Transformers documentation
Trainer
The Trainer class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for NVIDIA GPUs, AMD GPUs, and torch.amp for PyTorch. Trainer goes hand-in-hand with the TrainingArguments class, which offers a wide range of options to customize how a model is trained. Together, these two classes provide a complete training API.
Seq2SeqTrainer and Seq2SeqTrainingArguments inherit from the Trainer and TrainingArguments classes and they’re adapted for training models for sequence-to-sequence tasks such as summarization or translation.
The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when used with other models. When using it with your own model, make sure:
- your model always return tuples or subclasses of ModelOutput
- your model can compute the loss if a
labelsargument is provided and that loss is returned as the first element of the tuple (if your model returns tuples)- your model can accept multiple label arguments (use
label_namesin TrainingArguments to indicate their name to the Trainer) but none of them should be named"label"
( model: transformers.modeling_utils.PreTrainedModel | torch.nn.modules.module.Module | None = None args: transformers.training_args.TrainingArguments | None = None data_collator: collections.abc.Callable[[list[typing.Any]], dict[str, typing.Any]] | None = None train_dataset: Dataset | IterableDataset | datasets.Dataset | None = None eval_dataset: Dataset | dict[str, Dataset] | datasets.Dataset | None = None processing_class: transformers.tokenization_utils_base.PreTrainedTokenizerBase | transformers.image_processing_utils.BaseImageProcessor | transformers.feature_extraction_utils.FeatureExtractionMixin | transformers.processing_utils.ProcessorMixin | None = None model_init: collections.abc.Callable[..., transformers.modeling_utils.PreTrainedModel] | None = None compute_loss_func: collections.abc.Callable | None = None compute_metrics: collections.abc.Callable[[transformers.trainer_utils.EvalPrediction], dict] | None = None callbacks: list[transformers.trainer_callback.TrainerCallback] | None = None optimizers: tuple = (None, None) optimizer_cls_and_kwargs: tuple[type[torch.optim.optimizer.Optimizer], dict[str, typing.Any]] | None = None preprocess_logits_for_metrics: collections.abc.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None )
Parameters
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.
output_dir set to a directory named tmp_trainer in the current directory if not provided. 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. 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. 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. 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. 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 hyperparameters (such as layer count, sizes of
inner layers, dropout probabilities etc). 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. 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 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. 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.
Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.
Important attributes:
DeepSpeed,
the inner model is wrapped in DeepSpeed and then again in torch.nn.DistributedDataParallel. If the inner
model hasn’t been wrapped, then self.model_wrapped is the same as self.model.True unless model parallel, DeepSpeed, FSDP, full fp16/bf16 eval, or SageMaker MP is active. Can be
overridden by subclassing TrainingArguments and overriding the place_model_on_device property.train (e.g. when evaluate is called while
in train)( callback: type[transformers.trainer_callback.TrainerCallback] | transformers.trainer_callback.TrainerCallback )
Parameters
type or [`~transformers.TrainerCallback]`) —
A TrainerCallback class or an instance of a TrainerCallback. In the
first case, will instantiate a member of that class. Add a callback to the current list of TrainerCallback.
A helper wrapper that creates an appropriate context manager for autocast while feeding it the desired
arguments, depending on the situation. We rely on accelerate for autocast, hence we do nothing here.
Invoke model_init to get a fresh model instance, optionally conditioned on a hyperparameter trial.
( model: Module inputs: dict return_outputs: bool = False num_items_in_batch: torch.Tensor | int | None = None )
Parameters
nn.Module) —
The model to compute the loss for. dict[str, torch.Tensor | Any]) —
The input data for the model. bool, optional, defaults to False) —
Whether to return the model outputs along with the loss. How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior. If you are not using num_items_in_batch when computing your loss,
make sure to overwrite self.model_accepts_loss_kwargs to False. Otherwise, the loss calculation might be slightly inaccurate when performing gradient accumulation.
A helper wrapper to group together context managers.
Create the accelerator and perform post-creation setup (FSDP, DeepSpeed, etc.).
( language: str | None = None license: str | None = None tags: str | list[str] | None = None model_name: str | None = None finetuned_from: str | None = None tasks: str | list[str] | None = None dataset_tags: str | list[str] | None = None dataset: str | list[str] | None = None dataset_args: str | list[str] | None = None )
Parameters
str, optional) —
The language of the model (if applicable) str, optional) —
The license of the model. Will default to the license of the pretrained model used, if the original
model given to the Trainer comes from a repo on the Hub. str or list[str], optional) —
Some tags to be included in the metadata of the model card. str, optional) —
The name of the model. str, optional) —
The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
of the original model given to the Trainer (if it comes from the Hub). str or list[str], optional) —
One or several task identifiers, to be included in the metadata of the model card. str or list[str], optional) —
One or several dataset tags, to be included in the metadata of the model card. str or list[str], optional) —
One or several dataset identifiers, to be included in the metadata of the model card. str or list[str], optional) —
One or several dataset arguments, to be included in the metadata of the model card. Creates a draft of a model card using the information available to the Trainer.
( model = None ) → torch.optim.Optimizer
Returns
torch.optim.Optimizer
The optimizer instance.
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through optimizers, or subclass and override this method in a subclass.
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through optimizers, or subclass and override this method (or create_optimizer and/or
create_scheduler) in a subclass.
( num_training_steps: int optimizer: torch.optim.optimizer.Optimizer | None = None ) → torch.optim.lr_scheduler.LRScheduler
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.
( eval_dataset: torch.utils.data.dataset.Dataset | dict[str, torch.utils.data.dataset.Dataset] | None = None ignore_keys: list[str] | None = None metric_key_prefix: str = 'eval' )
Parameters
Dataset | dict[str, Dataset], optional) —
Pass a dataset if you wish to override self.eval_dataset. 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. Datasets must implement the
__len__ method.
If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run
separate evaluations on each dataset. This can be useful to monitor how training affects other
datasets or simply to get a more fine-grained evaluation.
When used with load_best_model_at_end, make sure metric_for_best_model references exactly one
of the datasets. If you, for example, pass in {"data1": data1, "data2": data2} for two datasets
data1 and data2, you could specify metric_for_best_model="eval_data1_loss" for using the
loss on data1 and metric_for_best_model="eval_data2_loss" for the loss on data2.
list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions. str, optional, defaults to "eval") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is “eval” (default) Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init compute_metrics argument).
