"""Return logits for all tokens, not just the last token."""
    vocab_only: bool = Field(False, alias="vocab_only")
    """Only load the vocabulary, no weights."""
    use_mlock: bool = Field(False, alias="use_mlock")
    """Force system to keep model in RAM."""
    embedding: bool = Field(False, alias="embedding")
    """Use embedding mode only."""
    n_threads: Optional[int] = Field(4, alias="n_threads")
    """Number of threads to use."""
    n_predict: Optional[int] = 256
    """The maximum number of tokens to generate."""
    temp: Optional[float] = 0.7
    """The temperature to use for sampling."""
    top_p: Optional[float] = 0.1
    """The top-p value to use for sampling."""
    top_k: Optional[int] = 40
    """The top-k value to use for sampling."""
    echo: Optional[bool] = False
    """Whether to echo the prompt."""
    stop: Optional[List[str]] = []
    """A list of strings to stop generation when encountered."""
    repeat_last_n: Optional[int] = 64
    "Last n tokens to penalize"
    repeat_penalty: Optional[float] = 1.18
    """The penalty to apply to repeated tokens."""
    n_batch: int = Field(8, alias="n_batch")
    """Batch size for prompt processing."""
    streaming: bool = False
    """Whether to stream the results or not."""
    allow_download: bool = False
    """If model does not exist in ~/.cache/gpt4all/, download it."""
    client: Any = None  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""