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import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling
)
from datasets import load_dataset
import os
def train():
# Load model and tokenizer
model_name = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", trust_remote_code=True)
# Add padding token if missing
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load dataset (update paths as needed)
dataset = load_dataset(
"csv",
data_files={
"train": "eswardivi/medical_qa",
"validation": "eswardivi/medical_qa"
}
)
# Tokenization function
def tokenize_function(examples):
return tokenizer(
examples["text"],
padding="max_length",
truncation=True,
max_length=256,
return_tensors="pt",
)
# Preprocess dataset
tokenized_dataset = dataset.map(
tokenize_function,
batched=True,
remove_columns=["text"]
)
# Data collator
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Training arguments
training_args = TrainingArguments(
output_dir="./phi2-cpu-results",
overwrite_output_dir=True,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
num_train_epochs=3,
logging_dir="./logs",
logging_steps=100,
evaluation_strategy="epoch",
save_strategy="epoch",
fp16=False,
report_to="none",
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
data_collator=data_collator,
)
# Start training
print("Starting training...")
trainer.train()
# Save model
trainer.save_model("./phi2-trained-model")
tokenizer.save_pretrained("./phi2-trained-model")
print("Training complete! Model saved.")
if __name__ == "__main__":
train() |