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import torch
import gradio as gr
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling
)
from datasets import load_dataset
import logging
import sys
# Configure logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
def train():
try:
# 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 tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
dataset = load_dataset(
"csv",
data_files={
"train": "data/train/data.csv",
"validation": "data/validation/data.csv"
}
)
# Tokenization function
def tokenize_function(examples):
return tokenizer(
examples["text"],
padding="max_length",
truncation=True,
max_length=256,
return_tensors="pt",
)
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-results",
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
num_train_epochs=3,
logging_dir="./logs",
logging_steps=10,
fp16=False,
)
# Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
data_collator=data_collator,
)
# Start training
logging.info("Training started...")
trainer.train()
trainer.save_model("./phi2-trained-model")
logging.info("Training completed!")
return "✅ Training succeeded! Model saved."
except Exception as e:
logging.error(f"Training failed: {str(e)}")
return f"❌ Training failed: {str(e)}"
# Gradio UI
with gr.Blocks(title="Phi-2 Training") as demo:
gr.Markdown("# 🚀 Train Phi-2 on CPU")
with gr.Row():
start_btn = gr.Button("Start Training", variant="primary")
status_output = gr.Textbox(label="Status", interactive=False)
start_btn.click(
fn=train,
outputs=status_output
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |