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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
def test_model():
base_model_name = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_path = "./qwen-codeforces-cots"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=True)
print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
dtype=torch.float32,
trust_remote_code=True,
)
print("Loading fine-tuned adapter...")
model = PeftModel.from_pretrained(base_model, adapter_path)
model.eval()
# Test with a simple programming problem
test_problem = """You are given an array a of n integers. Find the maximum element in the array.
Input format:
The first line contains an integer n (1 ≤ n ≤ 100).
The second line contains n integers a₁, a₂, ..., aₙ (1 ≤ aᵢ ≤ 1000).
Output format:
Print the maximum element."""
messages = [
{"role": "user", "content": f"Please reason step by step about the solution, then provide a complete implementation.\n\n# Problem\n\n{test_problem}"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt")
print("\nGenerating response...")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
top_p=0.9,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("\n" + "="*80)
print("MODEL RESPONSE:")
print("="*80)
print(response)
print("="*80)
if __name__ == "__main__":
test_model()
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