You can also subclass and override this method to inject custom behavior.
( dataloader: DataLoader description: str prediction_loss_only: bool | None = None ignore_keys: list[str] | None = None metric_key_prefix: str = 'eval' )
Prediction/evaluation loop, shared by Trainer.evaluate() and Trainer.predict().
Works both with or without labels.
( inputs: dict ) → int
For models that inherit from PreTrainedModel, uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method.
Collects a specified number of batches from the epoch iterator and optionally counts the number of items in the batches to properly scale the loss.
Get the context parallel size
Get all parameter names that weight decay will be applied to.
This function filters out parameters in two ways:
( eval_dataset: str | torch.utils.data.dataset.Dataset | None = None )
Parameters
str or torch.utils.data.Dataset, optional) —
If a str, will use self.eval_dataset[eval_dataset] as the evaluation dataset. If a Dataset, will override self.eval_dataset and must implement __len__. If it is a Dataset, columns not accepted by the model.forward() method are automatically removed. Returns the evaluation ~torch.utils.data.DataLoader.
Subclass and override this method if you want to inject some custom behavior.
Returns the learning rate of each parameter from self.optimizer.
Get the number of trainable parameters.
( args: TrainingArguments model: transformers.modeling_utils.PreTrainedModel | None = None )
Returns the optimizer class and optimizer parameters based on the training arguments.
( param: str | torch.nn.parameter.Parameter | None = None )
Returns optimizer group for a parameter if given, else returns all optimizer groups for params.
Get the sequence parallel size
( test_dataset: Dataset )
Returns the test ~torch.utils.data.DataLoader.
Subclass and override this method if you want to inject some custom behavior.
Calculates total batch size (micro_batch grad_accum dp_world_size).
Accounts for all parallelism dimensions: TP, CP, and SP.
Formula: dp_world_size = world_size // (tp_size cp_size sp_size)
Where:
All dimensions are separate and multiplicative: world_size = dp_size tp_size cp_size * sp_size
Get the tensor parallel size from either the model or DeepSpeed config.
Returns the training ~torch.utils.data.DataLoader.
Will use no sampler if train_dataset does not implement __len__, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this method if you want to inject some custom behavior.
( hp_space: collections.abc.Callable[['optuna.Trial'], dict[str, float]] | None = None compute_objective: collections.abc.Callable[[dict[str, float]], float] | None = None n_trials: int = 20 direction: str | list[str] = 'minimize' backend: str | transformers.trainer_utils.HPSearchBackend | None = None hp_name: collections.abc.Callable[['optuna.Trial'], str] | None = None **kwargs ) → [trainer_utils.BestRun or list[trainer_utils.BestRun]]
Parameters
Callable[["optuna.Trial"], dict[str, float]], optional) —
A function that defines the hyperparameter search space. Will default to
default_hp_space_optuna() or default_hp_space_ray()
depending on your backend. Callable[[dict[str, float]], float], optional) —
A function computing the objective to minimize or maximize from the metrics returned by the evaluate
method. Will default to default_compute_objective(). int, optional, defaults to 100) —
The number of trial runs to test. str or list[str], optional, defaults to "minimize") —
If it’s single objective optimization, direction is str, can be "minimize" or "maximize", you
should pick "minimize" when optimizing the validation loss, "maximize" when optimizing one or
several metrics. If it’s multi objectives optimization, direction is list[str], can be List of
"minimize" and "maximize", you should pick "minimize" when optimizing the validation loss,
"maximize" when optimizing one or several metrics. str or ~training_utils.HPSearchBackend, optional) —
The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending
on which one is installed. If all are installed, will default to optuna. Callable[["optuna.Trial"], str]], optional) —
A function that defines the trial/run name. Will default to None. dict[str, Any], optional) —
Additional keyword arguments for each backend:
optuna: parameters from
optuna.study.create_study
and also the parameters timeout, n_jobs and gc_after_trial from
optuna.study.Study.optimizeray: parameters from tune.run.
If resources_per_trial is not set in the kwargs, it defaults to 1 CPU core and 1 GPU (if available).
If progress_reporter is not set in the kwargs,
ray.tune.CLIReporter is used.Returns
[trainer_utils.BestRun or list[trainer_utils.BestRun]]
All the information about the best run or best
runs for multi-objective optimization. Experiment summary can be found in run_summary attribute for Ray
backend.
Launch a hyperparameter search using optuna or Ray Tune. The optimized quantity is determined
by compute_objective, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.
To use this method, you need to have provided a
model_initwhen initializing your Trainer: we need to reinitialize the model at each new run. This is incompatible with theoptimizersargument, so you need to subclass Trainer and override the method create_optimizer_and_scheduler() for custom optimizer/scheduler.
Initializes a git repo in self.args.hub_model_id.
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process.
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be True for one process).
( logs: dict start_time: float | None = None )
Log logs on the various objects watching training.
Subclass and override this method to inject custom behavior.
( split metrics )
Log metrics in a specially formatted way.
Under distributed environment this is done only for a process with rank 0.
Notes on memory reports:
In order to get memory usage report you need to install psutil. You can do that with pip install psutil.
Now when this method is run, you will see a report that will include:
init_mem_cpu_alloc_delta = 1301MB
init_mem_cpu_peaked_delta = 154MB
init_mem_gpu_alloc_delta = 230MB
init_mem_gpu_peaked_delta = 0MB
train_mem_cpu_alloc_delta = 1345MB
train_mem_cpu_peaked_delta = 0MB
train_mem_gpu_alloc_delta = 693MB
train_mem_gpu_peaked_delta = 7MBUnderstanding the reports:
train__, tells you which stage the metrics are for. Reports starting with init_
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
__init__ will be reported along with the eval_ metrics.cpu or gpu, tells you whether it’s the general RAM or the gpu0 memory
metric.*_alloc_delta - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.*_peaked_delta - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up alloc_delta +
peaked_delta and you know how much memory was needed to complete that stage.The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more memory than the rest since it stores the gradient and optimizer states for all participating GPUs. Perhaps in the future these reports will evolve to measure those too.
The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the memory shared with other processes. It is important to note that it does not include swapped out memory, so the reports could be imprecise.
The CPU peak memory is measured using a sampling thread. Due to python’s GIL it may miss some of the peak memory if
that thread didn’t get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using tracemalloc would have reported the exact peak memory, but it doesn’t report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won’t be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage.
