#
Trainer

The Trainer is a complete training and evaluation loop for PyTorch models implemented in the Transformers library. You only need to pass it the necessary pieces for training (model, tokenizer, dataset, evaluation function, training hyperparameters, etc.), and the Trainer class takes care of the rest. This makes it easier to start training faster without manually writing your own training loop. But at the same time, Trainer is very customizable and offers a ton of training options so you can tailor it to your exact training needs.

In addition to the Trainer class, Transformers also provides a Seq2SeqTrainer class for sequence-to-sequence tasks like translation or summarization. There is also the SFTTrainer class from the TRL library which wraps the Trainer class and is optimized for training language models like Llama-2 and Mistral with autoregressive techniques. SFTTrainer also supports features like sequence packing, LoRA, quantization, and DeepSpeed for efficiently scaling to any model size.

Feel free to check out the API reference for these other Trainer-type classes to learn more about when to use which one. In general, Trainer is the most versatile option and is appropriate for a broad spectrum of tasks. Seq2SeqTrainer is designed for sequence-to-sequence tasks and SFTTrainer is designed for training language models.

Before you start, make sure Accelerate - a library for enabling and running PyTorch training across distributed environments - is installed.

    pip install accelerate

    # upgrade
    pip install accelerate --upgrade

This guide provides an overview of the Trainer class.

##
Basic usage

Trainer includes all the code you’ll find in a basic training loop:

1. perform a training step to calculate the loss
2. calculate the gradients with the backward method
3. update the weights based on the gradients
4. repeat this process until you’ve reached a predetermined number of epochs

The Trainer class abstracts all of this code away so you don’t have to worry about manually writing a training loop every time or if you’re just getting started with PyTorch and training. You only need to provide the essential components required for training, such as a model and a dataset, and the Trainer class handles everything else.

If you want to specify any training options or hyperparameters, you can find them in the TrainingArguments class. For example, let’s define where to save the model in `output_dir` and push the model to the Hub after training with `push_to_hub=True`.

    from transformers import TrainingArguments

    training_args = TrainingArguments(
        output_dir="your-model",
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=2,
        weight_decay=0.01,
        eval_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
        push_to_hub=True,
    )

Pass `training_args` to the Trainer along with a model, dataset, something to preprocess the dataset with (depending on your data type it could be a tokenizer, feature extractor or image processor), a data collator, and a function to compute the metrics you want to track during training.

Finally, call train() to start training!

    from transformers import Trainer

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["test"],
        processing_class=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
    )

    trainer.train()

###
Checkpoints

The Trainer class saves your model checkpoints to the directory specified in the `output_dir` parameter of TrainingArguments. You’ll find the checkpoints saved in a `checkpoint-000` subfolder where the numbers at the end correspond to the training step. Saving checkpoints are useful for resuming training later.

    # resume from latest checkpoint
    trainer.train(resume_from_checkpoint=True)

    # resume from specific checkpoint saved in output directory
    trainer.train(resume_from_checkpoint="your-model/checkpoint-1000")

You can save your checkpoints (the optimizer state is not saved by default) to the Hub by setting `push_to_hub=True` in TrainingArguments to commit and push them. Other options for deciding how your checkpoints are saved are set up in the `hub_strategy` parameter:

- `hub_strategy="checkpoint"` pushes the latest checkpoint to a subfolder named “last-checkpoint” from which you can resume training
- `hub_strategy="all_checkpoints"` pushes all checkpoints to the directory defined in `output_dir` (you’ll see one checkpoint per folder in your model repository)

When you resume training from a checkpoint, the Trainer tries to keep the Python, NumPy, and PyTorch RNG states the same as they were when the checkpoint was saved. But because PyTorch has various non-deterministic default settings, the RNG states aren’t guaranteed to be the same. If you want to enable full determinism, take a look at the Controlling sources of randomness guide to learn what you can enable to make your training fully deterministic. Keep in mind though that by making certain settings deterministic, training may be slower.

