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 processing_class is provided, an instance of DataCollatorWithPadding otherwise if the processing_class is a feature extractor or tokenizer.
• 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.
• processing_class (PreTrainedTokenizerBase or BaseImageProcessor or FeatureExtractionMixin or ProcessorMixin, optional) — Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, 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 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_loss_func (Callable, optional) — A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default loss function used by Trainer.
• 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.
• optimizer_cls_and_kwargs (tuple[Type[torch.optim.Optimizer], dict[str, Any]], optional) — A tuple containing the optimizer class and keyword arguments to use. Overrides optim and optim_args in args. Incompatible with the optimizers argument.

  Unlike optimizers, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer.

• 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, optional, defaults to "trainer_output") — The output directory where the model predictions and checkpoints will be written.

Training Duration and Batch Size

• per_device_train_batch_size (int, optional, defaults to 8) — The batch size per device. The global batch size is computed as: per_device_train_batch_size * number_of_devices in multi-GPU or distributed setups.
• 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) — Overrides num_train_epochs. If set to a positive number, the total number of training steps to perform. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.

Learning Rate & Scheduler

• learning_rate (float, optional, defaults to 5e-5) — The initial learning rate for the optimizer. This is typically the peak learning rate when using a scheduler with warmup.
• lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") — The learning rate scheduler type to use. See SchedulerType for all possible values. Common choices:
  • “linear” = get_linear_schedule_with_warmup()
  • “cosine” = get_cosine_schedule_with_warmup()
  • “constant” = get_constant_schedule()
  • “constant_with_warmup” = get_constant_schedule_with_warmup()
• lr_scheduler_kwargs (dict or str, optional, defaults to None) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
• warmup_steps (int or float, optional, defaults to 0) — Number of steps for a linear warmup from 0 to learning_rate. Warmup helps stabilize training in the initial phase. Can be:
  • An integer: exact number of warmup steps
  • A float in range [0, 1): interpreted as ratio of total training steps

Optimizer

• optim (str or training_args.OptimizerNames, optional, defaults to "adamw_torch" (for torch>=2.8 "adamw_torch_fused")) — The optimizer to use. Common options:
  • "adamw_torch": PyTorch’s AdamW (recommended default)
  • "adamw_torch_fused": Fused AdamW kernel
  • "adamw_hf": HuggingFace’s AdamW implementation
  • "sgd": Stochastic Gradient Descent with momentum
  • "adafactor": Memory-efficient optimizer for large models
  • "adamw_8bit": 8-bit AdamW (requires bitsandbytes) See OptimizerNames for the complete list.
• optim_args (str, optional) — Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
• weight_decay (float, optional, defaults to 0) — Weight decay coefficient applied by the optimizer (not the loss function). Adds L2 regularization to prevent overfitting by penalizing large weights. Automatically excluded from bias and LayerNorm parameters. Typical values: 0.01 (standard), 0.1 (stronger regularization), 0.0 (no regularization).
• adam_beta1 (float, optional, defaults to 0.9) — The exponential decay rate for the first moment estimates (momentum) in Adam-based optimizers. Controls how much history of gradients to retain.
• adam_beta2 (float, optional, defaults to 0.999) — The exponential decay rate for the second moment estimates (variance) in Adam-based optimizers. Controls adaptive learning rate scaling.
• adam_epsilon (float, optional, defaults to 1e-8) — Epsilon value for numerical stability in Adam-based optimizers. Prevents division by zero in the denominator of the update rule.
• optim_target_modules (Union[str, list[str]], optional) — The target modules to optimize, i.e. the module names that you would like to train. Currently used for the GaLore algorithm and APOLLO algorithm. See GaLore implementation and APOLLO implementation for more details. You need to make sure to pass a valid GaLore or APOLLO optimizer, e.g., one of: “apollo_adamw”, “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are nn.Linear modules only.

Regularization & Training Stability

• gradient_accumulation_steps (int, optional, defaults to 1) — Number of update steps to accumulate gradients before performing a backward/update pass. Simulates larger batch sizes without additional memory. Effective batch size = per_device_train_batch_size × num_devices × gradient_accumulation_steps.

