Parameters

• model (PreTrainedModel or torch.nn.Module, optional) — The model to train, evaluate or use for predictions. If not provided, a model_init must be passed.

  Trainer is optimized to work with the PreTrainedModel provided by the library. You can still use your own models defined as torch.nn.Module as long as they work the same way as the 🤗 Transformers models.

• args (TrainingArguments, optional) — The arguments to tweak for training. Will default to a basic instance of TrainingArguments with the output_dir set to a directory named tmp_trainer in the current directory if not provided.
• data_collator (DataCollator, optional) — The function to use to form a batch from a list of elements of train_dataset or eval_dataset. Will default to default_data_collator() if no tokenizer is provided, an instance of DataCollatorWithPadding otherwise.
• train_dataset (Union[torch.utils.data.Dataset, torch.utils.data.IterableDataset, datasets.Dataset], optional) — The dataset to use for training. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed.

  Note that if it’s a torch.utils.data.IterableDataset with some randomization and you are training in a distributed fashion, your iterable dataset should either use a internal attribute generator that is a torch.Generator for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this generator at each epoch) or have a set_epoch() method that internally sets the seed of the RNGs used.

• eval_dataset (Union[torch.utils.data.Dataset, Dict[str, torch.utils.data.Dataset, datasets.Dataset]), optional) — The dataset to use for evaluation. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. If it is a dictionary, it will evaluate on each dataset prepending the dictionary key to the metric name.
• tokenizer (PreTrainedTokenizerBase, optional) — The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model.
• model_init (Callable[[], PreTrainedModel], optional) — A function that instantiates the model to be used. If provided, each call to train() will start from a new instance of the model as given by this function.

  The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to be able to choose different architectures according to hyper parameters (such as layer count, sizes of inner layers, dropout probabilities etc).

• compute_metrics (Callable[[EvalPrediction], Dict], optional) — The function that will be used to compute metrics at evaluation. Must take a EvalPrediction and return a dictionary string to metric values. Note When passing TrainingArgs with batch_eval_metrics set to True, your compute_metrics function must take a boolean compute_result argument. This will be triggered after the last eval batch to signal that the function needs to calculate and return the global summary statistics rather than accumulating the batch-level statistics.
• callbacks (List of TrainerCallback, optional) — A list of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in here.

  If you want to remove one of the default callbacks used, use the Trainer.remove_callback() method.

• optimizers (Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR], optional, defaults to (None, None)) — A tuple containing the optimizer and the scheduler to use. Will default to an instance of AdamW on your model and a scheduler given by get_linear_schedule_with_warmup() controlled by args.
• preprocess_logits_for_metrics (Callable[[torch.Tensor, torch.Tensor], torch.Tensor], optional) — A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by compute_metrics.

  Note that the labels (second parameter) will be None if the dataset does not have them.

Parameters

• output_dir (str) — The output directory where the model predictions and checkpoints will be written.
• overwrite_output_dir (bool, optional, defaults to False) — If True, overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory.
• do_train (bool, optional, defaults to False) — Whether to run training or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• do_eval (bool, optional) — Whether to run evaluation on the validation set or not. Will be set to True if eval_strategy is different from "no". This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• do_predict (bool, optional, defaults to False) — Whether to run predictions on the test set or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• eval_strategy (str or IntervalStrategy, optional, defaults to "no") — The evaluation strategy to adopt during training. Possible values are:
  • "no": No evaluation is done during training.
  • "steps": Evaluation is done (and logged) every eval_steps.
  • "epoch": Evaluation is done at the end of each epoch.
• prediction_loss_only (bool, optional, defaults to False) — When performing evaluation and generating predictions, only returns the loss.
• per_device_train_batch_size (int, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training.
• per_device_eval_batch_size (int, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation.
• gradient_accumulation_steps (int, optional, defaults to 1) — Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

  When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

• eval_accumulation_steps (int, optional) — Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but requires more memory).
• eval_delay (float, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy.
• torch_empty_cache_steps (int, optional) — Number of steps to wait before calling torch.<device>.empty_cache(). If left unset or set to None, cache will not be emptied.

  This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about 10% slower performance.

• learning_rate (float, optional, defaults to 5e-5) — The initial learning rate for AdamW optimizer.
• weight_decay (float, optional, defaults to 0) — The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer.
• adam_beta1 (float, optional, defaults to 0.9) — The beta1 hyperparameter for the AdamW optimizer.
• adam_beta2 (float, optional, defaults to 0.999) — The beta2 hyperparameter for the AdamW optimizer.
• adam_epsilon (float, optional, defaults to 1e-8) — The epsilon hyperparameter for the AdamW optimizer.
• max_grad_norm (float, optional, defaults to 1.0) — Maximum gradient norm (for gradient clipping).
• num_train_epochs(float, optional, defaults to 3.0) — Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).
• max_steps (int, optional, defaults to -1) — If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.
• lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") — The scheduler type to use. See the documentation of SchedulerType for all possible values.
• lr_scheduler_kwargs (‘dict’, optional, defaults to {}) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
• warmup_ratio (float, optional, defaults to 0.0) — Ratio of total training steps used for a linear warmup from 0 to learning_rate.
• warmup_steps (int, optional, defaults to 0) — Number of steps used for a linear warmup from 0 to learning_rate. Overrides any effect of warmup_ratio.
• log_level (str, optional, defaults to passive) — Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and keeps the current log level for the Transformers library (which will be "warning" by default).
• log_level_replica (str, optional, defaults to "warning") — Logger log level to use on replicas. Same choices as log_level”
• log_on_each_node (bool, optional, defaults to True) — In multinode distributed training, whether to log using log_level once per node, or only on the main node.
• logging_dir (str, optional) — TensorBoard log directory. Will default to *output_dir/runs/CURRENT_DATETIME_HOSTNAME*.
• logging_strategy (str or IntervalStrategy, optional, defaults to "steps") — The logging strategy to adopt during training. Possible values are:
  • "no": No logging is done during training.
  • "epoch": Logging is done at the end of each epoch.
  • "steps": Logging is done every logging_steps.
• logging_first_step (bool, optional, defaults to False) — Whether to log the first global_step or not.
• logging_steps (int or float, optional, defaults to 500) — Number of update steps between two logs if logging_strategy="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.
• logging_nan_inf_filter (bool, optional, defaults to True) — Whether to filter nan and inf losses for logging. If set to True the loss of every step that is nan or inf is filtered and the average loss of the current logging window is taken instead.

