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from transformers import AutoModelForCausalLM, Trainer, TrainingArguments, AutoTokenizer
from datasets import load_dataset
import os
import yaml
def load_config(config_path):
with open(config_path, "r") as f:
return yaml.safe_load(f)
def main():
config = load_config("configs/train_config.yaml")
model_name = config["model_name"]
dataset_path = config["dataset_path"]
output_dir = config["output_dir"]
learning_rate = float(config["learning_rate"]) # Convert to float
batch_size = config["batch_size"]
num_epochs = config["num_epochs"]
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Fix for tokenizers without a pad_token (e.g., GPT-2)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example dataset loading; replace with your data loading pipeline
dataset = load_dataset("json", data_files={"train": dataset_path})
def tokenize_function(examples):
tokenized = tokenizer(
examples['text'],
truncation=True,
padding="max_length",
max_length=512
)
# Add labels identical to input_ids for causal LM loss computation
tokenized["labels"] = tokenized["input_ids"].copy()
return tokenized
tokenized_dataset = dataset.map(tokenize_function, batched=True)
training_args = TrainingArguments(
output_dir=output_dir,
overwrite_output_dir=True,
# Removed evaluation_strategy to avoid error
learning_rate=learning_rate,
per_device_train_batch_size=batch_size,
num_train_epochs=num_epochs,
save_strategy="epoch",
logging_dir='./logs',
logging_steps=10,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
tokenizer=tokenizer,
)
trainer.train()
trainer.save_model(output_dir) # Saves model files like pytorch_model.bin, config.json
tokenizer.save_pretrained(output_dir) # Saves tokenizer files like tokenizer_config.json, vocab files
if __name__ == "__main__":
main() |