  When using gradient accumulation, one “step” is counted as one step with a backward pass. Therefore, logging, evaluation, and saving will occur every gradient_accumulation_steps × xxx_step training examples.

• average_tokens_across_devices (bool, optional, defaults to True) — Whether or not to average tokens across devices. If enabled, will use all_reduce to synchronize num_tokens_in_batch for precise loss calculation. Reference: https://github.com/huggingface/transformers/issues/34242
• max_grad_norm (float, optional, defaults to 1.0) — Maximum gradient norm for gradient clipping. Applied after backward pass, before optimizer step. Prevents gradient explosion by scaling down gradients when their global norm exceeds this threshold. Set to 0 to disable clipping. Typical values: 1.0 (standard), 0.5 (more conservative), 5.0 (less aggressive).
• label_smoothing_factor (float, optional, defaults to 0.0) — Label smoothing factor to prevent overconfidence. Replaces hard 0/1 targets with soft targets: 0 becomes ε/num_labels and 1 becomes 1 - ε + ε/num_labels, where ε = label_smoothing_factor. Zero means no smoothing. Typical range: 0.0 to 0.1.

Mixed Precision Training

• bf16 (bool, optional, defaults to False) — Enable bfloat16 (BF16) mixed precision training Generally preferred over FP16 due to better numerical stability and no loss scaling required.
• fp16 (bool, optional, defaults to False) — Enable float16 (FP16) mixed precision training. Consider using BF16 instead if your hardware supports it.
• bf16_full_eval (bool, optional, defaults to False) — Use full BF16 precision for evaluation (not just mixed precision). Faster and saves memory but may affect metric values slightly. Only applies during evaluation.
• fp16_full_eval (bool, optional, defaults to False) — Use full FP16 precision for evaluation (not just mixed precision). Faster and saves memory but may affect metric values slightly. Only applies during evaluation.
• tf32 (bool, optional) — Enable TensorFloat-32 (TF32) mode on Ampere and newer GPUs. TF32 uses 19-bit precision for matrix multiplications (instead of FP32’s 23-bit), providing up to 8x speedup with negligible accuracy loss. Default depends on PyTorch version. See TF32 docs.

Gradient Checkpointing

• gradient_checkpointing (bool, optional, defaults to False) — Enable gradient checkpointing to trade compute for memory. Reduces memory usage by clearing activations during forward pass and recomputing them during backward pass. Enables training larger models or batch sizes at the cost of ~20% slower training.
• gradient_checkpointing_kwargs (dict, optional, defaults to None) — Keyword arguments passed to gradient_checkpointing_enable().

Compilation

• torch_compile (bool, optional, defaults to False) — Compile the model using PyTorch 2.0’s torch.compile() for faster training. Can provide 20-50% speedup with no code changes. Uses default compilation settings unless torch_compile_backend or torch_compile_mode are specified.
• torch_compile_backend (str, optional) — Backend for torch.compile(). If set, automatically enables torch_compile. Options include "inductor" (default), "aot_eager", "cudagraphs". Backends vary by PyTorch version - see PyTorch docs for available options.
• torch_compile_mode (str, optional) — Compilation mode for torch.compile(). If set, automatically enables torch_compile. Options: "default", "reduce-overhead" (minimize Python overhead), "max-autotune" (aggressive optimization, slower compile time).

Kernels

• use_liger_kernel (bool, optional, defaults to False) — Enable Liger Kernel optimizations. Increases multi-GPU throughput by ~20% and reduces memory usage by ~60%. Works with Flash Attention, FSDP, and DeepSpeed. Currently supports Llama, Mistral, Mixtral, and Gemma models.
• liger_kernel_config (Optional[dict], optional) — Configuration for Liger Kernel. Passed as kwargs to _apply_liger_kernel_to_instance(). Options typically include: "rope", "swiglu", "cross_entropy", "fused_linear_cross_entropy", "rms_norm". If None, uses default configuration.

Additional Optimizations

• use_cache (bool, optional, defaults to False) — Whether or not to enable cache for the model. For training, this is usually not needed apart from some PEFT methods that uses past_key_values.
• 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].
• 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.
• 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.