The GPU allocated and peak memory reporting is done with torch.cuda.memory_allocated() and
torch.cuda.max_memory_allocated(). This metric reports only “deltas” for pytorch-specific allocations, as
torch.cuda memory management system doesn’t track any memory allocated outside of pytorch. For example, the very
first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.
Note that this tracker doesn’t account for memory allocations outside of Trainer’s __init__, train,
evaluate and predict calls.
Because evaluation calls may happen during train, we can’t handle nested invocations because
torch.cuda.max_memory_allocated is a single counter, so if it gets reset by a nested eval call, train’s tracker
will report incorrect info. If this pytorch issue gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
train, evaluate and predict methods. Which means that if eval is called during train, it’s the latter
that will account for its memory usage and that of the former.
This also means that if any other tool that is used along the Trainer calls
torch.cuda.reset_peak_memory_stats, the gpu peak memory stats could be invalid. And the Trainer will disrupt
the normal behavior of any such tools that rely on calling torch.cuda.reset_peak_memory_stats themselves.
For best performance you may want to consider turning the memory profiling off for production runs.
( metrics: dict ) → metrics (dict[str, float])
Reformat Trainer metrics values to a human-readable format.
Helper to get number of samples in a ~torch.utils.data.DataLoader by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can
( callback: type[transformers.trainer_callback.TrainerCallback] | transformers.trainer_callback.TrainerCallback ) → TrainerCallback
Parameters
type or [`~transformers.TrainerCallback]`) —
A TrainerCallback class or an instance of a TrainerCallback. In the
first case, will pop the first member of that class found in the list of callbacks. Returns
The callback removed, if found.
Remove a callback from the current list of TrainerCallback and returns it.
If the callback is not found, returns None (and no error is raised).
( test_dataset: Dataset ignore_keys: list[str] | None = None metric_key_prefix: str = 'test' )
Parameters
Dataset) —
Dataset to run the predictions on. If it is an datasets.Dataset, columns not accepted by the
model.forward() method are automatically removed. Has to implement the method __len__ list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions. str, optional, defaults to "test") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“test_bleu” if the prefix is “test” (default) Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in evaluate().
If your predictions or labels have different sequence length (for instance because you’re doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.
Returns: NamedTuple A namedtuple with the following keys:
np.ndarray): The predictions on test_dataset.np.ndarray, optional): The labels (if the dataset contained some).dict[str, float], optional): The potential dictionary of metrics (if the dataset contained
labels).( model: Module inputs: dict prediction_loss_only: bool ignore_keys: list[str] | None = None ) → tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]
Parameters
nn.Module) —
The model to evaluate. dict[str, torch.Tensor | Any]) —
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument labels. Check your model’s documentation for all accepted arguments.
bool) —
Whether or not to return the loss only. list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions. Returns
tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]
A tuple with the loss, logits and labels (each being optional).
Perform an evaluation step on model using inputs.
Subclass and override to inject custom behavior.
( commit_message: str | None = 'End of training' blocking: bool = True token: str | None = None revision: str | None = None **kwargs )
Parameters
str, optional, defaults to "End of training") —
Message to commit while pushing. bool, optional, defaults to True) —
Whether the function should return only when the git push has finished. str, optional, defaults to None) —
Token with write permission to overwrite Trainer’s original args. str, optional) —
The git revision to commit from. Defaults to the head of the “main” branch. dict[str, Any], optional) —
Additional keyword arguments passed along to create_model_card(). Upload self.model and self.processing_class to the 🤗 model hub on the repo self.args.hub_model_id.
( callback: type[transformers.trainer_callback.TrainerCallback] | transformers.trainer_callback.TrainerCallback )
Parameters
type or [`~transformers.TrainerCallback]`) —
A TrainerCallback class or an instance of a TrainerCallback. In the
first case, will remove the first member of that class found in the list of callbacks. Remove a callback from the current list of TrainerCallback.
( split metrics combined = True )
Save metrics into a json file for that split, e.g. train_results.json.
Under distributed environment this is done only for a process with rank 0.
To understand the metrics please read the docstring of log_metrics(). The only difference is that raw unformatted numbers are saved in the current method.
Will save the model, so you can reload it using from_pretrained().
Will only save from the main process.
Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model.
Under distributed environment this is done only for a process with rank 0.
Calculates and returns the following values:
num_train_epochsnum_update_steps_per_epochnum_examplesnum_train_samplestotal_train_batch_sizesteps_in_epoch (total batches per epoch)max_stepsStore the number of floating-point operations that went into the model.
( resume_from_checkpoint: str | bool | None = None trial: optuna.Trial | dict[str, Any] | None = None ignore_keys_for_eval: list[str] | None = None ) → TrainOutput
Parameters
str or bool, optional) —
If a str, local path to a saved checkpoint as saved by a previous instance of Trainer. If a
bool and equals True, load the last checkpoint in args.output_dir as saved by a previous instance
of Trainer. If present, training will resume from the model/optimizer/scheduler states loaded here. optuna.Trial or dict[str, Any], optional) —
The trial run or the hyperparameter dictionary for hyperparameter search. list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions for evaluation during the training. Returns
TrainOutput
Object containing the global step count, training loss, and metrics.
Main training entry point.
( model: Module inputs: dict num_items_in_batch: torch.Tensor | int | None = None ) → torch.Tensor
Parameters
nn.Module) —
The model to train. dict[str, torch.Tensor | Any]) —
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument labels. Check your model’s documentation for all accepted arguments.
Returns
torch.Tensor
The tensor with training loss on this batch.
