class TextGenerationInputGenerateParameters(BaseInferenceType):
    adapter_id: Optional[str] = None
    """Lora adapter id"""
    best_of: Optional[int] = None
    """Generate best_of sequences and return the one if the highest token logprobs."""
    decoder_input_details: Optional[bool] = None
    """Whether to return decoder input token logprobs and ids."""
    details: Optional[bool] = None
    """Whether to return generation details."""
    do_sample: Optional[bool] = None
    """Activate logits sampling."""
    frequency_penalty: Optional[float] = None
    """The parameter for frequency penalty. 1.0 means no penalty
    Penalize new tokens based on their existing frequency in the text so far,
    decreasing the model's likelihood to repeat the same line verbatim.
    """
    grammar: Optional[TextGenerationInputGrammarType] = None
    max_new_tokens: Optional[int] = None
    """Maximum number of tokens to generate."""
    repetition_penalty: Optional[float] = None
    """The parameter for repetition penalty. 1.0 means no penalty.
    See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
    """
    return_full_text: Optional[bool] = None
    """Whether to prepend the prompt to the generated text"""
    seed: Optional[int] = None
    """Random sampling seed."""
    stop: Optional[List[str]] = None
    """Stop generating tokens if a member of `stop` is generated."""
    temperature: Optional[float] = None
    """The value used to module the logits distribution."""
    top_k: Optional[int] = None
    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
    top_n_tokens: Optional[int] = None
    """The number of highest probability vocabulary tokens to keep for top-n-filtering."""
    top_p: Optional[float] = None
    """Top-p value for nucleus sampling."""
    truncate: Optional[int] = None
    """Truncate inputs tokens to the given size."""
    typical_p: Optional[float] = None
    """Typical Decoding mass
    See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
    for more information.
    """
    watermark: Optional[bool] = None
    """Watermarking with [A Watermark for Large Language
    Models](https://arxiv.org/abs/2301.10226).
    """
/Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/inference/_generated/types/text_generation.py