irectory where the model predictions and checkpoints will be written.
        overwrite_output_dir (`bool`, *optional*, defaults to `False`):
            If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
            points to a checkpoint directory.
        do_train (`bool`, *optional*, defaults to `False`):
            Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
            by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_eval (`bool`, *optional*):
            Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_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](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_predict (`bool`, *optional*, defaults to `False`):
            Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
            intended to be used by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
            The evaluation strategy to adopt during training. Possible values are:

                - `"no"`: No evaluation is done during training.
                - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
                - `"epoch"`: Evaluation is done at the end of each epoch.

        prediction_loss_only (`bool`, *optional*, defaults to `False`):
            When performing evaluation and generating predictions, only returns the loss.
        per_device_train_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU/MPS/NPU core/CPU for training.
        per_device_eval_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU/MPS/NPU core/CPU for evaluation.
        gradient_accumulation_steps (`int`, *optional*, defaults to 1):
            Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

            <Tip warning={true}>

            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.

            </Tip>

        eval_accumulation_steps (`int`, *optional*):
            Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
            left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
            requires more memory).
        eval_delay (`float`, *optional*):
            Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
            evaluation_strategy.
        learning_rate (`float`, *optional*, defaults to 5e-5):
            The initial learning rate for [`AdamW`] optimizer.
        weight_decay (`float`, *optional*, defaults to 0):
            The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`]
            optimizer.
        adam_beta1 (`float`, *optional*, defaults to 0.9):
            The beta1 hyperparameter for the [`AdamW`] optimizer.
        adam_beta2 (`float`, *optional*, defaults to 0.999):
            The beta2 hyperparameter for the [`AdamW`] optimizer.
        adam_epsilon (`float`, *optional*, defaults to 1e-8):
            The epsilon hyperparameter for the [`AdamW`] optimizer.
        max_grad_norm (`float`, *optional*, defaults to 1.0):
            Maximum gradient norm (for gradient clipping).
        num_train_epochs(`float`, *optional*, defaults to 3.0):
            Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
            the last epoch before stopping training).
        max_steps (`int`, *optional*, defaults to -1):
            If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
            In case of using a finite iterable dataset the training may stop before reaching the set number of steps
            when all data is exhausted
        lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
            The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
        warmup_ratio (`float`, *optional*, defaults to 0.0):
            Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
        warmup_steps (`int`, *optional*, defaults to 0):
            Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
        log_level (`str`, *optional*, defaults to `passive`):
            Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
            'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the
            current log level for the Transformers library (which will be `"warning"` by default).
        log_level_replica (`str`, *optional*, defaults to `"warning"`):
            Logger log level to use on replicas. Same choices as `log_level`"
        log_on_each_node (`bool`, *optional*, defaults to `True`):
            In multinode distributed training, whether to log using `log_level` once per node, or only on the main
            node.
        logging_dir (`str`, *optional*):
            [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
            *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.
        logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
            The logging strategy to adopt during training. Possible values are:

                - `"no"`: No logging is done during training.
                - `"epoch"`: Logging is done at the end of each epoch.
                - `"steps"`: Logging is done every `logging_steps`.

        logging_first_step (`bool`, *optional*, defaults to `False`):
            Whether to log and evaluate the first `global_step` or not.
        logging_steps (`int` or `float`, *optional*, defaults to 500):
            Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in
            range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
        logging_nan_inf_filter (`bool`, *optional*, defaults to `True`):
            Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan`
            or `inf` is filtered and the average loss of the current logging window is taken instead.

            <Tip>

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

            </Tip>

        save_strategy (`str` or [`~trainer_utils.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`.
        save_steps (`int` or `float`, *optional*, defaults to 500):
            Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a
            float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
        save_total_limit (`int`, *optional*):
            If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
            `output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to
            `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for
            `save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained
            alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two
            checkpoints are saved: the last one and the best one (if they are different).
        save_safetensors (`bool`, *optional*, defaults to `False`):
            Use [safetensors](https://huggingface.co/docs/safetensors) saving and loading for state dicts instead of
            default `torch.load` and `torch.save`.
        save_on_each_node (`bool`, *optional*, defaults to `False`):
            When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
            the main one.