  logging_nan_inf_filter only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

• save_strategy (str or IntervalStrategy, optional, defaults to "steps") — The checkpoint save strategy to adopt during training. Possible values are:
  • "no": No save is done during training.
  • "epoch": Save is done at the end of each epoch.
  • "steps": Save is done every save_steps.

  If "epoch" or "steps" is chosen, saving will also be performed at the very end of training, always.

• save_steps (int or float, optional, defaults to 500) — Number of updates steps before two checkpoint saves if save_strategy="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.
• save_total_limit (int, optional) — 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, the four last checkpoints will always be retained alongside the best model. When save_total_limit=1 and load_best_model_at_end, it is possible that two checkpoints are saved: the last one and the best one (if they are different).
• save_safetensors (bool, optional, defaults to True) — Use safetensors saving and loading for state dicts instead of default torch.load and torch.save.
• save_on_each_node (bool, optional, defaults to False) — When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one.

  This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

• save_only_model (bool, optional, defaults to False) — When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state. Note that when this is true, you won’t be able to resume training from checkpoint. This enables you to save storage by not storing the optimizer, scheduler & rng state. You can only load the model using from_pretrained with this option set to True.
• restore_callback_states_from_checkpoint (bool, optional, defaults to False) — Whether to restore the callback states from the checkpoint. If True, will override callbacks passed to the Trainer if they exist in the checkpoint.”
• use_cpu (bool, optional, defaults to False) — Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
• seed (int, optional, defaults to 42) — Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the ~Trainer.model_init function to instantiate the model if it has some randomly initialized parameters.
• data_seed (int, optional) — Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as seed. This can be used to ensure reproducibility of data sampling, independent of the model seed.
• jit_mode_eval (bool, optional, defaults to False) — Whether or not to use PyTorch jit trace for inference.
• use_ipex (bool, optional, defaults to False) — Use Intel extension for PyTorch when it is available. IPEX installation.
• bf16 (bool, optional, defaults to False) — Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
• fp16 (bool, optional, defaults to False) — Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
• fp16_opt_level (str, optional, defaults to ‘O1’) — For fp16 training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on the Apex documentation.
• fp16_backend (str, optional, defaults to "auto") — This argument is deprecated. Use half_precision_backend instead.
• half_precision_backend (str, optional, defaults to "auto") — The backend to use for mixed precision training. Must be one of "auto", "apex", "cpu_amp". "auto" will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.
• bf16_full_eval (bool, optional, defaults to False) — Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change.
• fp16_full_eval (bool, optional, defaults to False) — Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values.
• tf32 (bool, optional) — Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch’s version default of torch.backends.cuda.matmul.allow_tf32. For more details please refer to the TF32 documentation. This is an experimental API and it may change.
• local_rank (int, optional, defaults to -1) — Rank of the process during distributed training.
• ddp_backend (str, optional) — The backend to use for distributed training. Must be one of "nccl", "mpi", "ccl", "gloo", "hccl".
• tpu_num_cores (int, optional) — When training on TPU, the number of TPU cores (automatically passed by launcher script).
• dataloader_drop_last (bool, optional, defaults to False) — Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.
• eval_steps (int or float, optional) — Number of update steps between two evaluations if eval_strategy="steps". Will default to the same value as logging_steps if not set. Should be an integer or a float in range [0,1). If smaller than 1, will be interpreted as ratio of total training steps.
• dataloader_num_workers (int, optional, defaults to 0) — Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.
• past_index (int, optional, defaults to -1) — Some models like TransformerXL or XLNet can make use of the past hidden states for their predictions. If this argument is set to a positive int, the Trainer will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument mems.
• run_name (str, optional, defaults to output_dir) — A descriptor for the run. Typically used for wandb, mlflow and comet logging. If not specified, will be the same as output_dir.
• disable_tqdm (bool, optional) — Whether or not to disable the tqdm progress bars and table of metrics produced by ~notebook.NotebookTrainingTracker in Jupyter Notebooks. Will default to True if the logging level is set to warn or lower (default), False otherwise.
• remove_unused_columns (bool, optional, defaults to True) — Whether or not to automatically remove the columns unused by the model forward method.
• label_names (List[str], optional) — The list of keys in your dictionary of inputs that correspond to the labels.

  Will eventually default to the list of argument names accepted by the model that contain the word “label”, except if the model used is one of the XxxForQuestionAnswering in which case it will also include the ["start_positions", "end_positions"] keys.

• load_best_model_at_end (bool, optional, defaults to False) — 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.

  When set to True, the parameters save_strategy needs to be the same as eval_strategy, and in the case it is “steps”, save_steps must be a round multiple of eval_steps.