##
Customize the Trainer

While the Trainer class is designed to be accessible and easy-to-use, it also offers a lot of customizability for more adventurous users. Many of the Trainer’s method can be subclassed and overridden to support the functionality you want, without having to rewrite the entire training loop from scratch to accommodate it. These methods include:

- get_train_dataloader() creates a training DataLoader
- get_eval_dataloader() creates an evaluation DataLoader
- get_test_dataloader() creates a test DataLoader
- log() logs information on the various objects that watch training
- create_optimizer_and_scheduler() creates an optimizer and learning rate scheduler if they weren’t passed in the `__init__`; these can also be separately customized with create_optimizer() and create_scheduler() respectively
- compute_loss() computes the loss on a batch of training inputs
- training_step() performs the training step
- prediction_step() performs the prediction and test step
- evaluate() evaluates the model and returns the evaluation metrics
- predict() makes predictions (with metrics if labels are available) on the test set

For example, if you want to customize the compute_loss() method to use a weighted loss instead.

    from torch import nn
    from transformers import Trainer

    class CustomTrainer(Trainer):
        def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
            labels = inputs.pop("labels")
            # forward pass
            outputs = model(**inputs)
            logits = outputs.get("logits")
            # compute custom loss for 3 labels with different weights
            loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
            loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
            return (loss, outputs) if return_outputs else loss

###
Callbacks

Another option for customizing the Trainer is to use callbacks. Callbacks don’t change anything in the training loop. They inspect the training loop state and then execute some action (early stopping, logging results, etc.) depending on the state. In other words, a callback can’t be used to implement something like a custom loss function and you’ll need to subclass and override the compute_loss() method for that.

For example, if you want to add an early stopping callback to the training loop after 10 steps.

    from transformers import TrainerCallback

    class EarlyStoppingCallback(TrainerCallback):
        def __init__(self, num_steps=10):
            self.num_steps = num_steps

        def on_step_end(self, args, state, control, **kwargs):
            if state.global_step >= self.num_steps:
                control.should_training_stop = True

Then pass it to the Trainer’s `callback` parameter.

    from transformers import Trainer

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["test"],
        processing_class=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
        callbacks=[EarlyStoppingCallback()],
    )

##
Logging

Check out the logging API reference for more information about the different logging levels.

The Trainer is set to `logging.INFO` by default which reports errors, warnings, and other basic information. A Trainer replica - in distributed environments - is set to `logging.WARNING` which only reports errors and warnings. You can change the logging level with the `log_level` and `log_level_replica` parameters in TrainingArguments.

To configure the log level setting for each node, use the `log_on_each_node` parameter to determine whether to use the log level on each node or only on the main node.

Trainer sets the log level separately for each node in the `Trainer.__init__()` method, so you may want to consider setting this sooner if you’re using other Transformers functionalities before creating the Trainer object.

For example, to set your main code and modules to use the same log level according to each node:

    logger = logging.getLogger(__name__)

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)

    trainer = Trainer(...)

Use different combinations of `log_level` and `log_level_replica` to configure what gets logged on each of the nodes.

single node
multi-node

    my_app.py ... --log_level warning --log_level_replica error

##
NEFTune

NEFTune is a technique that can improve performance by adding noise to the embedding vectors during training. To enable it in Trainer, set the `neftune_noise_alpha` parameter in TrainingArguments to control how much noise is added.

    from transformers import TrainingArguments, Trainer

    training_args = TrainingArguments(..., neftune_noise_alpha=0.1)
    trainer = Trainer(..., args=training_args)

NEFTune is disabled after training to restore the original embedding layer to avoid any unexpected behavior.

##
Liger Kernel

Liger-Kernel Kernel is a collection of Triton kernels developed by Linkedin designed specifically for LLM training. We have implemented Hugging Face Compatible RMSNorm, RoPE, SwiGLU, CrossEntropy, FusedLinearCrossEntropy, and more to come. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The kernel works out of the box with flash attention, PyTorch FSDP, and Microsoft DeepSpeed.

Gain +20% throughput and reduce memory usage by 60% on LLaMA 3-8B model training. Achieve longer context lengths and larger batch sizes. It’s also useful if you want to scale up your model to multi-head training or large vocabulary sizes. Unleash multi-head training (medusa) and more. See details and examples in [Liger](https://github.com/linkedin/Liger-Kernel/tree/main/examples)

First make sure to install Liger official repository:

    pip install liger-kernel

You should pass `use_liger_kernel=True` to apply liger kernel on your model, for example:

    from transformers import TrainingArguments

    training_args = TrainingArguments(
        output_dir="your-model",
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=2,
        weight_decay=0.01,
        eval_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
        push_to_hub=True,
        use_liger_kernel=True
    )

The kernel supports the Llama, Gemma, Mistral, and Mixtral model architectures. The most up-to-date list of supported models can be found here. When `use_liger_kernel` is set to `True`, the corresponding layers in the original model will be patched with Liger’s efficient implementation, so you don’t need to do anything extra other than setting the argument value.

