Create finetune.py

finetune.py

@@ -0,0 +1,41 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load model and tokenizer
+model_name = "distilgpt2"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load dialogue dataset
+dataset = load_dataset("HuggingFaceH4/ultrachat", split="train[:1%]")  # Use 1% for demo
+
+# Preprocess dataset
+def preprocess(examples):
+    prompts = [f"User: {ex['prompt']} Assistant: {ex['response']}" for ex in examples]
+    return tokenizer(prompts, truncation=True, padding="max_length", max_length=512)
+
+tokenized_dataset = dataset.map(preprocess, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./evo_finetuned",
+    per_device_train_batch_size=4,
+    num_train_epochs=3,
+    save_steps=1000,
+    save_total_limit=2,
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+)
+
+# Fine-tune
+trainer.train()
+
+# Save model
+model.save_pretrained("evo_finetuned")
+tokenizer.save_pretrained("evo_finetuned")