dataset info from restored data version if exists.
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:47 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:48 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
Using custom data configuration default-afe4b27d28cbdcb1
Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:48 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
06/04/2024 01:01:48 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
Overwrite dataset info from restored data version if exists.
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:48 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:48 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
06/04/2024 01:01:48 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
[INFO|configuration_utils.py:726] 2024-06-04 01:01:48,499 >> loading configuration file config.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/config.json
[INFO|configuration_utils.py:789] 2024-06-04 01:01:48,501 >> Model config LlamaConfig {
  "_name_or_path": "JackFram/llama-68m",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 0,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "max_position_embeddings": 2048,
  "model_type": "llama",
  "num_attention_heads": 12,
  "num_hidden_layers": 2,
  "num_key_value_heads": 12,
  "pad_token_id": 1,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.41.0.dev0",
  "use_cache": true,
  "vocab_size": 32000
}

[INFO|tokenization_utils_base.py:2102] 2024-06-04 01:01:48,634 >> loading file tokenizer.model from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/tokenizer.model
[INFO|tokenization_utils_base.py:2102] 2024-06-04 01:01:48,635 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2102] 2024-06-04 01:01:48,635 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2102] 2024-06-04 01:01:48,635 >> loading file special_tokens_map.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/special_tokens_map.json
[INFO|tokenization_utils_base.py:2102] 2024-06-04 01:01:48,635 >> loading file tokenizer_config.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/tokenizer_config.json
[WARNING|logging.py:329] 2024-06-04 01:01:48,636 >> You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
[WARNING|logging.py:329] 2024-06-04 01:01:48,716 >> You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
[INFO|configuration_utils.py:936] 2024-06-04 01:01:49,214 >> Generate config GenerationConfig {
  "bos_token_id": 0,
  "eos_token_id": 2,
  "pad_token_id": 1
}

06/04/2024 01:01:50 - INFO - __main__ - Training new model from scratch - Total size=64.88M params
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-988d048fea8d2473.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-988d048fea8d2473.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e281c930893bca9.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e281c930893bca9.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-3fe350bccdda6078.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-3fe350bccdda6078.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-35d09b588a0c62b9.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-35d09b588a0c62b9.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e5279ee31a5d8d3.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e5279ee31a5d8d3.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-63d56456928edd43.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-63d56456928edd43.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-6a784a78d9818240.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-6a784a78d9818240.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-46540f58a00a92bf.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-46540f58a00a92bf.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-73605724efaea9d2.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-73605724efaea9d2.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-83d3df87e1b82021.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-83d3df87e1b82021.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-efdbb02491aa6344.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-efdbb02491aa6344.arrow
06/04/2024 01:01:50 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-0cf2ae38fef927f3.arrow
Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-0cf2ae38fef927f3.arrow
06/04/2024 01:01:50 - WARNING - accelerate.utils.other - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
[INFO|trainer.py:2068] 2024-06-04 01:01:51,038 >> ***** Running training *****
[INFO|trainer.py:2069] 2024-06-04 01:01:51,038 >>   Num examples = 90,745
[INFO|trainer.py:2070] 2024-06-04 01:01:51,038 >>   Num Epochs = 3
[INFO|trainer.py:2071] 2024-06-04 01:01:51,038 >>   Instantaneous batch size per device = 4
[INFO|trainer.py:2073] 2024-06-04 01:01:51,038 >>   Training with DataParallel so batch size has been adjusted to: 24
[INFO|trainer.py:2074] 2024-06-04 01:01:51,038 >>   Total train batch size (w. parallel, distributed & accumulation) = 24
[INFO|trainer.py:2075] 2024-06-04 01:01:51,038 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:2076] 2024-06-04 01:01:51,038 >>   Total optimization steps = 11,346
[INFO|trainer.py:2077] 2024-06-04 01:01:51,038 >>   Number of trainable parameters = 68,030,208

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  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 01:29:49,519 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 01:29:49,519 >>   Batch size = 48
{'loss': 5.1118, 'grad_norm': 0.8546377420425415, 'learning_rate': 8.816009873931059e-05, 'epoch': 0.13}
{'loss': 3.406, 'grad_norm': 0.8593683838844299, 'learning_rate': 9.59831475011252e-05, 'epoch': 0.26}