##
Optimizers

You can choose a built-in optimizer for training using:

    from transformers import TrainingArguments
    training_args = TrainingArguments(..., optim="adamw_torch")

See `OptimizerNames` for a full list of choices. We include advanced examples in the sections below.

You can also use an arbitrary PyTorch optimizer via:

    import torch

    optimizer_cls = torch.optim.AdamW
    optimizer_kwargs = {
        "lr": 4e-3,
        "betas": (0.9, 0.999),
        "weight_decay": 0.05,
    }

    from transformers import Trainer
    trainer = Trainer(..., optimizer_cls_and_kwargs=(optimizer_cls, optimizer_kwargs))

###
GaLore

Gradient Low-Rank Projection (GaLore) is a memory-efficient low-rank training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods, such as LoRA.

First make sure to install GaLore official repository:

    pip install galore-torch

Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regex or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):

    import torch
    import datasets
    import trl

    from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-galore",
        max_steps=100,
        per_device_train_batch_size=2,
        optim="galore_adamw",
        optim_target_modules=[r".*.attn.*", r".*.mlp.*"]
    )

    model_id = "google/gemma-2b"

    config = AutoConfig.from_pretrained(model_id)

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_config(config).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=512,
    )

    trainer.train()

To pass extra arguments supported by GaLore, you should pass correctly `optim_args`, for example:

    import torch
    import datasets
    import trl

    from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-galore",
        max_steps=100,
        per_device_train_batch_size=2,
        optim="galore_adamw",
        optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
        optim_args="rank=64, update_proj_gap=100, scale=0.10",
    )

    model_id = "google/gemma-2b"

    config = AutoConfig.from_pretrained(model_id)

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_config(config).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=512,
    )

    trainer.train()

You can read more about the method in the original repository or the paper.

Currently you can only train Linear layers that are considered as GaLore layers and will use low-rank decomposition to be trained while remaining layers will be optimized in the conventional manner.

Note it will take a bit of time before starting the training (~3 minutes for a 2B model on a NVIDIA A100), but training should go smoothly afterwards.

You can also perform layer-wise optimization by post-pending the optimizer name with `layerwise` like below:

    import torch
    import datasets
    import trl

    from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-galore",
        max_steps=100,
        per_device_train_batch_size=2,
        optim="galore_adamw_layerwise",
        optim_target_modules=[r".*.attn.*", r".*.mlp.*"]
    )

    model_id = "google/gemma-2b"

    config = AutoConfig.from_pretrained(model_id)

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_config(config).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=512,
    )

    trainer.train()

Note layerwise optimization is a bit experimental and does not support DDP (Distributed Data Parallel), thus you can run the training script only on a single GPU. Please see this appropriate section for more details. Other features such as gradient clipping, DeepSpeed, etc might not be supported out of the box. Please raise an issue on GitHub if you encounter such issue.

###
APOLLO

Approximated Gradient Scaling for Memory Efficient LLM Optimization (APOLLO) is a memory-efficient training strategy that allows full-parameter learning for both pre-training and fine-tuning, while maintaining AdamW-level performance with SGD-like memory efficiency.

- Ultra-low rank efficiency → Requires much lower rank than GaLore—even rank 1 (APOLLO-Mini) suffices.
- No expensive SVD computations → Unlike GaLore, APOLLO leverages random projection, avoiding training stalls.

You can read more about the method in the original repository or the APOLLO: SGD-like Memory, AdamW-level Performance.

First, make sure to install APOLLO from its official repository:

    pip install apollo-torch

Then, APOLLO optimizers can be used simply by setting `optim="apollo_adamw"` and specifying `optim_target_modules`. `optim_target_modules` can be a list of strings, regex or full path corresponding to the target module names you want to adapt. Currently, only Linear layers are considered to use the APOLLO optimizers, i.e., included in `optim_target_modules,` while the remaining models are still using AdamW.

You can also enable layer-wise APOLLO by appending “layerwise” to the optimizer name (optim=“apollo_adamw_layerwise”), the same as layer-wise GaLore. This saves additional memory for gradient by performing weight updates layer by layer.