• metric_for_best_model (str, optional) — Use in conjunction with load_best_model_at_end to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix "eval_". Will default to "loss" if unspecified and load_best_model_at_end=True (to use the evaluation loss).

  If you set this value, greater_is_better will default to True. Don’t forget to set it to False if your metric is better when lower.

• greater_is_better (bool, optional) — Use in conjunction with load_best_model_at_end and metric_for_best_model to specify if better models should have a greater metric or not. Will default to:
  • True if metric_for_best_model is set to a value that doesn’t end in "loss".
  • False if metric_for_best_model is not set, or set to a value that ends in "loss".
• ignore_data_skip (bool, optional, defaults to False) — When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to True, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have.
• fsdp (bool, str or list of FSDPOption, optional, defaults to '') — Use PyTorch Distributed Parallel Training (in distributed training only).

  A list of options along the following:

  • "full_shard": Shard parameters, gradients and optimizer states.
  • "shard_grad_op": Shard optimizer states and gradients.
  • "hybrid_shard": Apply FULL_SHARD within a node, and replicate parameters across nodes.
  • "hybrid_shard_zero2": Apply SHARD_GRAD_OP within a node, and replicate parameters across nodes.
  • "offload": Offload parameters and gradients to CPUs (only compatible with "full_shard" and "shard_grad_op").
  • "auto_wrap": Automatically recursively wrap layers with FSDP using default_auto_wrap_policy.
• fsdp_config (str or dict, optional) — Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of fsdp json config file (e.g., fsdp_config.json) or an already loaded json file as dict.

  A List of config and its options:

  • min_num_params (int, optional, defaults to 0): FSDP’s minimum number of parameters for Default Auto Wrapping. (useful only when fsdp field is passed).

  • transformer_layer_cls_to_wrap (List[str], optional): List of transformer layer class names (case-sensitive) to wrap, e.g, BertLayer, GPTJBlock, T5Block … (useful only when fsdp flag is passed).

  • backward_prefetch (str, optional) FSDP’s backward prefetch mode. Controls when to prefetch next set of parameters (useful only when fsdp field is passed).

    A list of options along the following:

    • "backward_pre" : Prefetches the next set of parameters before the current set of parameter’s gradient computation.
    • "backward_post" : This prefetches the next set of parameters after the current set of parameter’s gradient computation.

  • forward_prefetch (bool, optional, defaults to False) FSDP’s forward prefetch mode (useful only when fsdp field is passed). If "True", then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass.

  • limit_all_gathers (bool, optional, defaults to False) FSDP’s limit_all_gathers (useful only when fsdp field is passed). If "True", FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers.

  • use_orig_params (bool, optional, defaults to True) If "True", allows non-uniform requires_grad during init, which means support for interspersed frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019

  • sync_module_states (bool, optional, defaults to True) If "True", each individually wrapped FSDP unit will broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization

  • cpu_ram_efficient_loading (bool, optional, defaults to False) If "True", only the first process loads the pretrained model checkpoint while all other processes have empty weights. When this setting as "True", sync_module_states also must to be "True", otherwise all the processes except the main process would have random weights leading to unexpected behaviour during training.

  • activation_checkpointing (bool, optional, defaults to False): If "True", activation checkpointing is a technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage.

  • xla (bool, optional, defaults to False): Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature and its API may evolve in the future.

  • xla_fsdp_settings (dict, optional) The value is a dictionary which stores the XLA FSDP wrapping parameters.

    For a complete list of options, please see here.

  • xla_fsdp_grad_ckpt (bool, optional, defaults to False): Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.

• deepspeed (str or dict, optional) — Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., ds_config.json) or an already loaded json file as a dict”
  If enabling any Zero-init, make sure that your model is not initialized until *after* initializing the `TrainingArguments`, else it will not be applied.
• accelerator_config (str, dict, or AcceleratorConfig, optional) — Config to be used with the internal Accelerator implementation. The value is either a location of accelerator json config file (e.g., accelerator_config.json), an already loaded json file as dict, or an instance of AcceleratorConfig.

  A list of config and its options:

  • split_batches (bool, optional, defaults to False): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If True the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the num_processes you are using. If False, actual batch size used will be the one set in your script multiplied by the number of processes.
  • dispatch_batches (bool, optional): If set to True, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to True for DataLoader whose underlying dataset is an IterableDataset, False otherwise.
  • even_batches (bool, optional, defaults to True): If set to True, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers.
  • use_seedable_sampler (bool, optional, defaults to True): Whether or not use a fully seedable random sampler (accelerate.data_loader.SeedableRandomSampler). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with ~utils.set_seed for the best results.
  • use_configured_state (bool, optional, defaults to False): Whether or not to use a pre-configured AcceleratorState or PartialState defined before calling TrainingArguments. If True, an Accelerator or PartialState must be initialized. Note that by doing so, this could lead to issues with hyperparameter tuning.
• label_smoothing_factor (float, optional, defaults to 0.0) — The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_smoothing_factor/num_labels respectively.
• debug (str or list of DebugOption, optional, defaults to "") — Enable one or more debug features. This is an experimental feature.

  Possible options are:

  • "underflow_overflow": detects overflow in model’s input/outputs and reports the last frames that led to the event
  • "tpu_metrics_debug": print debug metrics on TPU

  The options should be separated by whitespaces.

• optim (str or training_args.OptimizerNames, optional, defaults to "adamw_torch") — The optimizer to use, such as “adamw_hf”, “adamw_torch”, “adamw_torch_fused”, “adamw_apex_fused”, “adamw_anyprecision”, “adafactor”. See OptimizerNames in training_args.py for a full list of optimizers.
• optim_args (str, optional) — Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
• group_by_length (bool, optional, defaults to False) — Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.
• length_column_name (str, optional, defaults to "length") — Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless group_by_length is True and the dataset is an instance of Dataset.
• report_to (str or List[str], optional, defaults to "all") — The list of integrations to report the results and logs to. Supported platforms are "azure_ml", "clearml", "codecarbon", "comet_ml", "dagshub", "dvclive", "flyte", "mlflow", "neptune", "tensorboard", and "wandb". Use "all" to report to all integrations installed, "none" for no integrations.
• ddp_find_unused_parameters (bool, optional) — When using distributed training, the value of the flag find_unused_parameters passed to DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.
• ddp_bucket_cap_mb (int, optional) — When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel.
• ddp_broadcast_buffers (bool, optional) — When using distributed training, the value of the flag broadcast_buffers passed to DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.
• dataloader_pin_memory (bool, optional, defaults to True) — Whether you want to pin memory in data loaders or not. Will default to True.
• dataloader_persistent_workers (bool, optional, defaults to False) — If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to False.
• dataloader_prefetch_factor (int, optional) — Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.
• skip_memory_metrics (bool, optional, defaults to True) — Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed.
• push_to_hub (bool, optional, defaults to False) — Whether or not to push the model to the Hub every time the model is saved. If this is activated, output_dir will begin a git directory synced with the repo (determined by hub_model_id) and the content will be pushed each time a save is triggered (depending on your save_strategy). Calling save_model() will also trigger a push.

  If output_dir exists, it needs to be a local clone of the repository to which the Trainer will be pushed.

• resume_from_checkpoint (str, optional) — The path to a folder with a valid checkpoint for your model. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• hub_model_id (str, optional) — The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance "user_name/model", which allows you to push to an organization you are a member of with "organization_name/model". Will default to user_name/output_dir_name with output_dir_name being the name of output_dir.

  Will default to the name of output_dir.

• hub_strategy (str or HubStrategy, optional, defaults to "every_save") — Defines the scope of what is pushed to the Hub and when. Possible values are:
  • "end": push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called.
  • "every_save": push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training.
  • "checkpoint": like "every_save" but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with trainer.train(resume_from_checkpoint="last-checkpoint").
  • "all_checkpoints": like "checkpoint" but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)
• hub_token (str, optional) — The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with huggingface-cli login.
• hub_private_repo (bool, optional, defaults to False) — If True, the Hub repo will be set to private.
• hub_always_push (bool, optional, defaults to False) — Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished.
• gradient_checkpointing (bool, optional, defaults to False) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.
• gradient_checkpointing_kwargs (dict, optional, defaults to None) — Key word arguments to be passed to the gradient_checkpointing_enable method.
• include_inputs_for_metrics (bool, optional, defaults to False) — Whether or not the inputs will be passed to the compute_metrics function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class.
• eval_do_concat_batches (bool, optional, defaults to True) — Whether to recursively concat inputs/losses/labels/predictions across batches. If False, will instead store them as lists, with each batch kept separate.
• auto_find_batch_size (bool, optional, defaults to False) — Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (pip install accelerate)
• full_determinism (bool, optional, defaults to False) — If True, enable_full_determinism() is called instead of set_seed() to ensure reproducible results in distributed training. Important: this will negatively impact the performance, so only use it for debugging.
• torchdynamo (str, optional) — If set, the backend compiler for TorchDynamo. Possible choices are "eager", "aot_eager", "inductor", "nvfuser", "aot_nvfuser", "aot_cudagraphs", "ofi", "fx2trt", "onnxrt" and "ipex".
• ray_scope (str, optional, defaults to "last") — 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 for more options.
• ddp_timeout (int, optional, defaults to 1800) — The timeout for torch.distributed.init_process_group calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information.
• use_mps_device (bool, optional, defaults to False) — This argument is deprecated.mps device will be used if it is available similar to cuda device.
• torch_compile (bool, optional, defaults to False) — Whether or not to compile the model using PyTorch 2.0 torch.compile.

  This will use the best defaults for the torch.compile API. You can customize the defaults with the argument torch_compile_backend and torch_compile_mode but we don’t guarantee any of them will work as the support is progressively rolled in in PyTorch.

  This flag and the whole compile API is experimental and subject to change in future releases.

• torch_compile_backend (str, optional) — The backend to use in torch.compile. If set to any value, torch_compile will be set to True.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

• torch_compile_mode (str, optional) — The mode to use in torch.compile. If set to any value, torch_compile will be set to True.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

• split_batches (bool, optional) — Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If

  set to True, the actual batch size used will be the same on any kind of distributed processes, but it must be a

  round multiple of the number of processes you are using (such as GPUs).

• include_tokens_per_second (bool, optional) — Whether or not to compute the number of tokens per second per device for training speed metrics.

  This will iterate over the entire training dataloader once beforehand,

  and will slow down the entire process.

• include_num_input_tokens_seen (bool, optional) — Whether or not to track the number of input tokens seen throughout training.

  May be slower in distributed training as gather operations must be called.

