args_docs.md

usage: finetune.py [-h] [--model_name_or_path MODEL_NAME_OR_PATH] [--load_in_8bit [LOAD_IN_8BIT]] [--load_in_4bit [LOAD_IN_4BIT]] [--bnb_4bit_quant_type BNB_4BIT_QUANT_TYPE] [--bnb_4bit_compute_dtype BNB_4BIT_COMPUTE_DTYPE] [--bnb_4bit_use_double_quant [BNB_4BIT_USE_DOUBLE_QUANT]] [--lora_r LORA_R] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] [--lora_target_modules LORA_TARGET_MODULES] --dataset_name DATASET_NAME [--dataset_config_name DATASET_CONFIG_NAME] [--model_max_length MODEL_MAX_LENGTH] [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS] [--val_set_size VAL_SET_SIZE] --output_dir OUTPUT_DIR [--overwrite_output_dir [OVERWRITE_OUTPUT_DIR]] [--do_train [DO_TRAIN]] [--do_eval [DO_EVAL]] [--do_predict [DO_PREDICT]] [--evaluation_strategy {no,steps,epoch}] [--prediction_loss_only [PREDICTION_LOSS_ONLY]] [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE] [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE] [--per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE] [--per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE] [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--eval_accumulation_steps EVAL_ACCUMULATION_STEPS] [--eval_delay EVAL_DELAY] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2] [--adam_epsilon ADAM_EPSILON] [--max_grad_norm MAX_GRAD_NORM] [--num_train_epochs NUM_TRAIN_EPOCHS] [--max_steps MAX_STEPS] [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau}] [--warmup_ratio WARMUP_RATIO] [--warmup_steps WARMUP_STEPS] [--log_level {detail,debug,info,warning,error,critical,passive}] [--log_level_replica {detail,debug,info,warning,error,critical,passive}] [--log_on_each_node [LOG_ON_EACH_NODE]] [--no_log_on_each_node] [--logging_dir LOGGING_DIR] [--logging_strategy {no,steps,epoch}] [--logging_first_step [LOGGING_FIRST_STEP]] [--logging_steps LOGGING_STEPS] [--logging_nan_inf_filter [LOGGING_NAN_INF_FILTER]] [--no_logging_nan_inf_filter] [--save_strategy {no,steps,epoch}] [--save_steps SAVE_STEPS] [--save_total_limit SAVE_TOTAL_LIMIT] [--save_safetensors [SAVE_SAFETENSORS]] [--save_on_each_node [SAVE_ON_EACH_NODE]] [--no_cuda [NO_CUDA]] [--use_cpu [USE_CPU]] [--use_mps_device [USE_MPS_DEVICE]] [--seed SEED] [--data_seed DATA_SEED] [--jit_mode_eval [JIT_MODE_EVAL]] [--use_ipex [USE_IPEX]] [--bf16 [BF16]] [--fp16 [FP16]] [--fp16_opt_level FP16_OPT_LEVEL] [--half_precision_backend {auto,cuda_amp,apex,cpu_amp}] [--bf16_full_eval [BF16_FULL_EVAL]] [--fp16_full_eval [FP16_FULL_EVAL]] [--tf32 TF32] [--local_rank LOCAL_RANK] [--ddp_backend {nccl,gloo,mpi,ccl}] [--tpu_num_cores TPU_NUM_CORES] [--tpu_metrics_debug [TPU_METRICS_DEBUG]] [--debug DEBUG [DEBUG ...]] [--dataloader_drop_last [DATALOADER_DROP_LAST]] [--eval_steps EVAL_STEPS] [--dataloader_num_workers DATALOADER_NUM_WORKERS] [--past_index PAST_INDEX] [--run_name RUN_NAME] [--disable_tqdm DISABLE_TQDM] [--remove_unused_columns [REMOVE_UNUSED_COLUMNS]] [--no_remove_unused_columns] [--label_names LABEL_NAMES [LABEL_NAMES ...]] [--load_best_model_at_end [LOAD_BEST_MODEL_AT_END]] [--metric_for_best_model METRIC_FOR_BEST_MODEL] [--greater_is_better GREATER_IS_BETTER] [--ignore_data_skip [IGNORE_DATA_SKIP]] [--sharded_ddp SHARDED_DDP] [--fsdp FSDP] [--fsdp_min_num_params FSDP_MIN_NUM_PARAMS] [--fsdp_config FSDP_CONFIG] [--fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP] [--deepspeed DEEPSPEED] [--label_smoothing_factor LABEL_SMOOTHING_FACTOR] [--optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit}] [--optim_args OPTIM_ARGS] [--adafactor [ADAFACTOR]] [--group_by_length [GROUP_BY_LENGTH]] [--length_column_name LENGTH_COLUMN_NAME] [--report_to REPORT_TO [REPORT_TO ...]] [--ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS] [--ddp_bucket_cap_mb DDP_BUCKET_CAP_MB] [--ddp_broadcast_buffers DDP_BROADCAST_BUFFERS] [--dataloader_pin_memory [DATALOADER_PIN_MEMORY]] [--no_dataloader_pin_memory] [--skip_memory_metrics [SKIP_MEMORY_METRICS]] [--no_skip_memory_metrics] [--use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP]] [--push_to_hub [PUSH_TO_HUB]] [--resume_from_checkpoint RESUME_FROM_CHECKPOINT] [--hub_model_id HUB_MODEL_ID] [--hub_strategy {end,every_save,checkpoint,all_checkpoints}] [--hub_token HUB_TOKEN] [--hub_private_repo [HUB_PRIVATE_REPO]] [--hub_always_push [HUB_ALWAYS_PUSH]] [--gradient_checkpointing [GRADIENT_CHECKPOINTING]] [--include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS]] [--fp16_backend {auto,cuda_amp,apex,cpu_amp}] [--push_to_hub_model_id PUSH_TO_HUB_MODEL_ID] [--push_to_hub_organization PUSH_TO_HUB_ORGANIZATION] [--push_to_hub_token PUSH_TO_HUB_TOKEN] [--mp_parameters MP_PARAMETERS] [--auto_find_batch_size [AUTO_FIND_BATCH_SIZE]] [--full_determinism [FULL_DETERMINISM]] [--torchdynamo TORCHDYNAMO] [--ray_scope RAY_SCOPE] [--ddp_timeout DDP_TIMEOUT] [--torch_compile [TORCH_COMPILE]] [--torch_compile_backend TORCH_COMPILE_BACKEND] [--torch_compile_mode TORCH_COMPILE_MODE] [--dispatch_batches DISPATCH_BATCHES]

options: -h, --help show this help message and exit --model_name_or_path MODEL_NAME_OR_PATH --load_in_8bit [LOAD_IN_8BIT] Whether to convert the loaded model into mixed-8bit quantized model. (default: False) --load_in_4bit [LOAD_IN_4BIT] Whether to convert the loaded model into mixed-4bit quantized model. (default: False) --bnb_4bit_quant_type BNB_4BIT_QUANT_TYPE bnb_4bit_quant_type (str, {fp4, nf4}, defaults to fp4): This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types which are specified by fp4 or nf4. (default: fp4) --bnb_4bit_compute_dtype BNB_4BIT_COMPUTE_DTYPE The compute dtype of the model. Can be float32, fp32, float16, fp16 bfloat16, bf16. (default: float32) --bnb_4bit_use_double_quant [BNB_4BIT_USE_DOUBLE_QUANT] Whether to use double quantization for mixed-4bit quantized model. (default: False) --lora_r LORA_R Lora rank. (default: 8) --lora_alpha LORA_ALPHA Lora alpha. (default: 16) --lora_dropout LORA_DROPOUT Lora dropout. (default: 0.05) --lora_target_modules LORA_TARGET_MODULES Names of the modules to apply Lora to. (default: q_proj,v_proj) --dataset_name DATASET_NAME The name of the dataset to use (via the datasets library). (default: None) --dataset_config_name DATASET_CONFIG_NAME The configuration name of the dataset to use (via the datasets library). (default: None) --model_max_length MODEL_MAX_LENGTH Maximum sequence length. Sequences will be right padded (and possibly truncated). (default: 1024) --preprocessing_num_workers PREPROCESSING_NUM_WORKERS The number of processes to use for the preprocessing. (default: None) --val_set_size VAL_SET_SIZE The validation set size. For loss checking. (default: 2000) --output_dir OUTPUT_DIR The output directory where the model predictions and checkpoints will be written. (default: None) --overwrite_output_dir [OVERWRITE_OUTPUT_DIR] Overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory. (default: False) --do_train [DO_TRAIN] Whether to run training. (default: False) --do_eval [DO_EVAL] Whether to run eval on the dev set. (default: False) --do_predict [DO_PREDICT] Whether to run predictions on the test set. (default: False) --evaluation_strategy {no,steps,epoch} The evaluation strategy to use. (default: no) --prediction_loss_only [PREDICTION_LOSS_ONLY] When performing evaluation and predictions, only returns the loss. (default: False) --per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for training. (default: 8) --per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation. (default: 8) --per_gpu_train_batch_size PER_GPU_TRAIN_BATCH_SIZE Deprecated, the use of --per_device_train_batch_size is preferred. Batch size per GPU/TPU core/CPU for training. (default: None) --per_gpu_eval_batch_size PER_GPU_EVAL_BATCH_SIZE Deprecated, the use of --per_device_eval_batch_size is preferred. Batch size per GPU/TPU core/CPU for evaluation. (default: None) --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS Number of updates steps to accumulate before performing a backward/update pass. (default: 1) --eval_accumulation_steps EVAL_ACCUMULATION_STEPS Number of predictions steps to accumulate before moving the tensors to the CPU. (default: None) --eval_delay EVAL_DELAY Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy. (default: 0) --learning_rate LEARNING_RATE The initial learning rate for AdamW. (default: 5e-05) --weight_decay WEIGHT_DECAY Weight decay for AdamW if we apply some. (default: 0.0) --adam_beta1 ADAM_BETA1 Beta1 for AdamW optimizer (default: 0.9) --adam_beta2 ADAM_BETA2 Beta2 for AdamW optimizer (default: 0.999) --adam_epsilon ADAM_EPSILON Epsilon for AdamW optimizer. (default: 1e-08) --max_grad_norm MAX_GRAD_NORM Max gradient norm. (default: 1.0) --num_train_epochs NUM_TRAIN_EPOCHS Total number of training epochs to perform. (default: 3.0) --max_steps MAX_STEPS If > 0: set total number of training steps to perform. Override num_train_epochs. (default: -1) --lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau} The scheduler type to use. (default: linear) --warmup_ratio WARMUP_RATIO Linear warmup over warmup_ratio fraction of total steps. (default: 0.0) --warmup_steps WARMUP_STEPS Linear warmup over warmup_steps. (default: 0) --log_level {detail,debug,info,warning,error,critical,passive} Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. Defaults to 'passive'. (default: passive) --log_level_replica {detail,debug,info,warning,error,critical,passive} Logger log level to use on replica nodes. Same choices and defaults as log_level (default: warning) --log_on_each_node [LOG_ON_EACH_NODE] When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: True) --no_log_on_each_node When doing a multinode distributed training, whether to log once per node or just once on the main node. (default: False) --logging_dir LOGGING_DIR Tensorboard log dir. (default: None) --logging_strategy {no,steps,epoch} The logging strategy to use. (default: steps) --logging_first_step [LOGGING_FIRST_STEP] Log the first global_step (default: False) --logging_steps LOGGING_STEPS Log every X updates steps. Should be an integer or a float in range [0,1).If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --logging_nan_inf_filter [LOGGING_NAN_INF_FILTER] Filter nan and inf losses for logging. (default: True) --no_logging_nan_inf_filter Filter nan and inf losses for logging. (default: False) --save_strategy {no,steps,epoch} The checkpoint save strategy to use. (default: steps) --save_steps SAVE_STEPS Save checkpoint every X updates steps. Should be an integer or a float in range [0,1).If smaller than 1, will be interpreted as ratio of total training steps. (default: 500) --save_total_limit SAVE_TOTAL_LIMIT If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. When load_best_model_at_end is enabled, the 'best' checkpoint according to metric_for_best_model will always be retained in addition to the most recent ones. For example, for save_total_limit=5 and load_best_model_at_end=True, the four last checkpoints will always be retained alongside the best model. When save_total_limit=1 and load_best_model_at_end=True, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Default is unlimited checkpoints (default: None) --save_safetensors [SAVE_SAFETENSORS] Use safetensors saving and loading for state dicts instead of default torch.load and torch.save. (default: False) --save_on_each_node [SAVE_ON_EACH_NODE] When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one (default: False) --no_cuda [NO_CUDA] This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers. (default: False) --use_cpu [USE_CPU] Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available. (default: False) --use_mps_device [USE_MPS_DEVICE] This argument is deprecated. mps device will be used if available similar to cuda device. It will be removed in version 5.0 of 🤗 Transformers (default: False) --seed SEED Random seed that will be set at the beginning of training. (default: 42) --data_seed DATA_SEED Random seed to be used with data samplers. (default: None) --jit_mode_eval [JIT_MODE_EVAL] Whether or not to use PyTorch jit trace for inference (default: False) --use_ipex [USE_IPEX] Use Intel extension for PyTorch when it is available, installation: 'https://github.com/intel/intel- extension-for-pytorch' (default: False) --bf16 [BF16] Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu). This is an experimental API and it may change. (default: False) --fp16 [FP16] Whether to use fp16 (mixed) precision instead of 32-bit (default: False) --fp16_opt_level FP16_OPT_LEVEL For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details at https://nvidia.github.io/apex/amp.html (default: O1) --half_precision_backend {auto,cuda_amp,apex,cpu_amp} The backend to be used for half precision. (default: auto) --bf16_full_eval [BF16_FULL_EVAL] Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may change. (default: False) --fp16_full_eval [FP16_FULL_EVAL] Whether to use full float16 evaluation instead of 32-bit (default: False) --tf32 TF32 Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change. (default: None) --local_rank LOCAL_RANK For distributed training: local_rank (default: -1) --ddp_backend {nccl,gloo,mpi,ccl} The backend to be used for distributed training (default: None) --tpu_num_cores TPU_NUM_CORES TPU: Number of TPU cores (automatically passed by launcher script) (default: None) --tpu_metrics_debug [TPU_METRICS_DEBUG] Deprecated, the use of --debug tpu_metrics_debug is preferred. TPU: Whether to print debug metrics (default: False) --debug DEBUG [DEBUG ...] Whether or not to enable debug mode. Current options: underflow_overflow (Detect underflow and overflow in activations and weights), tpu_metrics_debug (print debug metrics on TPU). (default: None) --dataloader_drop_last [DATALOADER_DROP_LAST] Drop the last incomplete batch if it is not divisible by the batch size. (default: False) --eval_steps EVAL_STEPS Run an evaluation every X steps. Should be an integer or a float in range [0,1).If smaller than 1, will be interpreted as ratio of total training steps. (default: None) --dataloader_num_workers DATALOADER_NUM_WORKERS Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. (default: 0) --past_index PAST_INDEX If >=0, uses the corresponding part of the output as the past state for next step. (default: -1) --run_name RUN_NAME An optional descriptor for the run. Notably used for wandb logging. (default: None) --disable_tqdm DISABLE_TQDM Whether or not to disable the tqdm progress bars. (default: None) --remove_unused_columns [REMOVE_UNUSED_COLUMNS] Remove columns not required by the model when using an nlp.Dataset. (default: True) --no_remove_unused_columns Remove columns not required by the model when using an nlp.Dataset. (default: False) --label_names LABEL_NAMES [LABEL_NAMES ...] The list of keys in your dictionary of inputs that correspond to the labels. (default: None) --load_best_model_at_end [LOAD_BEST_MODEL_AT_END] Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See save_total_limit for more. (default: False) --metric_for_best_model METRIC_FOR_BEST_MODEL The metric to use to compare two different models. (default: None) --greater_is_better GREATER_IS_BETTER Whether the metric_for_best_model should be maximized or not. (default: None) --ignore_data_skip [IGNORE_DATA_SKIP] When resuming training, whether or not to skip the first epochs and batches to get to the same training data. (default: False) --sharded_ddp SHARDED_DDP Whether or not to use sharded DDP training (in distributed training only). The base option should be simple, zero_dp_2 or zero_dp_3 and you can add CPU-offload to zero_dp_2 or zero_dp_3 like this: zero_dp_2 offloadorzero_dp_3 offload. You can add auto-wrap to zero_dp_2orzero_dp_3 with the same syntax: zero_dp_2 auto_wrap or zero_dp_3 auto_wrap. (default: ) --fsdp FSDP Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training only). The base option should be full_shard, shard_grad_op or no_shard and you can add CPU- offload to full_shard or shard_grad_op like this: full_shard offloadorshard_grad_op offload. You can add auto-wrap to full_shardorshard_grad_op with the same syntax: full_shard auto_wrap or shard_grad_op auto_wrap. (default: ) --fsdp_min_num_params FSDP_MIN_NUM_PARAMS This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when fsdp field is passed). (default: 0) --fsdp_config FSDP_CONFIG Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either afsdp json config file (e.g., fsdp_config.json) or an already loaded json file as dict. (default: None) --fsdp_transformer_layer_cls_to_wrap FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g, BertLayer, GPTJBlock, T5Block .... (useful only when fsdp flag is passed). (default: None) --deepspeed DEEPSPEED Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already loaded json file as a dict (default: None) --label_smoothing_factor LABEL_SMOOTHING_FACTOR The label smoothing epsilon to apply (zero means no label smoothing). (default: 0.0) --optim {adamw_hf,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_apex_fused,adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion_8bit,lion_32bit,paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit} The optimizer to use. (default: adamw_torch) --optim_args OPTIM_ARGS Optional arguments to supply to optimizer. (default: None) --adafactor [ADAFACTOR] Whether or not to replace AdamW by Adafactor. (default: False) --group_by_length [GROUP_BY_LENGTH] Whether or not to group samples of roughly the same length together when batching. (default: False) --length_column_name LENGTH_COLUMN_NAME Column name with precomputed lengths to use when grouping by length. (default: length) --report_to REPORT_TO [REPORT_TO ...] The list of integrations to report the results and logs to. (default: None) --ddp_find_unused_parameters DDP_FIND_UNUSED_PARAMETERS When using distributed training, the value of the flag find_unused_parameters passed to DistributedDataParallel. (default: None) --ddp_bucket_cap_mb DDP_BUCKET_CAP_MB When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel. (default: None) --ddp_broadcast_buffers DDP_BROADCAST_BUFFERS When using distributed training, the value of the flag broadcast_buffers passed to DistributedDataParallel. (default: None) --dataloader_pin_memory [DATALOADER_PIN_MEMORY] Whether or not to pin memory for DataLoader. (default: True) --no_dataloader_pin_memory Whether or not to pin memory for DataLoader. (default: False) --skip_memory_metrics [SKIP_MEMORY_METRICS] Whether or not to skip adding of memory profiler reports to metrics. (default: True) --no_skip_memory_metrics Whether or not to skip adding of memory profiler reports to metrics. (default: False) --use_legacy_prediction_loop [USE_LEGACY_PREDICTION_LOOP] Whether or not to use the legacy prediction_loop in the Trainer. (default: False) --push_to_hub [PUSH_TO_HUB] Whether or not to upload the trained model to the model hub after training. (default: False) --resume_from_checkpoint RESUME_FROM_CHECKPOINT The path to a folder with a valid checkpoint for your model. (default: None) --hub_model_id HUB_MODEL_ID The name of the repository to keep in sync with the local output_dir. (default: None) --hub_strategy {end,every_save,checkpoint,all_checkpoints} The hub strategy to use when --push_to_hub is activated. (default: every_save) --hub_token HUB_TOKEN The token to use to push to the Model Hub. (default: None) --hub_private_repo [HUB_PRIVATE_REPO] Whether the model repository is private or not. (default: False) --hub_always_push [HUB_ALWAYS_PUSH] Unless True, the Trainer will skip pushes if the previous one wasn't finished yet. (default: False) --gradient_checkpointing [GRADIENT_CHECKPOINTING] If True, use gradient checkpointing to save memory at the expense of slower backward pass. (default: False) --include_inputs_for_metrics [INCLUDE_INPUTS_FOR_METRICS] Whether or not the inputs will be passed to the compute_metrics function. (default: False) --fp16_backend {auto,cuda_amp,apex,cpu_amp} Deprecated. Use half_precision_backend instead (default: auto) --push_to_hub_model_id PUSH_TO_HUB_MODEL_ID The name of the repository to which push the Trainer. (default: None) --push_to_hub_organization PUSH_TO_HUB_ORGANIZATION The name of the organization in with to which push the Trainer. (default: None) --push_to_hub_token PUSH_TO_HUB_TOKEN The token to use to push to the Model Hub. (default: None) --mp_parameters MP_PARAMETERS Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer (default: ) --auto_find_batch_size [AUTO_FIND_BATCH_SIZE] Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached (default: False) --full_determinism [FULL_DETERMINISM] Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed training. Important: this will negatively impact the performance, so only use it for debugging. (default: False) --torchdynamo TORCHDYNAMO This argument is deprecated, use --torch_compile_backend instead. (default: None) --ray_scope RAY_SCOPE The scope to use when doing hyperparameter search with Ray. By default, "last" will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation (https://doc s.ray.io/en/latest/tune/api_docs/analysis.html#ray.tun e.ExperimentAnalysis.get_best_trial) for more options. (default: last) --ddp_timeout DDP_TIMEOUT Overrides the default timeout for distributed training (value should be given in seconds). (default: 1800) --torch_compile [TORCH_COMPILE] If set to True, the model will be wrapped in torch.compile. (default: False) --torch_compile_backend TORCH_COMPILE_BACKEND Which backend to use with torch.compile, passing one will trigger a model compilation. (default: None) --torch_compile_mode TORCH_COMPILE_MODE Which mode to use with torch.compile, passing one will trigger a model compilation. (default: None) --dispatch_batches DISPATCH_BATCHES Whether to dispatch batches across devices in distributed training. If set to True, the dataloader prepared by the Accelerator is only iterated through on the main processand then the batches are split and broadcast to each process. Will default to True for DataLoader whoseunderlying dataset is an IterableDataset, False otherwise. (default: None)