from dataclasses import dataclass, field @dataclass class MLXLanguageModelHandlerArguments: mlx_lm_model_name: str = field( default="mlx-community/SmolLM-360M-Instruct", metadata={ "help": "The pretrained language model to use. Default is 'microsoft/Phi-3-mini-4k-instruct'." }, ) mlx_lm_device: str = field( default="mps", metadata={ "help": "The device type on which the model will run. Default is 'cuda' for GPU acceleration." }, ) mlx_lm_torch_dtype: str = field( default="float16", metadata={ "help": "The PyTorch data type for the model and input tensors. One of `float32` (full-precision), `float16` or `bfloat16` (both half-precision)." }, ) mlx_lm_user_role: str = field( default="user", metadata={ "help": "Role assigned to the user in the chat context. Default is 'user'." }, ) mlx_lm_init_chat_role: str = field( default="system", metadata={ "help": "Initial role for setting up the chat context. Default is 'system'." }, ) mlx_lm_init_chat_prompt: str = field( default="You are a helpful and friendly AI assistant. You are polite, respectful, and aim to provide concise responses of less than 20 words.", metadata={ "help": "The initial chat prompt to establish context for the language model. Default is 'You are a helpful AI assistant.'" }, ) mlx_lm_gen_max_new_tokens: int = field( default=128, metadata={ "help": "Maximum number of new tokens to generate in a single completion. Default is 128." }, ) mlx_lm_gen_temperature: float = field( default=0.0, metadata={ "help": "Controls the randomness of the output. Set to 0.0 for deterministic (repeatable) outputs. Default is 0.0." }, ) mlx_lm_gen_do_sample: bool = field( default=False, metadata={ "help": "Whether to use sampling; set this to False for deterministic outputs. Default is False." }, ) mlx_lm_chat_size: int = field( default=2, metadata={ "help": "Number of interactions assitant-user to keep for the chat. None for no limitations." }, )