CLI for model Training¶
You can train a new SAFE generative models using the provided CLI, which uses 🤗 Transformers !
Usage:
safe-train [-h] [--model_path MODEL_PATH] [--config CONFIG] [--tokenizer TOKENIZER] [--num_labels NUM_LABELS]
[--include_descriptors [INCLUDE_DESCRIPTORS]] [--no_include_descriptors] [--prop_loss_coeff PROP_LOSS_COEFF]
[--wandb_project WANDB_PROJECT] [--wandb_watch {gradients,all}] [--cache_dir CACHE_DIR]
[--torch_dtype {auto,bfloat16,float16,float32}] [--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]] [--model_max_length MODEL_MAX_LENGTH]
[--dataset DATASET] [--is_tokenized [IS_TOKENIZED]] [--streaming [STREAMING]] [--text_column TEXT_COLUMN] --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 {debug,info,warning,error,critical,passive}]
[--log_level_replica {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_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]]
[--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] [--xpu_backend {mpi,ccl,gloo}]
Options:
-h, --help show this help message and exit
--model_path MODEL_PATH
Optional model path or model name to use as a starting point for the safe model (default: None)
--config CONFIG Path to the default config file to use for the safe model (default: None)
--tokenizer TOKENIZER
--num_labels NUM_LABELS
Optional number of labels for the descriptors (default: None)
--include_descriptors [INCLUDE_DESCRIPTORS]
Whether to train with descriptors if they are available or Not (default: True)
--no_include_descriptors
Whether to train with descriptors if they are available or Not (default: False)
--prop_loss_coeff PROP_LOSS_COEFF
coefficient for the propery loss (default: 0.01)
--wandb_project WANDB_PROJECT
Name of the wandb project to use to log the SAFE model parameter (default: safe-gpt2)
--wandb_watch {gradients,all}
Whether to watch the wandb models or not (default: None)
--cache_dir CACHE_DIR
Where do you want to store the pretrained models downloaded from s3 (default: None)
--torch_dtype {auto,bfloat16,float16,float32}
Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the dtype will be
automatically derived from the model's weights. (default: None)
--low_cpu_mem_usage [LOW_CPU_MEM_USAGE]
It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights
are loaded.set True will benefit LLM loading time and RAM consumption. Only valid when loading a pretrained model
(default: False)
--model_max_length MODEL_MAX_LENGTH
Maximum sequence length. Sequences will be right padded (and possibly truncated) up to that value. (default: 1024)
--dataset DATASET Path to the preprocessed dataset to use for the safe model building (default: None)
--is_tokenized [IS_TOKENIZED]
whether the dataset submitted as input is already tokenized or not (default: False)
--streaming [STREAMING]
Whether to use a streaming dataset or not (default: False)
--text_column TEXT_COLUMN
Column containing text data to process. (default: inputs)
--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 core/CPU for training. (default: 8)
--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE
Batch size per GPU/TPU 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 {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 {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] Do not use CUDA even when it is 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
(no_cuda). 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 offload` or `zero_dp_3
offload`. You can add auto-wrap to `zero_dp_2` or `zero_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 offload` or `shard_grad_op offload`. You can add auto-wrap to `full_shard` or `shard_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_hf)
--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)
--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://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.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)
--xpu_backend {mpi,ccl,gloo}
The backend to be used for distributed training on Intel XPU. (default: None)