def __init__(self, model_name="/root/.cache/modelscope/hub/Qwen/Qwen2___5-Math-7B-Instruct",
                 device="cuda", max_new_tokens=512, temperature=0.7, top_k=30, top_p=0.9, model_type="vllm"):
        """
        Initialize the LanguageModel with parameters for the LLM service.

        Parameters:
        - model_name (str): Path or model name for the LLM.
        - device (str): Device for computation (e.g., 'cuda', 'cpu').
        - max_new_tokens (int): Max tokens for response generation.
        - temperature (float): Sampling temperature for diversity.
        - top_k (int): Top-K sampling for diversity.
        - top_p (float): Top-P sampling for response diversity.
        """
        self.llm_service = LLMService(
            model_name=model_name,
            device=device,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            model_type=model_type
        )
        self.default_prompt = (
            "Please complete the answer for the question based on the given steps without generating existing steps again, "
            "and separate your following steps using \\n\\n.\n\n"
        )
        self.llm_service.start_service()

        Initialize the LanguageModel with parameters for the LLM service.

        Parameters:
        - model_name (str): Path or model name for the LLM.
        - device (str): Device for computation (e.g., 'cuda', 'cpu').
        - max_new_tokens (int): Max tokens for response generation.
        - temperature (float): Sampling temperature for diversity.
        - top_k (int): Top-K sampling for diversity.
        - top_p (float): Top-P sampling for response diversity.
        
data/omegaPRM_v2/omegaprm.py