• neftune_noise_alpha (Optional[float]) — If not None, this will activate NEFTune noise embeddings. This can drastically improve model performance for instruction fine-tuning. Check out the original paper and the original code. Support transformers PreTrainedModel and also PeftModel from peft. The original paper used values in the range [5.0, 15.0].
• optim_target_modules (Union[str, List[str]], optional) — The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm https://arxiv.org/abs/2403.03507 See: https://github.com/jiaweizzhao/GaLore for more details. You need to make sure to pass a valid GaloRe optimizer, e.g. one of: “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are nn.Linear modules only.
• batch_eval_metrics (Optional[bool], defaults to False) — If set to True, evaluation will call compute_metrics at the end of each batch to accumulate statistics rather than saving all eval logits in memory. When set to True, you must pass a compute_metrics function that takes a boolean argument compute_result, which when passed True, will trigger the final global summary statistics from the batch-level summary statistics you’ve accumulated over the evaluation set.
• eval_on_start (bool, optional, defaults to False) — Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly.
• eval_use_gather_object (bool, optional, defaults to False) — Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. This should only be enabled if users are not just returning tensors, and this is actively discouraged by PyTorch.
• use_liger_kernel (bool, optional, defaults to False) — Whether enable Liger Kernel for LLM model training. It can effectively increase multi-GPU training throughput by ~20% and reduces memory usage by ~60%, works out of the box with flash attention, PyTorch FSDP, and Microsoft DeepSpeed. Currently, it supports llama, mistral, mixtral and gemma models.

Parameters

• output_dir (str) — The output directory where the model predictions and checkpoints will be written.
• overwrite_output_dir (bool, optional, defaults to False) — If True, overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory.
• do_train (bool, optional, defaults to False) — Whether to run training or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• do_eval (bool, optional) — Whether to run evaluation on the validation set or not. Will be set to True if eval_strategy is different from "no". This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• do_predict (bool, optional, defaults to False) — Whether to run predictions on the test set or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• eval_strategy (str or IntervalStrategy, optional, defaults to "no") — The evaluation strategy to adopt during training. Possible values are:
  • "no": No evaluation is done during training.
  • "steps": Evaluation is done (and logged) every eval_steps.
  • "epoch": Evaluation is done at the end of each epoch.
• prediction_loss_only (bool, optional, defaults to False) — When performing evaluation and generating predictions, only returns the loss.
• per_device_train_batch_size (int, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training.
• per_device_eval_batch_size (int, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation.
• gradient_accumulation_steps (int, optional, defaults to 1) — Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

  When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

• eval_accumulation_steps (int, optional) — Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but requires more memory).
• eval_delay (float, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy.
• torch_empty_cache_steps (int, optional) — Number of steps to wait before calling torch.<device>.empty_cache(). If left unset or set to None, cache will not be emptied.

  This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about 10% slower performance.

• learning_rate (float, optional, defaults to 5e-5) — The initial learning rate for AdamW optimizer.
• weight_decay (float, optional, defaults to 0) — The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer.
• adam_beta1 (float, optional, defaults to 0.9) — The beta1 hyperparameter for the AdamW optimizer.
• adam_beta2 (float, optional, defaults to 0.999) — The beta2 hyperparameter for the AdamW optimizer.
• adam_epsilon (float, optional, defaults to 1e-8) — The epsilon hyperparameter for the AdamW optimizer.
• max_grad_norm (float, optional, defaults to 1.0) — Maximum gradient norm (for gradient clipping).
• num_train_epochs(float, optional, defaults to 3.0) — Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).
• max_steps (int, optional, defaults to -1) — If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.
• lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") — The scheduler type to use. See the documentation of SchedulerType for all possible values.
• lr_scheduler_kwargs (‘dict’, optional, defaults to {}) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
• warmup_ratio (float, optional, defaults to 0.0) — Ratio of total training steps used for a linear warmup from 0 to learning_rate.
• warmup_steps (int, optional, defaults to 0) — Number of steps used for a linear warmup from 0 to learning_rate. Overrides any effect of warmup_ratio.
• log_level (str, optional, defaults to passive) — Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and keeps the current log level for the Transformers library (which will be "warning" by default).
• log_level_replica (str, optional, defaults to "warning") — Logger log level to use on replicas. Same choices as log_level”
• log_on_each_node (bool, optional, defaults to True) — In multinode distributed training, whether to log using log_level once per node, or only on the main node.
• logging_dir (str, optional) — TensorBoard log directory. Will default to *output_dir/runs/CURRENT_DATETIME_HOSTNAME*.
• logging_strategy (str or IntervalStrategy, optional, defaults to "steps") — The logging strategy to adopt during training. Possible values are:
  • "no": No logging is done during training.
  • "epoch": Logging is done at the end of each epoch.
  • "steps": Logging is done every logging_steps.
• logging_first_step (bool, optional, defaults to False) — Whether to log the first global_step or not.
• logging_steps (int or float, optional, defaults to 500) — Number of update steps between two logs if logging_strategy="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.
• logging_nan_inf_filter (bool, optional, defaults to True) — Whether to filter nan and inf losses for logging. If set to True the loss of every step that is nan or inf is filtered and the average loss of the current logging window is taken instead.

  logging_nan_inf_filter only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

• save_strategy (str or IntervalStrategy, optional, defaults to "steps") — The checkpoint save strategy to adopt during training. Possible values are:
  • "no": No save is done during training.
  • "epoch": Save is done at the end of each epoch.
  • "steps": Save is done every save_steps.

  If "epoch" or "steps" is chosen, saving will also be performed at the very end of training, always.

• save_steps (int or float, optional, defaults to 500) — Number of updates steps before two checkpoint saves if save_strategy="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.
• save_total_limit (int, optional) — 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, the four last checkpoints will always be retained alongside the best model. When save_total_limit=1 and load_best_model_at_end, it is possible that two checkpoints are saved: the last one and the best one (if they are different).
• save_safetensors (bool, optional, defaults to True) — Use safetensors saving and loading for state dicts instead of default torch.load and torch.save.
• save_on_each_node (bool, optional, defaults to False) — When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one.