            This should not be activated when the different nodes use the same storage as the files will be saved with
            the same names for each node.
        use_cpu (`bool`, *optional*, defaults to `False`):
            Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
        seed (`int`, *optional*, defaults to 42):
            Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
            [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters.
        data_seed (`int`, *optional*):
            Random seed to be used with data samplers. If not set, random generators for data sampling will use the
            same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model
            seed.
        jit_mode_eval (`bool`, *optional*, defaults to `False`):
            Whether or not to use PyTorch jit trace for inference.
        use_ipex (`bool`, *optional*, defaults to `False`):
            Use Intel extension for PyTorch when it is available. [IPEX
            installation](https://github.com/intel/intel-extension-for-pytorch).
        bf16 (`bool`, *optional*, defaults to `False`):
            Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
            NVIDIA architecture or using CPU (use_cpu). This is an experimental API and it may change.
        fp16 (`bool`, *optional*, defaults to `False`):
            Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
        fp16_opt_level (`str`, *optional*, defaults to 'O1'):
            For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
            the [Apex documentation](https://nvidia.github.io/apex/amp).
        fp16_backend (`str`, *optional*, defaults to `"auto"`):
            This argument is deprecated. Use `half_precision_backend` instead.
        half_precision_backend (`str`, *optional*, defaults to `"auto"`):
            The backend to use for mixed precision training. Must be one of `"auto", "cuda_amp", "apex", "cpu_amp"`.
            `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices
            will force the requested backend.
        bf16_full_eval (`bool`, *optional*, defaults to `False`):
            Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
            metric values. This is an experimental API and it may change.
        fp16_full_eval (`bool`, *optional*, defaults to `False`):
            Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
            metric values.
        tf32 (`bool`, *optional*):
            Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends
            on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to
            the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an
            experimental API and it may change.
        local_rank (`int`, *optional*, defaults to -1):
            Rank of the process during distributed training.
        ddp_backend (`str`, *optional*):
            The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`.
        tpu_num_cores (`int`, *optional*):
            When training on TPU, the number of TPU cores (automatically passed by launcher script).
        dataloader_drop_last (`bool`, *optional*, defaults to `False`):
            Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
            or not.
        eval_steps (`int` or `float`, *optional*):
            Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same
            value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1,
            will be interpreted as ratio of total training steps.
        dataloader_num_workers (`int`, *optional*, defaults to 0):
            Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
            main process.
        past_index (`int`, *optional*, defaults to -1):
            Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of
            the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will
            use the corresponding output (usually index 2) as the past state and feed it to the model at the next
            training step under the keyword argument `mems`.
        run_name (`str`, *optional*):
            A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and
            [mlflow](https://www.mlflow.org/) logging.
        disable_tqdm (`bool`, *optional*):
            Whether or not to disable the tqdm progress bars and table of metrics produced by
            [`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is
            set to warn or lower (default), `False` otherwise.
        remove_unused_columns (`bool`, *optional*, defaults to `True`):
            Whether or not to automatically remove the columns unused by the model forward method.

            (Note that this behavior is not implemented for [`TFTrainer`] yet.)
        label_names (`List[str]`, *optional*):
            The list of keys in your dictionary of inputs that correspond to the labels.

            Will eventually default to the list of argument names accepted by the model that contain the word "label",
            except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the
            `["start_positions", "end_positions"]` keys.
        load_best_model_at_end (`bool`, *optional*, defaults to `False`):
            Whether or not to load the best model found during training at the end of training. When this option is
            enabled, the best checkpoint will always be saved. See
            [`save_total_limit`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.save_total_limit)
            for more.

            <Tip>

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

            </Tip>

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

            If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if
            your metric is better when lower.
        greater_is_better (`bool`, *optional*):
            Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models
            should have a greater metric or not. Will default to:

            - `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`.
            - `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`.
        ignore_data_skip (`bool`, *optional*, defaults to `False`):
            When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
            stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step
            can take a long time) but will not yield the same results as the interrupted training would have.
        sharded_ddp (`bool`, `str` or list of [`~trainer_utils.ShardedDDPOption`], *optional*, defaults to `''`):
            Use Sharded DDP training from [FairScale](https://github.com/facebookresearch/fairscale) (in distributed
            training only). This is an experimental feature.

