| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| # Path to your local model directory | |
| model_path = "C:/Users/YourName/Documents/fine_tuned_llama_3_2" # Update this path accordingly | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| # Load the model with optimized settings | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| torch_dtype=torch.float16, # Use float16 for efficiency | |
| device_map="auto" # Automatically use GPU if available | |
| ) | |
| # Move model to GPU if available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| print("✅ Model loaded successfully!") | |
| # Function to generate text | |
| def generate_text(prompt, max_length=200): | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| output = model.generate(**inputs, max_length=max_length) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| # Example usage | |
| prompt = "The future of AI is" | |
| print("\nGenerated Output:\n", generate_text(prompt)) | |