class Llama:
    def __init__(
        n_gpu_layers: int = 0,
        split_mode: int = llama_cpp.LLAMA_SPLIT_MODE_LAYER,
        main_gpu: int = 0,
        tensor_split: Optional[List[float]] = None,
        vocab_only: bool = False,
        use_mmap: bool = True,
        use_mlock: bool = False,
        kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]] = None,
        # Context Params
        seed: int = llama_cpp.LLAMA_DEFAULT_SEED,
        n_ctx: int = 512,
        n_batch: int = 512,
        n_threads: Optional[int] = None,
        n_threads_batch: Optional[int] = None,
        rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
        pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
        rope_freq_base: float = 0.0,
        rope_freq_scale: float = 0.0,
        yarn_ext_factor: float = -1.0,
        yarn_attn_factor: float = 1.0,
        yarn_beta_fast: float = 32.0,
        yarn_beta_slow: float = 1.0,
        yarn_orig_ctx: int = 0,
        logits_all: bool = False,
        embedding: bool = False,
        offload_kqv: bool = True,
        flash_attn: bool = False,
        # Sampling Params
        last_n_tokens_size: int = 64,
        # LoRA Params
        lora_base: Optional[str] = None,
        lora_scale: float = 1.0,
        lora_path: Optional[str] = None,
        # Backend Params
        numa: Union[bool, int] = False,
        # Chat Format Params
        chat_format: Optional[str] = None,
        chat_handler: Optional[llama_chat_format.LlamaChatCompletionHandler] = None,
        # Speculative Decoding
        draft_model: Optional[LlamaDraftModel] = None,
        # Tokenizer Override
        tokenizer: Optional[BaseLlamaTokenizer] = None,
    ):

# If any document is found irrelevant, transform the query string for better search results.
        if "no" in relevancy_results:
            prompt = DEFAULT_TRANSFORM_QUERY_TEMPLATE.format(query_str=query_str)
            result = self.llm.complete(prompt)
            transformed_query_str = result.text

            # Conduct a search with the transformed query string and collect the results.
            search_results = self.tavily_tool.search(
                transformed_query_str, max_results=5
            )
            search_text = "\n".join([result.text for result in search_results])
        else:
            search_text = ""

llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma2'