            A list of options along the following:

            - `"simple"`: to use first instance of sharded DDP released by fairscale (`ShardedDDP`) similar to ZeRO-2.
            - `"zero_dp_2"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in
              Zero-2 mode (with `reshard_after_forward=False`).
            - `"zero_dp_3"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in
              Zero-3 mode (with `reshard_after_forward=True`).
            - `"offload"`: to add ZeRO-offload (only compatible with `"zero_dp_2"` and `"zero_dp_3"`).

            If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
            list for `False` and `["simple"]` for `True`.
        fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `''`):
            Use PyTorch Distributed Parallel Training (in distributed training only).

            A list of options along the following:

            - `"full_shard"`: Shard parameters, gradients and optimizer states.
            - `"shard_grad_op"`: Shard optimizer states and gradients.
            - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and
              `"shard_grad_op"`).
            - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
        fsdp_config (`str` or `dict`, *optional*):
            Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of
            deepspeed json config file (e.g., `ds_config.json`) or an already loaded json file as `dict`.

            A List of config and its options:
                - min_num_params (`int`, *optional*, defaults to `0`):
                    FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
                    passed).
                - transformer_layer_cls_to_wrap (`List[str]`, *optional*):
                    List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
                    `T5Block` .... (useful only when `fsdp` flag is passed).
                - backward_prefetch (`str`, *optional*)
                    FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
                    `fsdp` field is passed).

                    A list of options along the following:

                    - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's
                      gradient
                        computation.
                    - `"backward_post"` : This prefetches the next set of parameters after the current set of
                      parameter’s
                        gradient computation.
                - forward_prefetch (`bool`, *optional*, defaults to `False`)
                    FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
                     If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
                     forward pass.
                - limit_all_gathers (`bool`, *optional*, defaults to `False`)
                    FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
                     If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
                     all-gathers.
                - use_orig_params (`bool`, *optional*, defaults to `False`)
                    If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
                    frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please
                    refer this
                    [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
                - sync_module_states (`bool`, *optional*, defaults to `True`)
                    If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
                    ensure they are the same across all ranks after initialization
                - xla (`bool`, *optional*, defaults to `False`):
                    Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
                    and its API may evolve in the future.
                - xla_fsdp_settings (`dict`, *optional*)
                    The value is a dictionary which stores the XLA FSDP wrapping parameters.

                    For a complete list of options, please see [here](
                    https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
                - xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`):
                    Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be
                    used when the xla flag is set to true, and an auto wrapping policy is specified through
                    fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
                - activation_checkpointing (`bool`, *optional*, defaults to `False`):
                    If True, activation checkpointing is a technique to reduce memory usage by clearing activations of
                    certain layers and recomputing them during a backward pass. Effectively, this trades extra
                    computation time for reduced memory usage.

        deepspeed (`str` or `dict`, *optional*):
            Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may
            evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
            `ds_config.json`) or an already loaded json file as a `dict`"
        label_smoothing_factor (`float`, *optional*, defaults to 0.0):
            The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
            labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor +
            label_smoothing_factor/num_labels` respectively.
        debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`):
            Enable one or more debug features. This is an experimental feature.

            Possible options are:

