class_docstr: Wrapper around the BigDL-LLM
  
  Example:
      .. code-block:: python
  
          from langchain.llms import LlamaLLM
          llm = LlamaLLM(model_path="/path/to/llama/model")
docstr: Get the default parameters for calling llama_cpp.
docstr_tok: ["Get","the","default","parameters","for","calling","llama_cpp","."]
code: def _default_params(self) -> Dict[str, Any]:
      """Get the default parameters for calling llama_cpp."""
      return {
          "suffix": self.suffix,
          "max_tokens": self.max_tokens,
          "temperature": self.temperature,
          "top_p": self.top_p,
          "logprobs": self.logprobs,
          "echo": self.echo,
          "stop_sequences": self.stop,  # key here is convention among LLM classes
          "repeat_penalty": self.repeat_penalty,
          "top_k": self.top_k,
      }
  
imports: import logging
  import importlib
  from typing import Any, Dict, Generator, List, Optional
  from pydantic import Field, root_validator
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.llms.base import LLM
  from .transformersllm import TransformersLLM
label_desc: Use image node_id 2 for calling the _BaseCausalLM obj's underlying member method code with example usage: obj._default_params() and returns: dict