Logging & Monitoring Training

• 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_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_first_step (bool, optional, defaults to False) — Whether to log the first global_step or not.
• 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_nan_inf_filter (bool, optional, defaults to True) — Filter out NaN and Inf losses when logging. If True, replaces NaN/Inf losses with the average of recent valid losses. Does not affect gradient computation, only logging.
• include_num_input_tokens_seen (Optional[Union[str, bool]], optional, defaults to “no”) — Whether to track the number of input tokens seen. Must be one of [“all”, “non_padding”, “no”] or a boolean value which map to “all” or “no”. May be slower in distributed training as gather operations must be called.

Logging

• log_level (str, optional, defaults to passive) — Logging level for the main process. Options: "debug", "info", "warning", "error", "critical", or "passive" (doesn’t change the current Transformers logging level, which defaults to "warning")
• log_level_replica (str, optional, defaults to "warning") — Logging level for replica processes in distributed training. Same options as log_level.
• disable_tqdm (bool, optional) — Disable tqdm progress bars. Defaults to True if log_level is warning or lower, False otherwise.

Experiment Tracking Integration

• report_to (str or list[str], optional, defaults to "none") — The list of integrations to report the results and logs to. Supported platforms are "azure_ml", "clearml", "codecarbon", "comet_ml", "dagshub", "dvclive", "flyte", "mlflow", "swanlab", "tensorboard", "trackio" and "wandb". Use "all" to report to all integrations installed, "none" for no integrations.
• run_name (str, optional) — A descriptor for the run. Typically used for trackio, wandb, mlflow, comet and swanlab logging.
• project (str, optional, defaults to "huggingface") — The name of the project to use for logging. Currently, only used by Trackio.
• trackio_space_id (str or None, optional, defaults to "trackio") — The Hugging Face Space ID to deploy to when using Trackio. Should be a complete Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' in which case the Space will be created in the currently-logged-in Hugging Face user’s namespace. If None, will log to a local directory. Note that this Space will be public unless you set hub_private_repo=True or your organization’s default is to create private Spaces.”

Evaluation

• eval_strategy (str or IntervalStrategy, optional, defaults to "no") — When to run evaluation. Options:
  • "no": No evaluation during training
  • "steps": Evaluate every eval_steps
  • "epoch": Evaluate at the end of each epoch
• 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.
• eval_delay (float, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy.
• per_device_eval_batch_size (int, optional, defaults to 8) — The batch size per device accelerator core/CPU for evaluation.
• prediction_loss_only (bool, optional, defaults to False) — When performing evaluation and generating predictions, only returns the loss.
• 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_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.
• 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. This is useful when the labels structure is non standard, like in computer vision tasks.
• 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 the device accelerator before being moved to the CPU (faster but requires more memory).

Metrics Computation

• include_for_metrics (list[str], optional, defaults to []) — Include additional data in the compute_metrics function if needed for metrics computation. Possible options to add to include_for_metrics list:
  • "inputs": Input data passed to the model, intended for calculating input dependent metrics.
  • "loss": Loss values computed during evaluation, intended for calculating loss dependent metrics.
• batch_eval_metrics (bool, optional, 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.

Checkpointing & Saving

• save_only_model (bool, optional, defaults to False) — Save only model weights, not optimizer/scheduler/RNG state. Significantly reduces checkpoint size but prevents resuming training from the checkpoint. Use when you only need the trained model for inference, not continued training. You can only load the model using from_pretrained with this option set to True.
• save_strategy (str or SaveStrategy, 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.
  • "best": Save is done whenever a new best_metric is achieved.
• 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_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_total_limit (int, optional) — Maximum number of checkpoints to keep. Deletes older checkpoints in output_dir. When load_best_model_at_end=True, the best checkpoint is always retained plus the most recent ones. For example, save_total_limit=5 keeps the 4 most recent plus the best
• enable_jit_checkpoint (bool, optional, defaults to False) — Enable Just-In-Time checkpointing on SIGTERM signal for graceful termination on preemptible workloads. Important: Configure your orchestrator’s graceful shutdown period to allow sufficient time. For Kubernetes, set terminationGracePeriodSeconds (default 30s is usually insufficient). For Slurm, use --signal=USR1@<seconds>. Required grace period ≥ longest iteration time + checkpoint save time.