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
( model: typing.Union[ForwardRef('PreTrainedModel'), torch.nn.modules.module.Module, NoneType] = None args: typing.Optional[ForwardRef('TrainingArguments')] = None data_collator: typing.Optional[ForwardRef('DataCollator')] = None train_dataset: typing.Union[torch.utils.data.dataset.Dataset, ForwardRef('IterableDataset'), ForwardRef('datasets.Dataset'), NoneType] = None eval_dataset: torch.utils.data.dataset.Dataset | dict[str, torch.utils.data.dataset.Dataset] | None = None processing_class: typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('BaseImageProcessor'), ForwardRef('FeatureExtractionMixin'), ForwardRef('ProcessorMixin'), NoneType] = None model_init: collections.abc.Callable[[], 'PreTrainedModel'] | None = None compute_loss_func: collections.abc.Callable | None = None compute_metrics: collections.abc.Callable[['EvalPrediction'], dict] | None = None callbacks: list['TrainerCallback'] | None = None optimizers: tuple = (None, None) preprocess_logits_for_metrics: collections.abc.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None )
( eval_dataset: torch.utils.data.dataset.Dataset | None = None ignore_keys: list[str] | None = None metric_key_prefix: str = 'eval' **gen_kwargs )
Parameters
Dataset, optional) —
Pass a dataset if you wish to override self.eval_dataset. If it is an Dataset, columns
not accepted by the model.forward() method are automatically removed. It must implement the __len__
method. list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions. str, optional, defaults to "eval") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is "eval" (default) int, optional) —
The maximum target length to use when predicting with the generate method. int, optional) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search. generate specific kwargs. Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init compute_metrics argument).
You can also subclass and override this method to inject custom behavior.
( test_dataset: Dataset ignore_keys: list[str] | None = None metric_key_prefix: str = 'test' **gen_kwargs )
Parameters
Dataset) —
Dataset to run the predictions on. If it is a Dataset, columns not accepted by the
model.forward() method are automatically removed. Has to implement the method __len__ list[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions. str, optional, defaults to "eval") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is "eval" (default) int, optional) —
The maximum target length to use when predicting with the generate method. int, optional) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search. generate specific kwargs. Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in evaluate().
If your predictions or labels have different sequence lengths (for instance because you’re doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.
Returns: NamedTuple A namedtuple with the following keys:
np.ndarray): The predictions on test_dataset.np.ndarray, optional): The labels (if the dataset contained some).dict[str, float], optional): The potential dictionary of metrics (if the dataset contained
labels).( output_dir: str | None = None per_device_train_batch_size: int = 8 num_train_epochs: float = 3.0 max_steps: int = -1 learning_rate: float = 5e-05 lr_scheduler_type: transformers.trainer_utils.SchedulerType | str = 'linear' lr_scheduler_kwargs: dict | str | None = None warmup_steps: float = 0 optim: transformers.training_args.OptimizerNames | str = 'adamw_torch_fused' optim_args: str | None = None weight_decay: float = 0.0 adam_beta1: float = 0.9 adam_beta2: float = 0.999 adam_epsilon: float = 1e-08 optim_target_modules: None | str | list[str] = None gradient_accumulation_steps: int = 1 average_tokens_across_devices: bool = True max_grad_norm: float = 1.0 label_smoothing_factor: float = 0.0 bf16: bool = False fp16: bool = False bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: bool | None = None gradient_checkpointing: bool = False gradient_checkpointing_kwargs: dict[str, typing.Any] | str | None = None torch_compile: bool = False torch_compile_backend: str | None = None torch_compile_mode: str | None = None use_liger_kernel: bool = False liger_kernel_config: dict[str, bool] | None = None use_cache: bool = False neftune_noise_alpha: float | None = None torch_empty_cache_steps: int | None = None auto_find_batch_size: bool = False logging_strategy: transformers.trainer_utils.IntervalStrategy | str = 'steps' logging_steps: float = 500 logging_first_step: bool = False log_on_each_node: bool = True logging_nan_inf_filter: bool = True include_num_input_tokens_seen: str | bool = 'no' log_level: str = 'passive' log_level_replica: str = 'warning' disable_tqdm: bool | None = None report_to: None | str | list[str] = 'none' run_name: str | None = None project: str = 'huggingface' trackio_space_id: str | None = None trackio_bucket_id: str | None = None trackio_static_space_id: typing.Union[str, NoneType, typing.Literal[False]] = None eval_strategy: transformers.trainer_utils.IntervalStrategy | str = 'no' eval_steps: float | None = None eval_delay: float = 0 per_device_eval_batch_size: int = 8 prediction_loss_only: bool = False eval_on_start: bool = False eval_do_concat_batches: bool = True eval_use_gather_object: bool = False eval_accumulation_steps: int | None = None include_for_metrics: list = <factory> batch_eval_metrics: bool = False save_only_model: bool = False save_strategy: transformers.trainer_utils.SaveStrategy | str = 'steps' save_steps: float = 500 save_on_each_node: bool = False save_total_limit: int | None = None enable_jit_checkpoint: bool = False push_to_hub: bool = False hub_token: str | None = None hub_private_repo: bool | None = None hub_model_id: str | None = None hub_strategy: transformers.trainer_utils.HubStrategy | str = 'every_save' hub_always_push: bool = False hub_revision: str | None = None load_best_model_at_end: bool = False metric_for_best_model: str | None = None greater_is_better: bool | None = None ignore_data_skip: bool = False restore_callback_states_from_checkpoint: bool = False full_determinism: bool = False seed: int = 42 data_seed: int | None = None use_cpu: bool = False accelerator_config: dict | str | None = None parallelism_config: accelerate.parallelism_config.ParallelismConfig | None = None dataloader_drop_last: bool = False dataloader_num_workers: int = 0 dataloader_pin_memory: bool = True dataloader_persistent_workers: bool = False dataloader_prefetch_factor: int | None = None remove_unused_columns: bool = True label_names: list[str] | None = None train_sampling_strategy: str = 'random' length_column_name: str = 'length' ddp_find_unused_parameters: bool | None = None ddp_bucket_cap_mb: int | None = None ddp_broadcast_buffers: bool | None = None ddp_static_graph: bool | None = None ddp_backend: str | None = None ddp_timeout: int = 1800 fsdp: str | None = None fsdp_config: dict[str, typing.Any] | str | None = None deepspeed: dict | str | None = None debug: str | list[transformers.debug_utils.DebugOption] = '' skip_memory_metrics: bool = True do_train: bool = False do_eval: bool = False do_predict: bool = False resume_from_checkpoint: str | None = None warmup_ratio: float | None = None logging_dir: str | None = None local_rank: int = -1 )
Parameters
str, optional, defaults to "trainer_output") —
The output directory where the model predictions and checkpoints will be written. Training Duration and 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. 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). 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
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. str or SchedulerType, optional, defaults to "linear") —
The learning rate scheduler type to use. See SchedulerType for all possible values. Common choices:
dict or str, optional, defaults to None) —
The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values. 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:
Optimizer
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.str, optional) —
Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore. 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). 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. 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. 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. 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
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_steptraining examples.
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 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). 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
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. bool, optional, defaults to False) —
Enable float16 (FP16) mixed precision training.
Consider using BF16 instead if your hardware supports it. 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. 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. 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
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. dict, optional, defaults to None) —
Keyword arguments passed to gradient_checkpointing_enable(). Compilation
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. 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. 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
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. 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
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. 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]. 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. 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
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.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. bool, optional, defaults to False) —
Whether to log the first global_step or not. bool, optional, defaults to True) —
In multinode distributed training, whether to log using log_level once per node, or only on the main
node. 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. 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
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") str, optional, defaults to "warning") —
Logging level for replica processes in distributed training. Same options as log_level. bool, optional) —
Disable tqdm progress bars. Defaults to True if log_level is warning or lower, False otherwise. Experiment Tracking Integration
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. str, optional) —
A descriptor for the run. Typically used for trackio,
wandb, mlflow, comet and
swanlab logging. str, optional, defaults to "huggingface") —
The name of the project to use for logging. Currently, only used by Trackio. str or None, optional, defaults to None) —
The Hugging Face Space ID to use for live Trackio logging with a Gradio-based Space. Should be a full
Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' (the Space is created in
the currently logged-in user’s namespace). If None, metrics are logged only to a local directory (no
Space on the Hub). That Gradio Space has read and write access to the Trackio Bucket, which is what you want while
training is in progress—for example when resuming a partial run or aggregating logs from multiple
machines. The Space will be public unless you set hub_private_repo=True or your organization’s default is to
create private Spaces. str or None, optional, defaults to None) —
Optional Hugging Face Bucket id for Trackio. If unset, Trackio derives one. Used together with a Gradio Space
(trackio_space_id) and when deploying a static Space (trackio_static_space_id is not False). str, False, or None, optional, defaults to None) —
The Hugging Face Space ID to use for static Space created after training is complete. Should be a full
Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' (the Space is created in
the currently logged-in user’s namespace). If False, no static Space will be created. If None, and model is pushed to the Hub,
a static Space will be created with a default name and this will be linked from the model card. The Space will be public
unless you set hub_private_repo=True or your organization’s default is to create private Spaces. Evaluation
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 epochint 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. float, optional) —
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
eval_strategy. int, optional, defaults to 8) —
The batch size per device accelerator core/CPU for evaluation. bool, optional, defaults to False) —
When performing evaluation and generating predictions, only returns the loss. bool, optional, defaults to False) —
Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly. 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. 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. 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
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.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
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. 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.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. 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. 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 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
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. 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. 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. 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. 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 appearbool, optional, defaults to False) —
Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished. 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
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).
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". 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
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). 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
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. 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. 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
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
str, dict, or AcceleratorConfig, optional) —
Configuration for the internal Accelerate integration. Can be:
"accelerator_config.json"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.ParallelismConfig, optional) —
Parallelism configuration for the training run. Requires Accelerate 1.10.1 Dataloader
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. 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. bool, optional, defaults to True) —
Whether you want to pin memory in data loaders or not. Will default to True. 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. 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. bool, optional, defaults to True) —
Whether or not to automatically remove the columns unused by the model forward method. 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). str, optional, defaults to "random") —
The sampler to use for the training dataloader. Possible values are:
"random": Uses RandomSampler (default)."sequential": Uses SequentialSampler."group_by_length": Uses LengthGroupedSampler to group samples of roughly the same length
together (to minimize padding and be more efficient).Note: When using an IterableDataset, this argument is ignored.
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 train_sampling_strategy is "group_by_length" and the dataset
is an instance of Dataset. DDP (DistributedDataParallel)
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. int, optional) —
When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel. 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. bool, optional) —
When using distributed training, the value of the flag static_graph passed to
DistributedDataParallel. str, optional) —
The backend to use for distributed training. Must be one of "nccl", "mpi", "xccl", "gloo", "hccl". 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)
bool, optional, defaults to None) —
Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Pass True to enable FSDP. str or dict, optional) —
Configuration settings for when fsdp is enabled. Pass a path to a JSON config file, such
as fsdp_config.json, or an already-loaded dict.
Supported keys:
int, optional, defaults to 2):
The version of FSDP to use (2 for FSDP2, 1 for the legacy FSDP1).bool, optional, defaults to True):
Whether to reshard parameters after the forward pass. Set to False to keep parameters
gathered between the forward and backward passes, avoids the re-all-gather, and use higher peak memory.bool, optional, defaults to False):
Offload parameters and gradients to CPU when not in use to save GPU memory.bool, optional, defaults to False):
Set to True to reduce memory by recomputing activations during the backward pass. Prefer
activation_checkpointing over gradient_checkpointing when using FSDP. gradient_checkpointing
introduces a redundant all-gather in the backward pass.bool, optional, defaults to False):
Set to True to load the pretrained checkpoint on the first process only. Other processes start
with empty weights and receive the weights by broadcast.str, optional, defaults to "FULL_STATE_DICT"):
Checkpoint format: "FULL_STATE_DICT" (single HF-compatible file) or
"SHARDED_STATE_DICT" (one file per rank, faster for large models).str, optional, defaults to "TRANSFORMER_BASED_WRAP"):
Auto-wrap policy to use. Choose "TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", or "NO_WRAP".list[str], optional):
Transformer layer class names (case-sensitive) to wrap, e.g. LlamaDecoderLayer. Usually
unnecessary: the wrap policy falls back to the model’s _no_split_modules, which covers
most transformers models.int, optional, defaults to 0):
Minimum number of parameters per module for size-based auto-wrapping (used with
auto_wrap_policy="SIZE_BASED_WRAP").bool, optional, defaults to False):
Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. Experimental.dict, optional):
Dictionary of XLA FSDP wrapping parameters. For a complete list of options, see the
XLA FSDP source.bool, optional, defaults to False):
Set to True to use gradient checkpointing over each nested XLA FSDP wrapped layer. Requires
xla=True and an auto-wrapping policy (min_num_params or transformer_layer_cls_to_wrap).DeepSpeed
str or dict, optional) —
Enable DeepSpeed integration. Value is either:
"ds_config.json"If using ZeRO initialization, instantiate your model after initializing
TrainingArguments, otherwise ZeRO won’t be applied.
Debugging & Profiling (Experimental)
str or list of DebugOption, optional, defaults to "") —
Enable one or more debug features. This is an experimental feature.
