test_llama.py

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "meta-llama/Llama-3.2-1B"  # Replace with your model's name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the refined prompt
prompt = (
    "You are a professional tour guide specializing in Saudi Arabia. Respond to questions about tourism with accurate, "
    "structured, and concise answers. Avoid unnecessary details and maintain a professional tone.\n\n"
    "Question: List the top tourist destinations in Saudi Arabia with a brief description.\nAnswer:\n"
)

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response with optimized parameters
outputs = model.generate(
    **inputs,
    max_new_tokens=120,
    temperature=0.5,
    top_k=30,
    top_p=0.85,
    repetition_penalty=1.5,
)

# Decode and display the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("LLaMA Response:", response)