  This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

• save_only_model (bool, optional, defaults to False) — When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state. Note that when this is true, you won’t be able to resume training from checkpoint. This enables you to save storage by not storing the optimizer, scheduler & rng state. You can only load the model using from_pretrained with this option set to True.
• restore_callback_states_from_checkpoint (bool, optional, defaults to False) — Whether to restore the callback states from the checkpoint. If True, will override callbacks passed to the Trainer if they exist in the checkpoint.”
• use_cpu (bool, optional, defaults to False) — Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
• seed (int, optional, defaults to 42) — Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the ~Trainer.model_init function to instantiate the model if it has some randomly initialized parameters.
• data_seed (int, optional) — Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as seed. This can be used to ensure reproducibility of data sampling, independent of the model seed.
• jit_mode_eval (bool, optional, defaults to False) — Whether or not to use PyTorch jit trace for inference.
• use_ipex (bool, optional, defaults to False) — Use Intel extension for PyTorch when it is available. IPEX installation.
• bf16 (bool, optional, defaults to False) — Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
• fp16 (bool, optional, defaults to False) — Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
• fp16_opt_level (str, optional, defaults to ‘O1’) — For fp16 training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on the Apex documentation.
• fp16_backend (str, optional, defaults to "auto") — This argument is deprecated. Use half_precision_backend instead.
• half_precision_backend (str, optional, defaults to "auto") — The backend to use for mixed precision training. Must be one of "auto", "apex", "cpu_amp". "auto" will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.
• bf16_full_eval (bool, optional, defaults to False) — Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change.
• fp16_full_eval (bool, optional, defaults to False) — Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values.
• tf32 (bool, optional) — Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch’s version default of torch.backends.cuda.matmul.allow_tf32. For more details please refer to the TF32 documentation. This is an experimental API and it may change.
• local_rank (int, optional, defaults to -1) — Rank of the process during distributed training.
• ddp_backend (str, optional) — The backend to use for distributed training. Must be one of "nccl", "mpi", "ccl", "gloo", "hccl".
• tpu_num_cores (int, optional) — When training on TPU, the number of TPU cores (automatically passed by launcher script).
• dataloader_drop_last (bool, optional, defaults to False) — Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.
• eval_steps (int or float, optional) — Number of update steps between two evaluations if eval_strategy="steps". Will default to the same value as logging_steps if not set. Should be an integer or a float in range [0,1). If smaller than 1, will be interpreted as ratio of total training steps.
• dataloader_num_workers (int, optional, defaults to 0) — Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.
• past_index (int, optional, defaults to -1) — Some models like TransformerXL or XLNet can make use of the past hidden states for their predictions. If this argument is set to a positive int, the Trainer will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument mems.
• run_name (str, optional, defaults to output_dir) — A descriptor for the run. Typically used for wandb, mlflow and comet logging. If not specified, will be the same as output_dir.
• disable_tqdm (bool, optional) — Whether or not to disable the tqdm progress bars and table of metrics produced by ~notebook.NotebookTrainingTracker in Jupyter Notebooks. Will default to True if the logging level is set to warn or lower (default), False otherwise.
• remove_unused_columns (bool, optional, defaults to True) — Whether or not to automatically remove the columns unused by the model forward method.
• label_names (List[str], optional) — The list of keys in your dictionary of inputs that correspond to the labels.

  Will eventually default to the list of argument names accepted by the model that contain the word “label”, except if the model used is one of the XxxForQuestionAnswering in which case it will also include the ["start_positions", "end_positions"] keys.

• load_best_model_at_end (bool, optional, defaults to False) — 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.

  When set to True, the parameters save_strategy needs to be the same as eval_strategy, and in the case it is “steps”, save_steps must be a round multiple of eval_steps.

• metric_for_best_model (str, optional) — Use in conjunction with load_best_model_at_end to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix "eval_". Will default to "loss" if unspecified and load_best_model_at_end=True (to use the evaluation loss).

  If you set this value, greater_is_better will default to True. Don’t forget to set it to False if your metric is better when lower.

• greater_is_better (bool, optional) — Use in conjunction with load_best_model_at_end and metric_for_best_model to specify if better models should have a greater metric or not. Will default to:
  • True if metric_for_best_model is set to a value that doesn’t end in "loss".
  • False if metric_for_best_model is not set, or set to a value that ends in "loss".
• ignore_data_skip (bool, optional, defaults to False) — When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to True, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have.
• fsdp (bool, str or list of FSDPOption, optional, defaults to '') — Use PyTorch Distributed Parallel Training (in distributed training only).

  A list of options along the following:

  • "full_shard": Shard parameters, gradients and optimizer states.
  • "shard_grad_op": Shard optimizer states and gradients.
  • "hybrid_shard": Apply FULL_SHARD within a node, and replicate parameters across nodes.
  • "hybrid_shard_zero2": Apply SHARD_GRAD_OP within a node, and replicate parameters across nodes.
  • "offload": Offload parameters and gradients to CPUs (only compatible with "full_shard" and "shard_grad_op").
  • "auto_wrap": Automatically recursively wrap layers with FSDP using default_auto_wrap_policy.
• fsdp_config (str or dict, optional) — Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of fsdp json config file (e.g., fsdp_config.json) or an already loaded json file as dict.

  A List of config and its options:

  • min_num_params (int, optional, defaults to 0): FSDP’s minimum number of parameters for Default Auto Wrapping. (useful only when fsdp field is passed).

  • transformer_layer_cls_to_wrap (List[str], optional): List of transformer layer class names (case-sensitive) to wrap, e.g, BertLayer, GPTJBlock, T5Block … (useful only when fsdp flag is passed).