Possible options are:
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)
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. 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. 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. 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. Configuration class for controlling all aspects of model training with the Trainer. TrainingArguments centralizes all hyperparameters, optimization settings, logging preferences, and infrastructure choices needed for training.
HfArgumentParser can turn this class into argparse arguments that can be specified on the command line.
Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process.
For the main process the log level defaults to the logging level set (logging.WARNING if you didn’t do
anything) unless overridden by log_level argument.
For the replica processes the log level defaults to logging.WARNING unless overridden by log_level_replica
argument.
The choice between the main and replica process settings is made according to the return value of should_log.
Get number of steps used for a linear warmup.
( local = True desc = 'work' )
Parameters
bool, optional, defaults to True) —
if True first means process of rank 0 of each node if False first means process of rank 0 of node
rank 0 In multi-node environment with a shared filesystem you most likely will want to use
local=False so that only the main process of the first node will do the processing. If however, the
filesystem is not shared, then the main process of each node will need to do the processing, which is
the default behavior. str, optional, defaults to "work") —
a work description to be used in debug logs A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it’s finished releasing the replicas.
One such use is for datasets’s map feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.
( train_batch_size: int = 8 eval_batch_size: int = 8 drop_last: bool = False num_workers: int = 0 pin_memory: bool = True persistent_workers: bool = False prefetch_factor: int | None = None auto_find_batch_size: bool = False ignore_data_skip: bool = False sampler_seed: int | None = None )
Parameters
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. 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. bool, optional, defaults to True) —
Whether you want to pin memory in data loaders or not. Will default to True. 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. 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. 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) 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. int, optional) —
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as self.seed. This can be used to ensure reproducibility of data sampling, independent of
the model seed. A method that regroups all arguments linked to the dataloaders creation.
( strategy: str | transformers.trainer_utils.IntervalStrategy = 'no' steps: int = 500 batch_size: int = 8 accumulation_steps: int | None = None delay: float | None = None loss_only: bool = False )
Parameters
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 steps."epoch": Evaluation is done at the end of each epoch.Setting a strategy different from "no" will set self.do_eval to True.
int, optional, defaults to 500) —
Number of update steps between two evaluations if strategy="steps". int optional, defaults to 8) —
The batch size per device (GPU/TPU core/CPU…) used for evaluation. 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/TPU before being moved to the CPU (faster
but requires more memory). float, optional) —
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
eval_strategy. bool, optional, defaults to False) —
Ignores all outputs except the loss. A method that regroups all arguments linked to evaluation.
Example:
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_evaluate(strategy="steps", steps=100)
>>> args.eval_steps
100( strategy: str | transformers.trainer_utils.IntervalStrategy = 'steps' steps: int = 500 report_to: str | list[str] = 'none' level: str = 'passive' first_step: bool = False nan_inf_filter: bool = False on_each_node: bool = False replica_level: str = 'passive' )
Parameters
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.int, optional, defaults to 500) —
Number of update steps between two logs if strategy="steps". 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 lets the application set the level. 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. bool, optional, defaults to False) —
Whether to log and evaluate the first global_step or not. 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.
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.
bool, optional, defaults to True) —
In multinode distributed training, whether to log using log_level once per node, or only on the main
node. str, optional, defaults to "passive") —
Logger log level to use on replicas. Same choices as log_level A method that regroups all arguments linked to logging.
( name: str | transformers.trainer_utils.SchedulerType = 'linear' num_epochs: float = 3.0 max_steps: int = -1 warmup_steps: float = 0 warmup_ratio: float | None = None )
Parameters
str or SchedulerType, optional, defaults to "linear") —
The scheduler type to use. See the documentation of SchedulerType for all possible values. 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). 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. float, optional, defaults to 0) —
Number of steps used for a linear warmup from 0 to learning_rate. Should be an integer or a float in range [0,1).
If smaller than 1, will be interpreted as ratio of steps used for a linear warmup from 0 to learning_rate. A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters.
( name: str | transformers.training_args.OptimizerNames = 'adamw_torch' learning_rate: float = 5e-05 weight_decay: float = 0 beta1: float = 0.9 beta2: float = 0.999 epsilon: float = 1e-08 args: str | None = None )
Parameters
str or training_args.OptimizerNames, optional, defaults to "adamw_torch") —
The optimizer to use: "adamw_torch", "adamw_torch_fused",
"adamw_anyprecision" or "adafactor". float, optional, defaults to 5e-5) —
The initial learning rate. float, optional, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights. float, optional, defaults to 0.9) —
The beta1 hyperparameter for the adam optimizer or its variants. float, optional, defaults to 0.999) —
The beta2 hyperparameter for the adam optimizer or its variants. float, optional, defaults to 1e-8) —
The epsilon hyperparameter for the adam optimizer or its variants. str, optional) —
Optional arguments that are supplied to AnyPrecisionAdamW (only useful when
optim="adamw_anyprecision"). A method that regroups all arguments linked to the optimizer and its hyperparameters.
( model_id: str strategy: str | transformers.trainer_utils.HubStrategy = 'every_save' token: str | None = None private_repo: bool | None = None always_push: bool = False revision: str | None = None )
Parameters
str) —
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". 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 processing_class e.g. 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 processing_class e.g. 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)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. bool, optional, defaults to False) —
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. bool, optional, defaults to False) —
Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not
finished. str, optional) —
The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash. A method that regroups all arguments linked to synchronizing checkpoints with the Hub.
Calling this method will set
self.push_to_hubtoTrue, which means theoutput_dirwill begin a git directory synced with the repo (determined bymodel_id) and the content will be pushed each time a save is triggered (depending on yourself.save_strategy). Calling save_model() will also trigger a push.
( strategy: str | transformers.trainer_utils.IntervalStrategy = 'steps' steps: int = 500 total_limit: int | None = None on_each_node: bool = False )
Parameters
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.int, optional, defaults to 500) —
Number of updates steps before two checkpoint saves if strategy="steps". int, optional) —
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
output_dir. 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.
A method that regroups all arguments linked to checkpoint saving.
Example:
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_save(strategy="steps", steps=100)
>>> args.save_steps
100( batch_size: int = 8 loss_only: bool = False )
A method that regroups all basic arguments linked to testing on a held-out dataset.
Calling this method will automatically set
self.do_predicttoTrue.
( learning_rate: float = 5e-05 batch_size: int = 8 weight_decay: float = 0 num_epochs: float = 3 max_steps: int = -1 gradient_accumulation_steps: int = 1 seed: int = 42 gradient_checkpointing: bool = False )
Parameters
float, optional, defaults to 5e-5) —
The initial learning rate for the optimizer. int optional, defaults to 8) —
The batch size per device (GPU/TPU core/CPU…) used for training. float, optional, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in the
optimizer. 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). 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. 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.
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. bool, optional, defaults to False) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass. A method that regroups all basic arguments linked to the training.
Calling this method will automatically set
self.do_traintoTrue.
Serializes this instance while replace Enum by their values (for JSON serialization support). It obfuscates
the token values by removing their value.
Serializes this instance to a JSON string.
Sanitized serialization to use with TensorBoard’s hparams
( output_dir: str | None = None per_device_train_batch_size: int = 8 num_train_epochs: float = 3.0 max_steps: int = -1 learning_rate: float = 5e-05 lr_scheduler_type: transformers.trainer_utils.SchedulerType | str = 'linear' lr_scheduler_kwargs: dict | str | None = None warmup_steps: float = 0 optim: transformers.training_args.OptimizerNames | str = 'adamw_torch_fused' optim_args: str | None = None weight_decay: float = 0.0 adam_beta1: float = 0.9 adam_beta2: float = 0.999 adam_epsilon: float = 1e-08 optim_target_modules: None | str | list[str] = None gradient_accumulation_steps: int = 1 average_tokens_across_devices: bool = True max_grad_norm: float = 1.0 label_smoothing_factor: float = 0.0 bf16: bool = False fp16: bool = False bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: bool | None = None gradient_checkpointing: bool = False gradient_checkpointing_kwargs: dict[str, typing.Any] | str | None = None torch_compile: bool = False torch_compile_backend: str | None = None torch_compile_mode: str | None = None use_liger_kernel: bool = False liger_kernel_config: dict[str, bool] | None = None use_cache: bool = False neftune_noise_alpha: float | None = None torch_empty_cache_steps: int | None = None auto_find_batch_size: bool = False logging_strategy: transformers.trainer_utils.IntervalStrategy | str = 'steps' logging_steps: float = 500 logging_first_step: bool = False log_on_each_node: bool = True logging_nan_inf_filter: bool = True include_num_input_tokens_seen: str | bool = 'no' log_level: str = 'passive' log_level_replica: str = 'warning' disable_tqdm: bool | None = None report_to: None | str | list[str] = 'none' run_name: str | None = None project: str = 'huggingface' trackio_space_id: str | None = None trackio_bucket_id: str | None = None trackio_static_space_id: typing.Union[str, NoneType, typing.Literal[False]] = None eval_strategy: transformers.trainer_utils.IntervalStrategy | str = 'no' eval_steps: float | None = None eval_delay: float = 0 per_device_eval_batch_size: int = 8 prediction_loss_only: bool = False eval_on_start: bool = False eval_do_concat_batches: bool = True eval_use_gather_object: bool = False eval_accumulation_steps: int | None = None include_for_metrics: list = <factory> batch_eval_metrics: bool = False save_only_model: bool = False save_strategy: transformers.trainer_utils.SaveStrategy | str = 'steps' save_steps: float = 500 save_on_each_node: bool = False save_total_limit: int | None = None enable_jit_checkpoint: bool = False push_to_hub: bool = False hub_token: str | None = None hub_private_repo: bool | None = None hub_model_id: str | None = None hub_strategy: transformers.trainer_utils.HubStrategy | str = 'every_save' hub_always_push: bool = False hub_revision: str | None = None load_best_model_at_end: bool = False metric_for_best_model: str | None = None greater_is_better: bool | None = None ignore_data_skip: bool = False restore_callback_states_from_checkpoint: bool = False full_determinism: bool = False seed: int = 42 data_seed: int | None = None use_cpu: bool = False accelerator_config: dict | str | None = None parallelism_config: accelerate.parallelism_config.ParallelismConfig | None = None dataloader_drop_last: bool = False dataloader_num_workers: int = 0 dataloader_pin_memory: bool = True dataloader_persistent_workers: bool = False dataloader_prefetch_factor: int | None = None remove_unused_columns: bool = True label_names: list[str] | None = None train_sampling_strategy: str = 'random' length_column_name: str = 'length' ddp_find_unused_parameters: bool | None = None ddp_bucket_cap_mb: int | None = None ddp_broadcast_buffers: bool | None = None ddp_static_graph: bool | None = None ddp_backend: str | None = None ddp_timeout: int = 1800 fsdp: str | None = None fsdp_config: dict[str, typing.Any] | str | None = None deepspeed: dict | str | None = None debug: str | list[transformers.debug_utils.DebugOption] = '' skip_memory_metrics: bool = True do_train: bool = False do_eval: bool = False do_predict: bool = False resume_from_checkpoint: str | None = None warmup_ratio: float | None = None logging_dir: str | None = None local_rank: int = -1 sortish_sampler: bool = False predict_with_generate: bool = False generation_max_length: int | None = None generation_num_beams: int | None = None generation_config: str | pathlib.Path | transformers.generation.configuration_utils.GenerationConfig | None = None )
Parameters
str, optional, defaults to "trainer_output") —
The output directory where the model predictions and checkpoints will be written. Training Duration and 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. 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). 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
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. str or SchedulerType, optional, defaults to "linear") —
The learning rate scheduler type to use. See SchedulerType for all possible values. Common choices:
dict or str, optional, defaults to None) —
The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values. 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:
Optimizer
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.str, optional) —
Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore. 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). 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. 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. 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. 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
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_steptraining examples.
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 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). 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
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. bool, optional, defaults to False) —
Enable float16 (FP16) mixed precision training.
