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import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, TrainerCallback
from datasets import load_from_disk
import matplotlib.pyplot as plt
# Set Hugging Face token (replace with your actual token)
os.environ["HF_TOKEN"] = "hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" # Replace with your HF_TOKEN
# Download model and tokenizer
model_name = "Salesforce/codegen-350M-multi"
local_model_path = "./codegen_model"
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=local_model_path)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, cache_dir=local_model_path)
# Set padding token
tokenizer.pad_token = tokenizer.eos_token
# Move model to CPU
device = torch.device("cpu")
model.to(device)
# Load custom dataset
dataset_path = "./custom_dataset"
dataset = load_from_disk(dataset_path)
# Tokenize dataset
def tokenize_function(examples):
inputs = [f"{prompt}\n{code}" for prompt, code in zip(examples["prompt"], examples["code"])]
return tokenizer(inputs, truncation=True, padding="max_length", max_length=128)
tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["prompt", "code"])
# Data collator for language modeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir="./finetuned_codegen",
overwrite_output_dir=True,
num_train_epochs=5, # Increased epochs for better fine-tuning
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
save_steps=500,
save_total_limit=2,
logging_steps=100,
learning_rate=5e-5,
fp16=False,
no_cuda=True,
dataloader_pin_memory=False,
)
# Custom callback to store training loss
class LossCallback(TrainerCallback):
def __init__(self):
self.losses = []
def on_log(self, args, state, control, logs=None, **kwargs):
if logs and "loss" in logs:
self.losses.append(logs["loss"])
loss_callback = LossCallback()
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
data_collator=data_collator,
callbacks=[loss_callback],
)
# Start fine-tuning
print("Starting fine-tuning...")
trainer.train()
# Save fine-tuned model
model.save_pretrained("./finetuned_codegen")
tokenizer.save_pretrained("./finetuned_codegen")
# Plot training loss
plt.plot(loss_callback.losses, label="Training Loss")
plt.xlabel("Steps")
plt.ylabel("Loss")
plt.title("Fine-Tuning Loss Curve")
plt.legend()
plt.savefig("./finetuned_codegen/loss_plot.png")
plt.show()
print("Fine-tuning completed. Model saved to ./finetuned_codegen. Loss plot saved to ./finetuned_codegen/loss_plot.png")
# Test fine-tuned model
print("\nTesting fine-tuned model...")
prompts = [
"Write a Python program to print 'Hello, World!'",
"Write a Python function to add two numbers.",
"Write a Python function to subtract two numbers.",
"Write a Python function to calculate factorial of a number",
"Write a Python function to check if a number is prime",
"Write a Python function to reverse a string"
]
for prompt in prompts:
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
outputs = model.generate(
**inputs,
max_length=200,
num_return_sequences=1,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.7,
top_p=0.9
)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Prompt: {prompt}\nGenerated Code:\n{generated_code}\n{'-'*50}") |