Below is an end-to-end example script (make sure to `pip install trl datasets`):

    import torch
    import datasets
    import trl

    from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-apollo",
        max_steps=100,
        per_device_train_batch_size=2,
        optim="apollo_adamw",
        optim_target_modules=[r".*.attn.*", r".*.mlp.*"]
    )

    model_id = "google/gemma-2b"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=512,
    )

    trainer.train()

You can further customize APOLLO’s behavior by passing hyperparameters using `optim_args`.

Parameter | Description
--- | ---
`rank` | Rank of the auxiliary sub-space used for gradient scaling. APOLLO (default=256) → Works well for 1B and 7B models. APOLLO-Mini (default=1)
`scale_type` | How scaling factors are applied. `channel` → Per-channel scaling (used in APOLLO). `tensor` → Per-tensor scaling (used in APOLLO-Mini).
`scale` | Adjusts gradient updates to stabilize training. APOLLO (default=1.0) APOLLO-Mini (default=128)
`update_proj_gap` | Steps before updating projection matrices. Default: 200.
`proj` | Type of projection. Default: `random`.

The `scale` parameter can be set to `n/r`, where `n` is the original space dimension and `r` is the low-rank space dimension. Alternatively, you can achieve a similar effect by adjusting the learning rate, while keeping scale at its default value.

For example, you can enable APOLLO-Mini (rank=1 for extreme memory efficiency) by passing `optim_args`:

    args = TrainingArguments(
        output_dir="./test-galore",
        max_steps=100,
        per_device_train_batch_size=2,
        optim="apollo_adamw",
        optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
        optim_args="proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200",

    )

###
LOMO optimizer

The LOMO optimizers have been introduced in Full Parameter Fine-Tuning for Large Language Models with Limited Resources and AdaLomo: Low-memory Optimization with Adaptive Learning Rate. They both consist of an efficient full-parameter fine-tuning method. These optimizers fuse the gradient computation and the parameter update in one step to reduce memory usage. Supported optimizers for LOMO are `"lomo"` and `"adalomo"`. First either install LOMO from pypi `pip install lomo-optim` or install it from source with `pip install git+https://github.com/OpenLMLab/LOMO.git`.

According to the authors, it is recommended to use `AdaLomo` without `grad_norm` to get better performance and higher throughput.

Below is a simple script to demonstrate how to fine-tune google/gemma-2b on IMDB dataset in full precision:

    import torch
    import datasets
    from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM
    import trl

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-lomo",
        max_steps=1000,
        per_device_train_batch_size=4,
        optim="adalomo",
        gradient_checkpointing=True,
        logging_strategy="steps",
        logging_steps=1,
        learning_rate=2e-6,
        save_strategy="no",
        run_name="lomo-imdb",
    )

    model_id = "google/gemma-2b"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=1024,
    )

    trainer.train()

###
GrokAdamW optimizer

The GrokAdamW optimizer is designed to enhance training performance and stability, particularly for models that benefit from grokking signal functions. To use GrokAdamW, first install the optimizer package with `pip install grokadamw`.

GrokAdamW is particularly useful for models that require advanced optimization techniques to achieve better performance and stability.

Below is a simple script to demonstrate how to fine-tune google/gemma-2b on the IMDB dataset using the GrokAdamW optimizer:

    import torch
    import datasets
    from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM, Trainer

    # Load the IMDB dataset
    train_dataset = datasets.load_dataset('imdb', split='train')

    # Define the training arguments
    args = TrainingArguments(
        output_dir="./test-grokadamw",
        max_steps=1000,
        per_device_train_batch_size=4,
        optim="grokadamw",
        logging_strategy="steps",
        logging_steps=1,
        learning_rate=2e-5,
        save_strategy="no",
        run_name="grokadamw-imdb",
    )

    # Load the model and tokenizer
    model_id = "google/gemma-2b"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)

    # Initialize the Trainer
    trainer = Trainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
    )

    # Train the model
    trainer.train()

This script demonstrates how to fine-tune the google/gemma-2b model on the IMDB dataset using the GrokAdamW optimizer. The `TrainingArguments` are configured to use GrokAdamW, and the dataset is passed to the `Trainer` for training.

###
Schedule-Free Optimizer

The Schedule-Free optimizers have been introduced in The Road Less Scheduled. Supported optimizers for Schedule-Free are `schedule_free_radam`, `schedule_free_adamw` and `schedule_free_sgd`. First install schedulefree from pypi `pip install schedulefree`.

Schedule-Free learning replaces the momentum of the base optimizer with a combination of averaging and interpolation, to completely remove the need to anneal the learning rate with a traditional schedule. Additionally, neither `warmup_steps` nor `warmup_ratio` parameters are required when using `schedule_free_radam`.

By default, we recommend setting `lr_scheduler_type="constant"` in the `TrainingArguments`. Setting other `lr_scheduler_type` would also work, but combining Schedule-Free with other learning rate schedules is not well-studied both in research and in practice, as it may affect the optimizer’s intended behavior and performance guarantees.

Below is a simple script to demonstrate how to fine-tune google/gemma-2b on IMDB dataset in full precision:

    import torch
    import datasets
    from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM
    import trl

    train_dataset = datasets.load_dataset('imdb', split='train')

    args = TrainingArguments(
        output_dir="./test-schedulefree",
        max_steps=1000,
        per_device_train_batch_size=4,
        optim="schedule_free_radam",
        lr_scheduler_type="constant",
        gradient_checkpointing=True,
        logging_strategy="steps",
        logging_steps=1,
        learning_rate=2e-6,
        save_strategy="no",
        run_name="sfo-imdb",
    )

    model_id = "google/gemma-2b"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)

    trainer = trl.SFTTrainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        dataset_text_field='text',
        max_seq_length=1024,
    )

    trainer.train()

##
Accelerate and Trainer

The Trainer class is powered by Accelerate, a library for easily training PyTorch models in distributed environments with support for integrations such as FullyShardedDataParallel (FSDP) and DeepSpeed.

Learn more about FSDP sharding strategies, CPU offloading, and more with the Trainer in the Fully Sharded Data Parallel guide.

To use Accelerate with Trainer, run the `accelerate.config` command to set up training for your training environment. This command creates a `config_file.yaml` that’ll be used when you launch your training script. For example, some example configurations you can setup are:

DistributedDataParallel
FSDP
DeepSpeed
DeepSpeed with Accelerate plugin
Tensor Parallelism with PyTorch 2

    compute_environment: LOCAL_MACHINE
    distributed_type: MULTI_GPU
    downcast_bf16: 'no'
    gpu_ids: all
    machine_rank: 0 #change rank as per the node
    main_process_ip: 192.168.20.1
    main_process_port: 9898
    main_training_function: main
    mixed_precision: fp16
    num_machines: 2
    num_processes: 8
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false

The `accelerate_launch` command is the recommended way to launch your training script on a distributed system with Accelerate and Trainer with the parameters specified in `config_file.yaml`. This file is saved to the Accelerate cache folder and automatically loaded when you run `accelerate_launch`.

For example, to run the run_glue.py training script with the FSDP configuration:

    accelerate launch \
        ./examples/pytorch/text-classification/run_glue.py \
        --model_name_or_path google-bert/bert-base-cased \
        --task_name $TASK_NAME \
        --do_train \
        --do_eval \
        --max_seq_length 128 \
        --per_device_train_batch_size 16 \
        --learning_rate 5e-5 \
        --num_train_epochs 3 \
        --output_dir /tmp/$TASK_NAME/ \
        --overwrite_output_dir

You could also specify the parameters from the `config_file.yaml` file directly in the command line:

    accelerate launch --num_processes=2 \
        --use_fsdp \
        --mixed_precision=bf16 \
        --fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP  \
        --fsdp_transformer_layer_cls_to_wrap="BertLayer" \
        --fsdp_sharding_strategy=1 \
        --fsdp_state_dict_type=FULL_STATE_DICT \
        ./examples/pytorch/text-classification/run_glue.py \
        --model_name_or_path google-bert/bert-base-cased \
        --task_name $TASK_NAME \
        --do_train \
        --do_eval \
        --max_seq_length 128 \
        --per_device_train_batch_size 16 \
        --learning_rate 5e-5 \
        --num_train_epochs 3 \
        --output_dir /tmp/$TASK_NAME/ \
        --overwrite_output_dir

Check out the Launching your Accelerate scripts tutorial to learn more about `accelerate_launch` and custom configurations.

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