Consider using BF16 instead if your hardware supports it. 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. 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. 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
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. dict, optional, defaults to None) —
Keyword arguments passed to gradient_checkpointing_enable(). Compilation
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. 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. 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
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. 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
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. 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]. 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. 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
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.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. bool, optional, defaults to False) —
Whether to log the first global_step or not. bool, optional, defaults to True) —
In multinode distributed training, whether to log using log_level once per node, or only on the main
node. 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. 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
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") str, optional, defaults to "warning") —
Logging level for replica processes in distributed training. Same options as log_level. bool, optional) —
Disable tqdm progress bars. Defaults to True if log_level is warning or lower, False otherwise. Experiment Tracking Integration
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. str, optional) —
A descriptor for the run. Typically used for trackio,
wandb, mlflow, comet and
swanlab logging. str, optional, defaults to "huggingface") —
The name of the project to use for logging. Currently, only used by Trackio. str or None, optional, defaults to None) —
The Hugging Face Space ID to use for live Trackio logging with a Gradio-based Space. Should be a full
Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' (the Space is created in
the currently logged-in user’s namespace). If None, metrics are logged only to a local directory (no
Space on the Hub). That Gradio Space has read and write access to the Trackio Bucket, which is what you want while
training is in progress—for example when resuming a partial run or aggregating logs from multiple
machines. The Space will be public unless you set hub_private_repo=True or your organization’s default is to
create private Spaces. str or None, optional, defaults to None) —
Optional Hugging Face Bucket id for Trackio. If unset, Trackio derives one. Used together with a Gradio Space
(trackio_space_id) and when deploying a static Space (trackio_static_space_id is not False). str, False, or None, optional, defaults to None) —
The Hugging Face Space ID to use for static Space created after training is complete. Should be a full
Space name like 'username/reponame' or 'orgname/reponame', or just 'reponame' (the Space is created in
the currently logged-in user’s namespace). If False, no static Space will be created. If None, and model is pushed to the Hub,
a static Space will be created with a default name and this will be linked from the model card. The Space will be public
unless you set hub_private_repo=True or your organization’s default is to create private Spaces. Evaluation
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 epochint 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. float, optional) —
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
eval_strategy. int, optional, defaults to 8) —
The batch size per device accelerator core/CPU for evaluation. bool, optional, defaults to False) —
When performing evaluation and generating predictions, only returns the loss. bool, optional, defaults to False) —
Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly. 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. 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. 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
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.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
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. 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.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. 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. 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 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
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. 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. 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. 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. 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 appearbool, optional, defaults to False) —
Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished. 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
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).
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". 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
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). 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
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. 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. 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
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
str, dict, or AcceleratorConfig, optional) —
Configuration for the internal Accelerate integration. Can be:
"accelerator_config.json"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.ParallelismConfig, optional) —
Parallelism configuration for the training run. Requires Accelerate 1.10.1 Dataloader
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. 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. bool, optional, defaults to True) —
Whether you want to pin memory in data loaders or not. Will default to True. 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. 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. bool, optional, defaults to True) —
Whether or not to automatically remove the columns unused by the model forward method. 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). str, optional, defaults to "random") —
The sampler to use for the training dataloader. Possible values are:
"random": Uses RandomSampler (default)."sequential": Uses SequentialSampler."group_by_length": Uses LengthGroupedSampler to group samples of roughly the same length
together (to minimize padding and be more efficient).Note: When using an IterableDataset, this argument is ignored.
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 train_sampling_strategy is "group_by_length" and the dataset
is an instance of Dataset. DDP (DistributedDataParallel)
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. int, optional) —
When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel. 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. bool, optional) —
When using distributed training, the value of the flag static_graph passed to
DistributedDataParallel. str, optional) —
The backend to use for distributed training. Must be one of "nccl", "mpi", "xccl", "gloo", "hccl". 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)
bool, optional, defaults to None) —
Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Pass True to enable FSDP. str or dict, optional) —
Configuration settings for when fsdp is enabled. Pass a path to a JSON config file, such
as fsdp_config.json, or an already-loaded dict.
Supported keys:
int, optional, defaults to 2):
The version of FSDP to use (2 for FSDP2, 1 for the legacy FSDP1).bool, optional, defaults to True):
Whether to reshard parameters after the forward pass. Set to False to keep parameters
gathered between the forward and backward passes, avoids the re-all-gather, and use higher peak memory.bool, optional, defaults to False):
Offload parameters and gradients to CPU when not in use to save GPU memory.bool, optional, defaults to False):
Set to True to reduce memory by recomputing activations during the backward pass. Prefer
activation_checkpointing over gradient_checkpointing when using FSDP. gradient_checkpointing
introduces a redundant all-gather in the backward pass.bool, optional, defaults to False):
Set to True to load the pretrained checkpoint on the first process only. Other processes start
with empty weights and receive the weights by broadcast.str, optional, defaults to "FULL_STATE_DICT"):
Checkpoint format: "FULL_STATE_DICT" (single HF-compatible file) or
"SHARDED_STATE_DICT" (one file per rank, faster for large models).str, optional, defaults to "TRANSFORMER_BASED_WRAP"):
Auto-wrap policy to use. Choose "TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", or "NO_WRAP".list[str], optional):
Transformer layer class names (case-sensitive) to wrap, e.g. LlamaDecoderLayer. Usually
unnecessary: the wrap policy falls back to the model’s _no_split_modules, which covers
most transformers models.int, optional, defaults to 0):
Minimum number of parameters per module for size-based auto-wrapping (used with
auto_wrap_policy="SIZE_BASED_WRAP").bool, optional, defaults to False):
Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. Experimental.dict, optional):
Dictionary of XLA FSDP wrapping parameters. For a complete list of options, see the
XLA FSDP source.bool, optional, defaults to False):
Set to True to use gradient checkpointing over each nested XLA FSDP wrapped layer. Requires
xla=True and an auto-wrapping policy (min_num_params or transformer_layer_cls_to_wrap).DeepSpeed
str or dict, optional) —
Enable DeepSpeed integration. Value is either:
"ds_config.json"If using ZeRO initialization, instantiate your model after initializing
TrainingArguments, otherwise ZeRO won’t be applied.
Debugging & Profiling (Experimental)
str or list of DebugOption, optional, defaults to "") —
Enable one or more debug features. This is an experimental feature.
Possible options are:
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)
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. 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. 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. 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. 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.
bool, optional, defaults to False) —
Whether to use generate to calculate generative metrics (ROUGE, BLEU). 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. 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. str or Path or GenerationConfig, optional) —
Allows to load a GenerationConfig from the from_pretrained method. This can be either:
./my_model_directory/.Configuration class for controlling all aspects of model training with the Trainer. TrainingArguments centralizes all hyperparameters, optimization settings, logging preferences, and infrastructure choices needed for training.
HfArgumentParser can turn this class into argparse arguments that can be specified on the command line.
Serializes this instance while replace Enum by their values and GenerationConfig by dictionaries (for JSON
serialization support). It obfuscates the token values by removing their value.