import torch
from transformers import Trainer, TrainingArguments
from my_custom_model import MyCustomModel  # import your custom model class here
from my_dataset import MyDataset  # import your dataset class here

# Instantiate the tokenizer
tokenizer = ...  # define your tokenizer here

# Load the dataset and preprocess it
train_dataset = MyDataset(...)  # define your training dataset here
val_dataset = MyDataset(...)  # define your validation dataset here

# Define your custom model and the training arguments
model = MyCustomModel(...)  # define your custom model here
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy='epoch',
    learning_rate=2e-4,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=1,
    weight_decay=0.01,
)

# Define the trainer and train the model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)
trainer.train()

# Save the trained model
model_path = './trained_model'
model.save_pretrained(model_path)

# Load the trained model
model = MyCustomModel.from_pretrained(model_path)

# Define your inference function
def answer_question(input_text):
    # Tokenize the input text
    input_ids = tokenizer.encode(input_text, return_tensors='pt')

    # Generate the answer
    answer_ids = model.generate(input_ids)
    answer = tokenizer.decode(answer_ids[0], skip_special_tokens=True)

    return answer

# Test the model with an example input
input_text = "Your input text here"
answer = answer_question(input_text)
print(answer)