  • backward_prefetch (str, optional) FSDP’s backward prefetch mode. Controls when to prefetch next set of parameters (useful only when fsdp field is passed).

    A list of options along the following:

    • "backward_pre" : Prefetches the next set of parameters before the current set of parameter’s gradient computation.
    • "backward_post" : This prefetches the next set of parameters after the current set of parameter’s gradient computation.

  • forward_prefetch (bool, optional, defaults to False) FSDP’s forward prefetch mode (useful only when fsdp field is passed). If "True", then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass.

  • limit_all_gathers (bool, optional, defaults to False) FSDP’s limit_all_gathers (useful only when fsdp field is passed). If "True", FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers.

  • use_orig_params (bool, optional, defaults to True) If "True", allows non-uniform requires_grad during init, which means support for interspersed frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019

  • sync_module_states (bool, optional, defaults to True) If "True", each individually wrapped FSDP unit will broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization

  • cpu_ram_efficient_loading (bool, optional, defaults to False) If "True", only the first process loads the pretrained model checkpoint while all other processes have empty weights. When this setting as "True", sync_module_states also must to be "True", otherwise all the processes except the main process would have random weights leading to unexpected behaviour during training.

  • activation_checkpointing (bool, optional, defaults to False): If "True", activation checkpointing is a technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage.

  • xla (bool, optional, defaults to False): Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature and its API may evolve in the future.

  • xla_fsdp_settings (dict, optional) The value is a dictionary which stores the XLA FSDP wrapping parameters.

    For a complete list of options, please see here.

  • xla_fsdp_grad_ckpt (bool, optional, defaults to False): Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.

• deepspeed (str or dict, optional) — Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., ds_config.json) or an already loaded json file as a dict”
  If enabling any Zero-init, make sure that your model is not initialized until *after* initializing the `TrainingArguments`, else it will not be applied.
• accelerator_config (str, dict, or AcceleratorConfig, optional) — Config to be used with the internal Accelerator implementation. The value is either a location of accelerator json config file (e.g., accelerator_config.json), an already loaded json file as dict, or an instance of AcceleratorConfig.

  A list of config and its options:

  • split_batches (bool, optional, defaults to False): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If True the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the num_processes you are using. If False, actual batch size used will be the one set in your script multiplied by the number of processes.
  • dispatch_batches (bool, optional): If set to True, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to True for DataLoader whose underlying dataset is an IterableDataset, False otherwise.
  • even_batches (bool, optional, defaults to True): If set to True, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers.
  • use_seedable_sampler (bool, optional, defaults to True): Whether or not use a fully seedable random sampler (accelerate.data_loader.SeedableRandomSampler). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with ~utils.set_seed for the best results.
  • use_configured_state (bool, optional, defaults to False): Whether or not to use a pre-configured AcceleratorState or PartialState defined before calling TrainingArguments. If True, an Accelerator or PartialState must be initialized. Note that by doing so, this could lead to issues with hyperparameter tuning.
• label_smoothing_factor (float, optional, defaults to 0.0) — The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_smoothing_factor/num_labels respectively.
• debug (str or list of DebugOption, optional, defaults to "") — Enable one or more debug features. This is an experimental feature.

  Possible options are:

  • "underflow_overflow": detects overflow in model’s input/outputs and reports the last frames that led to the event
  • "tpu_metrics_debug": print debug metrics on TPU

  The options should be separated by whitespaces.

• optim (str or training_args.OptimizerNames, optional, defaults to "adamw_torch") — The optimizer to use, such as “adamw_hf”, “adamw_torch”, “adamw_torch_fused”, “adamw_apex_fused”, “adamw_anyprecision”, “adafactor”. See OptimizerNames in training_args.py for a full list of optimizers.
• optim_args (str, optional) — Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
• group_by_length (bool, optional, defaults to False) — Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.
• length_column_name (str, optional, defaults to "length") — Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless group_by_length is True and the dataset is an instance of Dataset.
• report_to (str or List[str], optional, defaults to "all") — The list of integrations to report the results and logs to. Supported platforms are "azure_ml", "clearml", "codecarbon", "comet_ml", "dagshub", "dvclive", "flyte", "mlflow", "neptune", "tensorboard", and "wandb". Use "all" to report to all integrations installed, "none" for no integrations.
• ddp_find_unused_parameters (bool, optional) — When using distributed training, the value of the flag find_unused_parameters passed to DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.
• ddp_bucket_cap_mb (int, optional) — When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel.
• ddp_broadcast_buffers (bool, optional) — When using distributed training, the value of the flag broadcast_buffers passed to DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.
• dataloader_pin_memory (bool, optional, defaults to True) — Whether you want to pin memory in data loaders or not. Will default to True.
• dataloader_persistent_workers (bool, optional, defaults to False) — If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to False.
• dataloader_prefetch_factor (int, optional) — Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.
• skip_memory_metrics (bool, optional, defaults to True) — Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed.
• push_to_hub (bool, optional, defaults to False) — Whether or not to push the model to the Hub every time the model is saved. If this is activated, output_dir will begin a git directory synced with the repo (determined by hub_model_id) and the content will be pushed each time a save is triggered (depending on your save_strategy). Calling save_model() will also trigger a push.

  If output_dir exists, it needs to be a local clone of the repository to which the Trainer will be pushed.

• resume_from_checkpoint (str, optional) — The path to a folder with a valid checkpoint for your model. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.
• hub_model_id (str, optional) — The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance "user_name/model", which allows you to push to an organization you are a member of with "organization_name/model". Will default to user_name/output_dir_name with output_dir_name being the name of output_dir.

