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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 os
# Configure environment
os.environ["CUDA_VISIBLE_DEVICES"] = "" # Force CPU
logging.basicConfig(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
)
# Load dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
# Tokenization
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"]
)
# Training setup
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=2,
num_train_epochs=1,
logging_dir="./logs",
fp16=False,
report_to="none"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
data_collator=data_collator,
)
# Start training
logging.info("Training started...")
trainer.train()
logging.info("Training completed!")
return "✅ Training successful"
except Exception as e:
logging.error(f"Error: {str(e)}")
return f"❌ Training failed: {str(e)}"
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Phi-2 CPU Training")
start_btn = gr.Button("Start Training")
output = gr.Textbox()
start_btn.click(
fn=train,
outputs=output
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |