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(dataset_name: str, dataset_config: str = None): 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 from Hugging Face Hub logging.info(f"Loading dataset: {eswardivi/medical_qa} (config: {dataset_config})") dataset = load_dataset( dataset_name, dataset_config, # Optional config (e.g., language for Common Voice) split="train+validation", # Combine splits trust_remote_code=True # Required for some datasets ) # Split into train/validation dataset = dataset.train_test_split(test_size=0.1, seed=42) # Tokenization function (adjust based on dataset columns) def tokenize_function(examples): return tokenizer( examples["text"], # Replace "text" with your dataset's text column padding="max_length", truncation=True, max_length=256, return_tensors="pt", ) tokenized_dataset = dataset.map( tokenize_function, batched=True, remove_columns=dataset["train"].column_names ) # 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["test"], 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 dataset input with gr.Blocks(title="Phi-2 Training") as demo: gr.Markdown("# 🚀 Train Phi-2 with HF Hub Data") with gr.Row(): dataset_name = gr.Textbox(label="Dataset Name", value="mozilla-foundation/common_voice_11_0") dataset_config = gr.Textbox(label="Dataset Config (optional)", value="en") start_btn = gr.Button("Start Training", variant="primary") status_output = gr.Textbox(label="Status", interactive=False) start_btn.click( fn=train, inputs=[dataset_name, dataset_config], outputs=status_output ) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860)