When training a model with Huggingface Trainer object, e.g. from https://www.kaggle.com/code/alvations/neural-plasticity-bert2bert-on-wmt14

from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments

import os
os.environ["WANDB_DISABLED"] = "true"

batch_size = 2

# set training arguments - these params are not really tuned, feel free to change
training_args = Seq2SeqTrainingArguments(
    output_dir="./",
    evaluation_strategy="steps",
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    predict_with_generate=True,
    logging_steps=2,  # set to 1000 for full training
    save_steps=16,    # set to 500 for full training
    eval_steps=4,     # set to 8000 for full training
    warmup_steps=1,   # set to 2000 for full training
    max_steps=16,     # delete for full training
    # overwrite_output_dir=True,
    save_total_limit=1,
    #fp16=True, 
)


# instantiate trainer
trainer = Seq2SeqTrainer(
    model=multibert,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=val_data,
)

trainer.train()

When it finished training, it outputs:

TrainOutput(global_step=16, training_loss=10.065429925918579, metrics={'train_runtime': 541.4209, 'train_samples_per_second': 0.059, 'train_steps_per_second': 0.03, 'total_flos': 19637939109888.0, 'train_loss': 10.065429925918579, 'epoch': 0.03})

If we want to continue training with more steps, e.g. max_steps=16 (from previous trainer.train() run) and another max_steps=160, do we do something like this?

from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments

import os
os.environ["WANDB_DISABLED"] = "true"

batch_size = 2

# set training arguments - these params are not really tuned, feel free to change
training_args = Seq2SeqTrainingArguments(
    output_dir="./",
    evaluation_strategy="steps",
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    predict_with_generate=True,
    logging_steps=2,  # set to 1000 for full training
    save_steps=16,    # set to 500 for full training
    eval_steps=4,     # set to 8000 for full training
    warmup_steps=1,   # set to 2000 for full training
    max_steps=16,     # delete for full training
    # overwrite_output_dir=True,
    save_total_limit=1,
    #fp16=True, 
)


# instantiate trainer
trainer = Seq2SeqTrainer(
    model=multibert,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=val_data,
)

# First 16 steps.
trainer.train()


# set training arguments - these params are not really tuned, feel free to change
training_args_2 = Seq2SeqTrainingArguments(
    output_dir="./",
    evaluation_strategy="steps",
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    predict_with_generate=True,
    logging_steps=2,  # set to 1000 for full training
    save_steps=16,    # set to 500 for full training
    eval_steps=4,     # set to 8000 for full training
    warmup_steps=1,   # set to 2000 for full training
    max_steps=160,     # delete for full training
    # overwrite_output_dir=True,
    save_total_limit=1,
    #fp16=True, 
)


# instantiate trainer
trainer = Seq2SeqTrainer(
    model=multibert,
    tokenizer=tokenizer,
    args=training_args_2,
    train_dataset=train_data,
    eval_dataset=val_data,
)

# Continue training for 160 steps
trainer.train()

If the above is not the canonical way to continue training a model, how to continue training with HuggingFace Trainer?

Edited

With transformers version, 4.29.1, trying @maciej-skorski answer with Seq2SeqTrainer,

trainer = Seq2SeqTrainer(
    model=multibert,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=val_data,
    resume_from_checkpoint=True
)

Its throwing an error:

TypeError: Seq2SeqTrainer.__init__() got an unexpected keyword argument 'resume_from_checkpoint'

If your use-case is about adjusting a somewhat-trained model then it can be solved just the same way as fine-tuning. To this end, you pass the current model state along with a new parameter config to the Trainer object in PyTorch API. I would say, this is canonical :-)

The code you proposed matches the general fine-tuning pattern from huggingface docs

trainer = Trainer(
    model,
    tokenizer=tokenizer,
    training_args,
    train_dataset=...,
    eval_dataset=...,
)

You may also resume training from existing checkpoints

trainer.train(resume_from_checkpoint=True)