Hugging Face Hub Integration

• 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.
• 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 hf auth login.
• hub_private_repo (bool, optional) — Whether to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists. If reporting to Trackio with deployment to Hugging Face Spaces enabled, the same logic determines whether the Space is private.
• 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.
• hub_strategy (str or HubStrategy, optional, defaults to "every_save") — Defines what and when to push to Hub. Options:
  • "end": Push only at the end of training
  • "every_save": Push on each save (async to not block training)
  • "checkpoint": Like "every_save" plus push latest checkpoint to "last-checkpoint" subfolder for easy resuming
  • "all_checkpoints": Push all checkpoints as they appear
• 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.
• hub_revision (str, optional) — The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash.

Best Model Tracking

• load_best_model_at_end (bool, optional, defaults to False) — Load the best checkpoint at the end of training. Requires eval_strategy to be set. When enabled, the best checkpoint is always saved (see save_total_limit). When `True`, `save_strategy` must match `eval_strategy`, and if using `"steps"`, `save_steps` must be a multiple of `eval_steps`.
• metric_for_best_model (str, optional) — Metric to use for comparing models when load_best_model_at_end=True. Must be a metric name returned by evaluation, with or without the "eval_" prefix. Defaults to "loss". If you set this, greater_is_better will default to True unless the name ends with "loss". Examples: "accuracy", "f1", "eval_bleu".
• greater_is_better (bool, optional) — Whether higher metric values are better. Defaults based on metric_for_best_model: True if the metric name doesn’t end in "loss", False otherwise.

Resuming Training

• ignore_data_skip (bool, optional, defaults to False) — When resuming training, skip fast-forwarding through the dataset to reach the previous state. If True, training starts from the beginning of the dataset (faster resume but results won’t match interrupted training). If False, skips seen data (slower resume but exact continuation).
• restore_callback_states_from_checkpoint (bool, optional, defaults to False) — Restore callback states from checkpoint when resuming. If True, will override callbacks passed to Trainer if they exist in the checkpoint.

Reproducibility

• 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.
• 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.

Hardware Configuration

• use_cpu (bool, optional, defaults to False) — Whether or not to use cpu. If set to False, we will use the available torch device/backend.

Accelerate Configuration

• accelerator_config (str, dict, or AcceleratorConfig, optional) — Configuration for the internal Accelerate integration. Can be:
  • Path to JSON config file: "accelerator_config.json"
  • Dictionary with config options
  • AcceleratorConfig instance Key options:
  • split_batches (bool, defaults to False): Whether to split batches across devices. If True, actual batch size is the same on all devices (total must be divisible by num_processes). If False, each device gets the specified batch size.
  • dispatch_batches (bool): If True, only main process iterates through dataloader and dispatches batches to devices. Defaults to True for IterableDataset, False otherwise.
  • even_batches (bool, defaults to True): Duplicate samples from dataset start to ensure all workers get equal batch sizes.
  • use_seedable_sampler (bool, defaults to True): Use fully seedable random sampler for reproducibility.
  • use_configured_state (bool, defaults to False): Use pre-initialized AcceleratorState/PartialState instead of creating new one. May cause issues with hyperparameter tuning.
• parallelism_config (ParallelismConfig, optional) — Parallelism configuration for the training run. Requires Accelerate 1.10.1

Dataloader

• 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.
• 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.
• 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.
• 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. You should only specify label_names if you’re using custom label names or if your model’s forward consumes multiple label tensors (e.g., extractive QA).
• 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.

DDP (DistributedDataParallel)

• 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.
• ddp_backend (str, optional) — The backend to use for distributed training. Must be one of "nccl", "mpi", "xccl", "gloo", "hccl".
• 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 to the PyTorch documentation for more information.

FSDP (Fully Sharded Data Parallel)

• fsdp (bool, str or list of FSDPOption, optional, defaults to None) — Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Options:
  • "full_shard": Shard parameters, gradients, and optimizer states (most memory efficient)
  • "shard_grad_op": Shard only optimizer states and gradients (ZeRO-2)
  • "hybrid_shard": Full shard within nodes, replicate across nodes
  • "hybrid_shard_zero2": Shard gradients/optimizer within nodes, replicate across nodes
  • "offload": Offload parameters and gradients to CPU (only with "full_shard" or "shard_grad_op")
  • "auto_wrap": Automatically wrap layers 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:

  • fsdp_version (int, optional, defaults to 1): The version of FSDP to use. Defaults to 1.

  • 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 parameters. 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

• deepspeed (str or dict, optional) — Enable DeepSpeed integration. Value is either:
  • Path to DeepSpeed JSON config file: "ds_config.json"
  • Loaded config as dictionary

    If using ZeRO initialization, instantiate your model after initializing TrainingArguments, otherwise ZeRO won’t be applied.

Debugging & Profiling (Experimental)

• 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
• 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.

External Script Flags (not used by Trainer)

• 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.
• 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.

Parameters

• output_dir (str, optional, defaults to "trainer_output") — The output directory where the model predictions and checkpoints will be written.

Training Duration and Batch Size

• per_device_train_batch_size (int, optional, defaults to 8) — The batch size per device. The global batch size is computed as: per_device_train_batch_size * number_of_devices in multi-GPU or distributed setups.
• 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) — Overrides num_train_epochs. If set to a positive number, the total number of training steps to perform. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until max_steps is reached.

Learning Rate & Scheduler

• learning_rate (float, optional, defaults to 5e-5) — The initial learning rate for the optimizer. This is typically the peak learning rate when using a scheduler with warmup.
• lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") — The learning rate scheduler type to use. See SchedulerType for all possible values. Common choices:
  • “linear” = get_linear_schedule_with_warmup()
  • “cosine” = get_cosine_schedule_with_warmup()
  • “constant” = get_constant_schedule()
  • “constant_with_warmup” = get_constant_schedule_with_warmup()
• lr_scheduler_kwargs (dict or str, optional, defaults to None) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
• warmup_steps (int or float, optional, defaults to 0) — Number of steps for a linear warmup from 0 to learning_rate. Warmup helps stabilize training in the initial phase. Can be:
  • An integer: exact number of warmup steps
  • A float in range [0, 1): interpreted as ratio of total training steps

Optimizer

• optim (str or training_args.OptimizerNames, optional, defaults to "adamw_torch" (for torch>=2.8 "adamw_torch_fused")) — The optimizer to use. Common options:
  • "adamw_torch": PyTorch’s AdamW (recommended default)
  • "adamw_torch_fused": Fused AdamW kernel
  • "adamw_hf": HuggingFace’s AdamW implementation
  • "sgd": Stochastic Gradient Descent with momentum
  • "adafactor": Memory-efficient optimizer for large models
  • "adamw_8bit": 8-bit AdamW (requires bitsandbytes) See OptimizerNames for the complete list.
• optim_args (str, optional) — Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
• weight_decay (float, optional, defaults to 0) — Weight decay coefficient applied by the optimizer (not the loss function). Adds L2 regularization to prevent overfitting by penalizing large weights. Automatically excluded from bias and LayerNorm parameters. Typical values: 0.01 (standard), 0.1 (stronger regularization), 0.0 (no regularization).
• adam_beta1 (float, optional, defaults to 0.9) — The exponential decay rate for the first moment estimates (momentum) in Adam-based optimizers. Controls how much history of gradients to retain.
• adam_beta2 (float, optional, defaults to 0.999) — The exponential decay rate for the second moment estimates (variance) in Adam-based optimizers. Controls adaptive learning rate scaling.
• adam_epsilon (float, optional, defaults to 1e-8) — Epsilon value for numerical stability in Adam-based optimizers. Prevents division by zero in the denominator of the update rule.
• optim_target_modules (Union[str, list[str]], optional) — The target modules to optimize, i.e. the module names that you would like to train. Currently used for the GaLore algorithm and APOLLO algorithm. See GaLore implementation and APOLLO implementation for more details. You need to make sure to pass a valid GaLore or APOLLO optimizer, e.g., one of: “apollo_adamw”, “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are nn.Linear modules only.