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                                               [INFO|trainer.py:3353] 2024-06-04 01:31:58,493 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000
[INFO|configuration_utils.py:471] 2024-06-04 01:31:58,504 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 01:31:58,509 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 01:31:59,417 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 01:31:59,430 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 01:31:59,434 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-1000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 01:32:01,652 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 01:32:01,656 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 18%|█▊        | 2000/11346 [58:05<4:22:00,  1.68s/it][INFO|trainer.py:3662] 2024-06-04 01:59:56,120 >> ***** Running Evaluation *****
[INFO|trainer.py:3664] 2024-06-04 01:59:56,120 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 01:59:56,120 >>   Batch size = 48
{'eval_loss': 3.23445987701416, 'eval_accuracy': 0.5035308829464115, 'eval_runtime': 128.969, 'eval_samples_per_second': 14.267, 'eval_steps_per_second': 0.302, 'epoch': 0.26}
{'loss': 3.0005, 'grad_norm': 0.9617242217063904, 'learning_rate': 9.134314230431938e-05, 'epoch': 0.4}
{'loss': 2.8119, 'grad_norm': 0.8956136107444763, 'learning_rate': 8.670313710751356e-05, 'epoch': 0.53}


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                                               [INFO|trainer.py:3353] 2024-06-04 02:02:04,978 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000
[INFO|configuration_utils.py:471] 2024-06-04 02:02:04,989 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 02:02:04,994 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 02:02:05,895 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 02:02:05,907 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 02:02:05,911 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-2000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 02:02:09,053 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 02:02:09,058 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 02:30:14,465 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 02:30:14,465 >>   Batch size = 48
{'eval_loss': 2.821624517440796, 'eval_accuracy': 0.5364783564495231, 'eval_runtime': 128.8547, 'eval_samples_per_second': 14.28, 'eval_steps_per_second': 0.303, 'epoch': 0.53}
{'loss': 2.6861, 'grad_norm': 1.2592207193374634, 'learning_rate': 8.206313191070773e-05, 'epoch': 0.66}
{'loss': 2.6076, 'grad_norm': 1.3978203535079956, 'learning_rate': 7.742312671390191e-05, 'epoch': 0.79}


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                                               [INFO|trainer.py:3353] 2024-06-04 02:32:23,584 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000
[INFO|configuration_utils.py:471] 2024-06-04 02:32:23,595 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 02:32:23,601 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 02:32:24,552 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 02:32:24,565 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 02:32:24,569 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-3000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 02:32:26,900 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 02:32:26,904 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 35%|███▌      | 4000/11346 [1:58:41<3:26:15,  1.68s/it][INFO|trainer.py:3662] 2024-06-04 03:00:33,044 >> ***** Running Evaluation *****
[INFO|trainer.py:3664] 2024-06-04 03:00:33,044 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 03:00:33,044 >>   Batch size = 48
{'eval_loss': 2.6552908420562744, 'eval_accuracy': 0.5501396529385527, 'eval_runtime': 129.1173, 'eval_samples_per_second': 14.251, 'eval_steps_per_second': 0.302, 'epoch': 0.79}
{'loss': 2.5643, 'grad_norm': 1.8927521705627441, 'learning_rate': 7.278312151709609e-05, 'epoch': 0.93}
{'loss': 2.4729, 'grad_norm': 4.235791206359863, 'learning_rate': 6.814311632029027e-05, 'epoch': 1.06}


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                                               [INFO|trainer.py:3353] 2024-06-04 03:02:42,365 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000
[INFO|configuration_utils.py:471] 2024-06-04 03:02:42,377 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 03:02:42,381 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 03:02:43,317 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 03:02:43,329 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 03:02:43,334 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-4000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 03:02:45,776 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 03:02:45,780 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 03:30:52,374 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 03:30:52,374 >>   Batch size = 48
{'eval_loss': 2.5761468410491943, 'eval_accuracy': 0.5581473949151462, 'eval_runtime': 129.3204, 'eval_samples_per_second': 14.228, 'eval_steps_per_second': 0.302, 'epoch': 1.06}
{'loss': 2.4531, 'grad_norm': 2.753159523010254, 'learning_rate': 6.350311112348446e-05, 'epoch': 1.19}
{'loss': 2.4323, 'grad_norm': 3.542167901992798, 'learning_rate': 5.886310592667864e-05, 'epoch': 1.32}


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                                               [INFO|trainer.py:3353] 2024-06-04 03:33:01,857 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000
[INFO|configuration_utils.py:471] 2024-06-04 03:33:01,868 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 03:33:01,873 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 03:33:02,766 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 03:33:02,777 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 03:33:02,780 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-5000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 03:33:05,704 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 03:33:05,708 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 04:01:12,560 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 04:01:12,560 >>   Batch size = 48
{'eval_loss': 2.536275863647461, 'eval_accuracy': 0.5616867738578195, 'eval_runtime': 129.4842, 'eval_samples_per_second': 14.21, 'eval_steps_per_second': 0.301, 'epoch': 1.32}
{'loss': 2.3981, 'grad_norm': 3.678074598312378, 'learning_rate': 5.422310072987282e-05, 'epoch': 1.45}
{'loss': 2.3824, 'grad_norm': 5.024658679962158, 'learning_rate': 4.9583095533066995e-05, 'epoch': 1.59}


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                                               [INFO|trainer.py:3353] 2024-06-04 04:03:21,703 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000
[INFO|configuration_utils.py:471] 2024-06-04 04:03:21,714 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 04:03:21,719 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 04:03:22,603 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 04:03:22,617 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 04:03:22,622 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-6000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 04:03:24,934 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 04:03:24,938 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 04:31:30,779 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 04:31:30,779 >>   Batch size = 48
{'eval_loss': 2.491274118423462, 'eval_accuracy': 0.5660391134427901, 'eval_runtime': 129.1418, 'eval_samples_per_second': 14.248, 'eval_steps_per_second': 0.302, 'epoch': 1.59}
{'loss': 2.3763, 'grad_norm': 5.106564521789551, 'learning_rate': 4.4943090336261176e-05, 'epoch': 1.72}
{'loss': 2.3719, 'grad_norm': 11.649778366088867, 'learning_rate': 4.030308513945535e-05, 'epoch': 1.85}


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                                               [INFO|trainer.py:3353] 2024-06-04 04:33:39,612 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000
[INFO|configuration_utils.py:471] 2024-06-04 04:33:39,623 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 04:33:39,628 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 04:33:40,501 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 04:33:40,514 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 04:33:40,518 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-7000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 04:33:42,694 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 04:33:42,698 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 05:01:45,903 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 05:01:45,903 >>   Batch size = 48
{'eval_loss': 2.466362237930298, 'eval_accuracy': 0.5685828678235382, 'eval_runtime': 128.8316, 'eval_samples_per_second': 14.282, 'eval_steps_per_second': 0.303, 'epoch': 1.85}
{'loss': 2.3411, 'grad_norm': 5.842829704284668, 'learning_rate': 3.566307994264953e-05, 'epoch': 1.98}
{'loss': 2.3021, 'grad_norm': 14.795243263244629, 'learning_rate': 3.1023074745843715e-05, 'epoch': 2.12}


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                                               [INFO|trainer.py:3353] 2024-06-04 05:03:54,284 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000
[INFO|configuration_utils.py:471] 2024-06-04 05:03:54,295 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 05:03:54,300 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 05:03:55,216 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 05:03:55,229 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 05:03:55,233 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-8000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 05:03:57,585 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 05:03:57,589 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 05:32:01,386 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 05:32:01,386 >>   Batch size = 48
{'eval_loss': 2.4403789043426514, 'eval_accuracy': 0.5715702725090801, 'eval_runtime': 128.3806, 'eval_samples_per_second': 14.332, 'eval_steps_per_second': 0.304, 'epoch': 2.12}
{'loss': 2.2897, 'grad_norm': 6.084632873535156, 'learning_rate': 2.6383069549037897e-05, 'epoch': 2.25}
{'loss': 2.2848, 'grad_norm': 17.67453956604004, 'learning_rate': 2.1743064352232075e-05, 'epoch': 2.38}


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                                               [INFO|trainer.py:3353] 2024-06-04 05:34:09,937 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000
[INFO|configuration_utils.py:471] 2024-06-04 05:34:09,952 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 05:34:09,957 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 05:34:10,819 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 05:34:10,832 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 05:34:10,837 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-9000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 05:34:13,079 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 05:34:13,083 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 06:02:17,519 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 06:02:17,520 >>   Batch size = 48
{'eval_loss': 2.407994508743286, 'eval_accuracy': 0.5754845372868036, 'eval_runtime': 128.557, 'eval_samples_per_second': 14.313, 'eval_steps_per_second': 0.303, 'epoch': 2.38}
{'loss': 2.2611, 'grad_norm': 6.01920223236084, 'learning_rate': 1.7103059155426253e-05, 'epoch': 2.51}
{'loss': 2.2653, 'grad_norm': 7.932415008544922, 'learning_rate': 1.2463053958620433e-05, 'epoch': 2.64}


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                                               [INFO|trainer.py:3353] 2024-06-04 06:04:26,284 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000
[INFO|configuration_utils.py:471] 2024-06-04 06:04:26,296 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 06:04:26,301 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 06:04:27,227 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:04:27,239 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:04:27,244 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-10000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:04:29,530 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:04:29,535 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3664] 2024-06-04 06:32:33,751 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 06:32:33,751 >>   Batch size = 48
{'eval_loss': 2.3834304809570312, 'eval_accuracy': 0.5784841549669718, 'eval_runtime': 128.7742, 'eval_samples_per_second': 14.289, 'eval_steps_per_second': 0.303, 'epoch': 2.64}
{'loss': 2.2542, 'grad_norm': 9.934358596801758, 'learning_rate': 7.823048761814613e-06, 'epoch': 2.78}
{'loss': 2.2447, 'grad_norm': 8.533864974975586, 'learning_rate': 3.183043565008793e-06, 'epoch': 2.91}


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                                               [INFO|trainer.py:3353] 2024-06-04 06:34:42,794 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000
[INFO|configuration_utils.py:471] 2024-06-04 06:34:42,807 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000/config.json
[INFO|configuration_utils.py:705] 2024-06-04 06:34:42,811 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 06:34:43,707 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:34:43,720 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:34:43,724 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/checkpoint-11000/special_tokens_map.json
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:34:46,017 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:34:46,021 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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Training completed. Do not forget to share your model on huggingface.co/models =)



                                                       

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[INFO|trainer.py:3353] 2024-06-04 06:44:27,410 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01
[INFO|configuration_utils.py:471] 2024-06-04 06:44:27,424 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/config.json
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[INFO|trainer.py:3353] 2024-06-04 06:44:28,428 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01
[INFO|configuration_utils.py:471] 2024-06-04 06:44:28,432 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/config.json
[INFO|configuration_utils.py:705] 2024-06-04 06:44:28,436 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 06:44:29,368 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:44:29,380 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:44:29,384 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
[INFO|modelcard.py:450] 2024-06-04 06:44:29,735 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.5811017183152439}]}
{'eval_loss': 2.3603451251983643, 'eval_accuracy': 0.5811017183152439, 'eval_runtime': 129.0604, 'eval_samples_per_second': 14.257, 'eval_steps_per_second': 0.302, 'epoch': 2.91}
{'train_runtime': 20556.3593, 'train_samples_per_second': 13.243, 'train_steps_per_second': 0.552, 'train_loss': 2.5941595100713495, 'epoch': 3.0}

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***** train metrics *****
  epoch                    =         3.0
  total_flos               = 135380817GF
  train_loss               =      2.5942
  train_runtime            =  5:42:36.35
  train_samples            =       90745
  train_samples_per_second =      13.243
  train_steps_per_second   =       0.552
06/04/2024 06:44:45 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:3662] 2024-06-04 06:44:45,746 >> ***** Running Evaluation *****
[INFO|trainer.py:3664] 2024-06-04 06:44:45,746 >>   Num examples = 1840
[INFO|trainer.py:3667] 2024-06-04 06:44:45,746 >>   Batch size = 48
/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:3353] 2024-06-04 06:46:54,461 >> Saving model checkpoint to ./training_outputs_job_116987_1_04-06_01-01
[INFO|configuration_utils.py:471] 2024-06-04 06:46:54,473 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/config.json
[INFO|configuration_utils.py:705] 2024-06-04 06:46:54,478 >> Configuration saved in ./training_outputs_job_116987_1_04-06_01-01/generation_config.json
[INFO|modeling_utils.py:2592] 2024-06-04 06:46:55,425 >> Model weights saved in ./training_outputs_job_116987_1_04-06_01-01/model.safetensors
[INFO|tokenization_utils_base.py:2503] 2024-06-04 06:46:55,436 >> tokenizer config file saved in ./training_outputs_job_116987_1_04-06_01-01/tokenizer_config.json
[INFO|tokenization_utils_base.py:2512] 2024-06-04 06:46:55,440 >> Special tokens file saved in ./training_outputs_job_116987_1_04-06_01-01/special_tokens_map.json
[INFO|modelcard.py:450] 2024-06-04 06:46:55,614 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.5819606104373314}]}
***** eval metrics *****
  epoch                   =        3.0
  eval_accuracy           =      0.582
  eval_loss               =     2.3558
  eval_runtime            = 0:02:08.69
  eval_samples            =       1840
  eval_samples_per_second =     14.297
  eval_steps_per_second   =      0.303
  perplexity              =    10.5468

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