  Will default to the name of output_dir.

• hub_strategy (str or HubStrategy, optional, defaults to "every_save") — Defines the scope of what is pushed to the Hub and when. Possible values are:
  • "end": push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called.
  • "every_save": push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training.
  • "checkpoint": like "every_save" but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with trainer.train(resume_from_checkpoint="last-checkpoint").
  • "all_checkpoints": like "checkpoint" but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)
• hub_token (str, optional) — The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with huggingface-cli login.
• hub_private_repo (bool, optional, defaults to False) — If True, the Hub repo will be set to private.
• hub_always_push (bool, optional, defaults to False) — Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished.
• gradient_checkpointing (bool, optional, defaults to False) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.
• gradient_checkpointing_kwargs (dict, optional, defaults to None) — Key word arguments to be passed to the gradient_checkpointing_enable method.
• include_inputs_for_metrics (bool, optional, defaults to False) — Whether or not the inputs will be passed to the compute_metrics function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class.
• eval_do_concat_batches (bool, optional, defaults to True) — Whether to recursively concat inputs/losses/labels/predictions across batches. If False, will instead store them as lists, with each batch kept separate.
• auto_find_batch_size (bool, optional, defaults to False) — Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (pip install accelerate)
• full_determinism (bool, optional, defaults to False) — If True, enable_full_determinism() is called instead of set_seed() to ensure reproducible results in distributed training. Important: this will negatively impact the performance, so only use it for debugging.
• torchdynamo (str, optional) — If set, the backend compiler for TorchDynamo. Possible choices are "eager", "aot_eager", "inductor", "nvfuser", "aot_nvfuser", "aot_cudagraphs", "ofi", "fx2trt", "onnxrt" and "ipex".
• ray_scope (str, optional, defaults to "last") — 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 for more options.
• ddp_timeout (int, optional, defaults to 1800) — The timeout for torch.distributed.init_process_group calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information.
• use_mps_device (bool, optional, defaults to False) — This argument is deprecated.mps device will be used if it is available similar to cuda device.
• torch_compile (bool, optional, defaults to False) — Whether or not to compile the model using PyTorch 2.0 torch.compile.

  This will use the best defaults for the torch.compile API. You can customize the defaults with the argument torch_compile_backend and torch_compile_mode but we don’t guarantee any of them will work as the support is progressively rolled in in PyTorch.

  This flag and the whole compile API is experimental and subject to change in future releases.

• torch_compile_backend (str, optional) — The backend to use in torch.compile. If set to any value, torch_compile will be set to True.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

• torch_compile_mode (str, optional) — The mode to use in torch.compile. If set to any value, torch_compile will be set to True.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

• split_batches (bool, optional) — Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If

  set to True, the actual batch size used will be the same on any kind of distributed processes, but it must be a

  round multiple of the number of processes you are using (such as GPUs).

• include_tokens_per_second (bool, optional) — Whether or not to compute the number of tokens per second per device for training speed metrics.

  This will iterate over the entire training dataloader once beforehand,

  and will slow down the entire process.

• include_num_input_tokens_seen (bool, optional) — Whether or not to track the number of input tokens seen throughout training.

  May be slower in distributed training as gather operations must be called.

• neftune_noise_alpha (Optional[float]) — If not None, this will activate NEFTune noise embeddings. This can drastically improve model performance for instruction fine-tuning. Check out the original paper and the original code. Support transformers PreTrainedModel and also PeftModel from peft. The original paper used values in the range [5.0, 15.0].
• optim_target_modules (Union[str, List[str]], optional) — The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm https://arxiv.org/abs/2403.03507 See: https://github.com/jiaweizzhao/GaLore for more details. You need to make sure to pass a valid GaloRe optimizer, e.g. one of: “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are nn.Linear modules only.
• batch_eval_metrics (Optional[bool], defaults to False) — If set to True, evaluation will call compute_metrics at the end of each batch to accumulate statistics rather than saving all eval logits in memory. When set to True, you must pass a compute_metrics function that takes a boolean argument compute_result, which when passed True, will trigger the final global summary statistics from the batch-level summary statistics you’ve accumulated over the evaluation set.
• eval_on_start (bool, optional, defaults to False) — Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly.
• eval_use_gather_object (bool, optional, defaults to False) — Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. This should only be enabled if users are not just returning tensors, and this is actively discouraged by PyTorch.
• use_liger_kernel (bool, optional, defaults to False) — Whether enable Liger Kernel for LLM model training. It can effectively increase multi-GPU training throughput by ~20% and reduces memory usage by ~60%, works out of the box with flash attention, PyTorch FSDP, and Microsoft DeepSpeed. Currently, it supports llama, mistral, mixtral and gemma models.
• sortish_sampler (bool, optional, defaults to False) — Whether to use a sortish sampler or not. Only possible if the underlying datasets are Seq2SeqDataset for now but will become generally available in the near future.

  It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set.

• predict_with_generate (bool, optional, defaults to False) — Whether to use generate to calculate generative metrics (ROUGE, BLEU).
• generation_max_length (int, optional) — The max_length to use on each evaluation loop when predict_with_generate=True. Will default to the max_length value of the model configuration.
• generation_num_beams (int, optional) — The num_beams to use on each evaluation loop when predict_with_generate=True. Will default to the num_beams value of the model configuration.
• generation_config (str or Path or GenerationConfig, optional) — Allows to load a GenerationConfig from the from_pretrained method. This can be either:
  • a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  • a path to a directory containing a configuration file saved using the save_pretrained() method, e.g., ./my_model_directory/.
  • a GenerationConfig object.