Fine-tuning is necessary when a general model doesn’t fit a specific domain...
It improves accuracy and relevance, especially for specialized applications...
Example in Python
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
# Sample fine-tuning setup
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=4,
save_steps=10_000,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=my_train_dataset,
eval_dataset=my_eval_dataset,
)
trainer.train()