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

            The options should be separated by whitespaces.
        optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`):
            The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or
            adafactor.
        optim_args (`str`, *optional*):
            Optional arguments that are supplied to AnyPrecisionAdamW.
        group_by_length (`bool`, *optional*, defaults to `False`):
            Whether or not to group together samples of roughly the same length in the training dataset (to minimize
            padding applied and be more efficient). Only useful if applying dynamic padding.
        length_column_name (`str`, *optional*, defaults to `"length"`):
            Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
            than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an
            instance of `Dataset`.
        report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
            The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
            `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"flyte"`, `"mlflow"`, `"neptune"`,
            `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no
            integrations.
        ddp_find_unused_parameters (`bool`, *optional*):
            When using distributed training, the value of the flag `find_unused_parameters` passed to
            `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
        ddp_bucket_cap_mb (`int`, *optional*):
            When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`.
        ddp_broadcast_buffers (`bool`, *optional*):
            When using distributed training, the value of the flag `broadcast_buffers` passed to
            `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
        dataloader_pin_memory (`bool`, *optional*, defaults to `True`):
            Whether you want to pin memory in data loaders or not. Will default to `True`.
        skip_memory_metrics (`bool`, *optional*, defaults to `True`):
            Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
            down the training and evaluation speed.
        push_to_hub (`bool`, *optional*, defaults to `False`):
            Whether or not to push the model to the Hub every time the model is saved. If this is activated,
            `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content
            will be pushed each time a save is triggered (depending on your `save_strategy`). Calling
            [`~Trainer.save_model`] will also trigger a push.

            <Tip warning={true}>

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

            </Tip>

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

            Will default to the name of `output_dir`.
        hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`):
            Defines the scope of what is pushed to the Hub and when. Possible values are:

            - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a
              draft of a model card when the [`~Trainer.save_model`] method is called.
            - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and
              a draft of a model card each time there is a model save. The pushes are asynchronous to not block
              training, and in case the save are very frequent, a new push is only attempted if the previous one is
              finished. A last push is made with the final model at the end of training.
            - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named
              last-checkpoint, allowing you to resume training easily with
              `trainer.train(resume_from_checkpoint="last-checkpoint")`.
            - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output
              folder (so you will get one checkpoint folder per folder in your final repository)

        hub_token (`str`, *optional*):
            The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
            `huggingface-cli login`.
        hub_private_repo (`bool`, *optional*, defaults to `False`):
            If True, the Hub repo will be set to private.
        hub_always_push (`bool`, *optional*, defaults to `False`):
            Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished.
        gradient_checkpointing (`bool`, *optional*, defaults to `False`):
            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
        include_inputs_for_metrics (`bool`, *optional*, defaults to `False`):
            Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics
            that need inputs, predictions and references for scoring calculation in Metric class.
        auto_find_batch_size (`bool`, *optional*, defaults to `False`)
            Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding
            CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
        full_determinism (`bool`, *optional*, defaults to `False`)
            If `True`, [`enable_full_determinism`] is called instead of [`set_seed`] to ensure reproducible results in
            distributed training. Important: this will negatively impact the performance, so only use it for debugging.
        torchdynamo (`str`, *optional*):
            If set, the backend compiler for TorchDynamo. Possible choices are `"eager"`, `"aot_eager"`, `"inductor"`,
            `"nvfuser"`, `"aot_nvfuser"`, `"aot_cudagraphs"`, `"ofi"`, `"fx2trt"`, `"onnxrt"` and `"ipex"`.
        ray_scope (`str`, *optional*, defaults to `"last"`):
            The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will
            then use the last checkpoint of all trials, compare those, and select the best one. However, other options
            are also available. See the [Ray documentation](
            https://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.ExperimentAnalysis.get_best_trial) for
            more options.
        ddp_timeout (`int`, *optional*, defaults to 1800):
            The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when
            performing slow operations in distributed runnings. Please refer the [PyTorch documentation]
            (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more
            information.
        use_mps_device (`bool`, *optional*, defaults to `False`):
            This argument is deprecated.`mps` device will be used if it is available similar to `cuda` device.
        torch_compile (`bool`, *optional*, defaults to `False`):
            Whether or not to compile the model using PyTorch 2.0
            [`torch.compile`](https://pytorch.org/get-started/pytorch-2.0/).

            This will use the best defaults for the [`torch.compile`
            API](https://pytorch.org/docs/stable/generated/torch.compile.html?highlight=torch+compile#torch.compile).
            You can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we
            don't guarantee any of them will work as the support is progressively rolled in in PyTorch.

            This flag and the whole compile API is experimental and subject to change in future releases.
        torch_compile_backend (`str`, *optional*):
            The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

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

            This flag is experimental and subject to change in future releases.
        torch_compile_mode (`str`, *optional*):
            The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

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

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