Regularization & Training Stability

• gradient_accumulation_steps (int, optional, defaults to 1) — Number of update steps to accumulate gradients before performing a backward/update pass. Simulates larger batch sizes without additional memory. Effective batch size = per_device_train_batch_size × num_devices × gradient_accumulation_steps.

  When using gradient accumulation, one “step” is counted as one step with a backward pass. Therefore, logging, evaluation, and saving will occur every gradient_accumulation_steps × xxx_step training examples.

• average_tokens_across_devices (bool, optional, defaults to True) — Whether or not to average tokens across devices. If enabled, will use all_reduce to synchronize num_tokens_in_batch for precise loss calculation. Reference: https://github.com/huggingface/transformers/issues/34242
• max_grad_norm (float, optional, defaults to 1.0) — Maximum gradient norm for gradient clipping. Applied after backward pass, before optimizer step. Prevents gradient explosion by scaling down gradients when their global norm exceeds this threshold. Set to 0 to disable clipping. Typical values: 1.0 (standard), 0.5 (more conservative), 5.0 (less aggressive).
• label_smoothing_factor (float, optional, defaults to 0.0) — Label smoothing factor to prevent overconfidence. Replaces hard 0/1 targets with soft targets: 0 becomes ε/num_labels and 1 becomes 1 - ε + ε/num_labels, where ε = label_smoothing_factor. Zero means no smoothing. Typical range: 0.0 to 0.1.

Mixed Precision Training

• bf16 (bool, optional, defaults to False) — Enable bfloat16 (BF16) mixed precision training Generally preferred over FP16 due to better numerical stability and no loss scaling required.
• fp16 (bool, optional, defaults to False) — Enable float16 (FP16) mixed precision training. Consider using BF16 instead if your hardware supports it.
• bf16_full_eval (bool, optional, defaults to False) — Use full BF16 precision for evaluation (not just mixed precision). Faster and saves memory but may affect metric values slightly. Only applies during evaluation.
• fp16_full_eval (bool, optional, defaults to False) — Use full FP16 precision for evaluation (not just mixed precision). Faster and saves memory but may affect metric values slightly. Only applies during evaluation.
• tf32 (bool, optional) — Enable TensorFloat-32 (TF32) mode on Ampere and newer GPUs. TF32 uses 19-bit precision for matrix multiplications (instead of FP32’s 23-bit), providing up to 8x speedup with negligible accuracy loss. Default depends on PyTorch version. See TF32 docs.

Gradient Checkpointing

• gradient_checkpointing (bool, optional, defaults to False) — Enable gradient checkpointing to trade compute for memory. Reduces memory usage by clearing activations during forward pass and recomputing them during backward pass. Enables training larger models or batch sizes at the cost of ~20% slower training.
• gradient_checkpointing_kwargs (dict, optional, defaults to None) — Keyword arguments passed to gradient_checkpointing_enable().

Compilation

• torch_compile (bool, optional, defaults to False) — Compile the model using PyTorch 2.0’s torch.compile() for faster training. Can provide 20-50% speedup with no code changes. Uses default compilation settings unless torch_compile_backend or torch_compile_mode are specified.
• torch_compile_backend (str, optional) — Backend for torch.compile(). If set, automatically enables torch_compile. Options include "inductor" (default), "aot_eager", "cudagraphs". Backends vary by PyTorch version - see PyTorch docs for available options.
• torch_compile_mode (str, optional) — Compilation mode for torch.compile(). If set, automatically enables torch_compile. Options: "default", "reduce-overhead" (minimize Python overhead), "max-autotune" (aggressive optimization, slower compile time).

