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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))
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