Create fine_tune.py

fine_tune.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
+from peft import LoraConfig, get_peft_model
+from datasets import load_dataset
+
+# Load model and tokenizer
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Use 4-bit quantization to save RAM
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    load_in_4bit=True,  # ✅ Reduce VRAM usage
+    device_map="cpu"  # ✅ Force CPU usage
+)
+
+# Apply LoRA configuration
+lora_config = LoraConfig(
+    r=8,  # Low-rank dimension (small)
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"  # For TinyLlama (Chat models)
+)
+
+model = get_peft_model(model, lora_config)
+
+# Load dataset (Example: "Hello world" text)
+dataset = load_dataset("Abirate/english_quotes", split="train[:1000]")
+
+def tokenize_function(examples):
+    return tokenizer(examples["quote"], padding="max_length", truncation=True, max_length=128)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./tinyllama_lora",
+    per_device_train_batch_size=2,  # ✅ Small batch size for CPU
+    num_train_epochs=1,
+    save_steps=10,
+    logging_steps=10,
+    optim="adamw_torch",  # ✅ Better optimizer
+    save_total_limit=1,
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets
+)
+
+# Start training
+trainer.train()
+
+# Save the fine-tuned model
+model.save_pretrained("./tinyllama_lora_finetuned")
+tokenizer.save_pretrained("./tinyllama_lora_finetuned")
+print("✅ Fine-tuning complete! Model saved.")