Kernels

• use_liger_kernel (bool, optional, defaults to False) — Enable Liger Kernel optimizations. Increases multi-GPU throughput by ~20% and reduces memory usage by ~60%. Works with Flash Attention, FSDP, and DeepSpeed. Currently supports Llama, Mistral, Mixtral, and Gemma models.
• liger_kernel_config (Optional[dict], optional) — Configuration for Liger Kernel. Passed as kwargs to _apply_liger_kernel_to_instance(). Options typically include: "rope", "swiglu", "cross_entropy", "fused_linear_cross_entropy", "rms_norm". If None, uses default configuration.

Additional Optimizations

• use_cache (bool, optional, defaults to False) — Whether or not to enable cache for the model. For training, this is usually not needed apart from some PEFT methods that uses past_key_values.
• 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].
• 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.
• 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.

Logging & Monitoring Training

• 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_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_first_step (bool, optional, defaults to False) — Whether to log the first global_step or not.
• 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_nan_inf_filter (bool, optional, defaults to True) — Filter out NaN and Inf losses when logging. If True, replaces NaN/Inf losses with the average of recent valid losses. Does not affect gradient computation, only logging.
• include_num_input_tokens_seen (Optional[Union[str, bool]], optional, defaults to “no”) — Whether to track the number of input tokens seen. Must be one of [“all”, “non_padding”, “no”] or a boolean value which map to “all” or “no”. May be slower in distributed training as gather operations must be called.

Logging

• log_level (str, optional, defaults to passive) — Logging level for the main process. Options: "debug", "info", "warning", "error", "critical", or "passive" (doesn’t change the current Transformers logging level, which defaults to "warning")
• log_level_replica (str, optional, defaults to "warning") — Logging level for replica processes in distributed training. Same options as log_level.
• disable_tqdm (bool, optional) — Disable tqdm progress bars. Defaults to True if log_level is warning or lower, False otherwise.

Experiment Tracking Integration

• report_to (str or list[str], optional, defaults to "none") — The list of integrations to report the results and logs to. Supported platforms are "azure_ml", "clearml", "codecarbon", "comet_ml", "dagshub", "dvclive", "flyte", "mlflow", "swanlab", "tensorboard", "trackio" and "wandb". Use "all" to report to all integrations installed, "none" for no integrations.
• run_name (str, optional) — A descriptor for the run. Typically used for trackio, wandb, mlflow, comet and swanlab logging.
• project (str, optional, defaults to "huggingface") — The name of the project to use for logging. Currently, only used by Trackio.
• trackio_space_id (str or None, optional, defaults to "trackio") — The Hugging Face Space ID to deploy to when using Trackio. Should be a complete Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' in which case the Space will be created in the currently-logged-in Hugging Face user’s namespace. If None, will log to a local directory. Note that this Space will be public unless you set hub_private_repo=True or your organization’s default is to create private Spaces.”

Evaluation

• eval_strategy (str or IntervalStrategy, optional, defaults to "no") — When to run evaluation. Options:
  • "no": No evaluation during training
  • "steps": Evaluate every eval_steps
  • "epoch": Evaluate at the end of each epoch
• 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.
• eval_delay (float, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the eval_strategy.
• per_device_eval_batch_size (int, optional, defaults to 8) — The batch size per device accelerator core/CPU for evaluation.
• prediction_loss_only (bool, optional, defaults to False) — When performing evaluation and generating predictions, only returns the loss.
• 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_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.
• 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. This is useful when the labels structure is non standard, like in computer vision tasks.
• 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 the device accelerator before being moved to the CPU (faster but requires more memory).

Metrics Computation

• include_for_metrics (list[str], optional, defaults to []) — Include additional data in the compute_metrics function if needed for metrics computation. Possible options to add to include_for_metrics list:
  • "inputs": Input data passed to the model, intended for calculating input dependent metrics.
  • "loss": Loss values computed during evaluation, intended for calculating loss dependent metrics.
• batch_eval_metrics (bool, optional, 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.

Checkpointing & Saving

• save_only_model (bool, optional, defaults to False) — Save only model weights, not optimizer/scheduler/RNG state. Significantly reduces checkpoint size but prevents resuming training from the checkpoint. Use when you only need the trained model for inference, not continued training. You can only load the model using from_pretrained with this option set to True.
• save_strategy (str or SaveStrategy, 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.
  • "best": Save is done whenever a new best_metric is achieved.
• 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_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_total_limit (int, optional) — Maximum number of checkpoints to keep. Deletes older checkpoints in output_dir. When load_best_model_at_end=True, the best checkpoint is always retained plus the most recent ones. For example, save_total_limit=5 keeps the 4 most recent plus the best
• enable_jit_checkpoint (bool, optional, defaults to False) — Enable Just-In-Time checkpointing on SIGTERM signal for graceful termination on preemptible workloads. Important: Configure your orchestrator’s graceful shutdown period to allow sufficient time. For Kubernetes, set terminationGracePeriodSeconds (default 30s is usually insufficient). For Slurm, use --signal=USR1@<seconds>. Required grace period ≥ longest iteration time + checkpoint save time.

Hugging Face Hub Integration

• 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.
• 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 hf auth login.
• hub_private_repo (bool, optional) — Whether to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists. If reporting to Trackio with deployment to Hugging Face Spaces enabled, the same logic determines whether the Space is private.
• 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.
• hub_strategy (str or HubStrategy, optional, defaults to "every_save") — Defines what and when to push to Hub. Options:
  • "end": Push only at the end of training
  • "every_save": Push on each save (async to not block training)
  • "checkpoint": Like "every_save" plus push latest checkpoint to "last-checkpoint" subfolder for easy resuming
  • "all_checkpoints": Push all checkpoints as they appear
• 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.
• hub_revision (str, optional) — The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash.

Best Model Tracking

• load_best_model_at_end (bool, optional, defaults to False) — Load the best checkpoint at the end of training. Requires eval_strategy to be set. When enabled, the best checkpoint is always saved (see save_total_limit). When `True`, `save_strategy` must match `eval_strategy`, and if using `"steps"`, `save_steps` must be a multiple of `eval_steps`.
• metric_for_best_model (str, optional) — Metric to use for comparing models when load_best_model_at_end=True. Must be a metric name returned by evaluation, with or without the "eval_" prefix. Defaults to "loss". If you set this, greater_is_better will default to True unless the name ends with "loss". Examples: "accuracy", "f1", "eval_bleu".
• greater_is_better (bool, optional) — Whether higher metric values are better. Defaults based on metric_for_best_model: True if the metric name doesn’t end in "loss", False otherwise.

Resuming Training

• ignore_data_skip (bool, optional, defaults to False) — When resuming training, skip fast-forwarding through the dataset to reach the previous state. If True, training starts from the beginning of the dataset (faster resume but results won’t match interrupted training). If False, skips seen data (slower resume but exact continuation).
• restore_callback_states_from_checkpoint (bool, optional, defaults to False) — Restore callback states from checkpoint when resuming. If True, will override callbacks passed to Trainer if they exist in the checkpoint.

Reproducibility

• 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.
• 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.

Hardware Configuration

• use_cpu (bool, optional, defaults to False) — Whether or not to use cpu. If set to False, we will use the available torch device/backend.

Accelerate Configuration

• accelerator_config (str, dict, or AcceleratorConfig, optional) — Configuration for the internal Accelerate integration. Can be:
  • Path to JSON config file: "accelerator_config.json"
  • Dictionary with config options
  • AcceleratorConfig instance Key options:
  • split_batches (bool, defaults to False): Whether to split batches across devices. If True, actual batch size is the same on all devices (total must be divisible by num_processes). If False, each device gets the specified batch size.
  • dispatch_batches (bool): If True, only main process iterates through dataloader and dispatches batches to devices. Defaults to True for IterableDataset, False otherwise.
  • even_batches (bool, defaults to True): Duplicate samples from dataset start to ensure all workers get equal batch sizes.
  • use_seedable_sampler (bool, defaults to True): Use fully seedable random sampler for reproducibility.
  • use_configured_state (bool, defaults to False): Use pre-initialized AcceleratorState/PartialState instead of creating new one. May cause issues with hyperparameter tuning.
• parallelism_config (ParallelismConfig, optional) — Parallelism configuration for the training run. Requires Accelerate 1.10.1