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#!/usr/bin/env python
import os
import logging
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
Trainer,
TrainingArguments,
DataCollatorForLanguageModeling,
)
from datasets import load_dataset
# Setup logging for progress messages
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# 1. Load the pre-trained tokenizer and model
model_name = "sshleifer/tiny-gpt2" # Using GPT-2 as a small language model example
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Check if a padding token is defined; if not, set it.
if tokenizer.pad_token is None:
# Option 1: Use the end-of-sequence token as the padding token.
tokenizer.pad_token = tokenizer.eos_token
# Option 2 (uncomment to use a dedicated PAD token):
# tokenizer.add_special_tokens({'pad_token': '[PAD]'})
# After adding special tokens, resize model embeddings:
# model.resize_token_embeddings(len(tokenizer))
# Load the pre-trained model.
model = AutoModelForCausalLM.from_pretrained(model_name)
# 2. Prepare the dataset
# For demonstration, we use the Wikitext-2 raw dataset.
dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
# Define a tokenization function.
def tokenize_function(examples):
# Tokenize texts with padding (to max_length) and truncation.
# Here, we set max_length=512. Adjust as needed.
return tokenizer(
examples["text"],
truncation=True,
max_length=32,
padding="max_length"
)
# Apply the tokenization function over the dataset.
tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"])
# 3. Create a data collator for language modeling (no masked LM).
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# 4. Setup training arguments
training_args = TrainingArguments(
output_dir="./gpt2-finetuned",
overwrite_output_dir=True,
num_train_epochs=1, # Adjust the number of epochs as needed
per_device_train_batch_size=8, # Adjust based on your available GPU memory
save_steps=500,
save_total_limit=2,
logging_steps=100,
prediction_loss_only=True, # Useful for language modeling tasks
)
# 5. Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
data_collator=data_collator,
)
# 6. Start training
logger.info("Starting training...")
trainer.train()
# 7. Save the fine-tuned model and tokenizer
model.save_pretrained("./gpt2-finetuned")
tokenizer.save_pretrained("./gpt2-finetuned")
logger.info("Training complete and model saved.")
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