# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/moonshine/modular_moonshine.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_moonshine.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters class MoonshineConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moonshine [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32768): Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MoonshineModel`]. hidden_size (`int`, *optional*, defaults to 288): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 1152): Dimension of the MLP representations. encoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. decoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer decoder. encoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. encoder_num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. decoder_num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `decoder_num_attention_heads`. pad_head_dim_to_multiple_of (`int`, *optional*): Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain optimized attention implementations. encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. decoder_start_token_id (`int`, *optional*, defaults to 1): Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` are provided to the `generate` function. It is used to guide the model`s generation process depending on the task. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. bos_token_id (`int`, *optional*, defaults to 1): Denotes beginning of sequences token id. eos_token_id (`int`, *optional*, defaults to 2): Denotes end of sequences token id. pad_token_id (`int`, *optional*): Padding token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import MoonshineModel, MoonshineConfig >>> # Initializing a Moonshine style configuration >>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny") >>> # Initializing a model from the configuration >>> model = MoonshineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "moonshine" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_key_value_heads": "encoder_num_key_value_heads", "num_attention_heads": "encoder_num_attention_heads", "num_hidden_layers": "encoder_num_hidden_layers", } def __init__( self, vocab_size: int | None = 32768, hidden_size: int | None = 288, intermediate_size: int | None = 1152, encoder_num_hidden_layers: int | None = 6, decoder_num_hidden_layers: int | None = 6, encoder_num_attention_heads: int | None = 8, decoder_num_attention_heads: int | None = 8, encoder_num_key_value_heads: int | None = None, decoder_num_key_value_heads: int | None = None, pad_head_dim_to_multiple_of: int | None = None, encoder_hidden_act: str | None = "gelu", decoder_hidden_act: str | None = "silu", max_position_embeddings: int | None = 512, initializer_range: float | None = 0.02, decoder_start_token_id: int | None = 1, use_cache: bool | None = True, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, is_encoder_decoder: bool | None = True, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, bos_token_id: int | None = 1, eos_token_id: int | None = 2, pad_token_id: int | None = None, tie_word_embeddings: bool | None = True, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.encoder_num_hidden_layers = encoder_num_hidden_layers self.decoder_num_hidden_layers = decoder_num_hidden_layers self.encoder_num_attention_heads = encoder_num_attention_heads self.decoder_num_attention_heads = decoder_num_attention_heads if encoder_num_key_value_heads is None: encoder_num_key_value_heads = encoder_num_attention_heads self.encoder_num_key_value_heads = encoder_num_key_value_heads if decoder_num_key_value_heads is None: decoder_num_key_value_heads = decoder_num_attention_heads self.decoder_num_key_value_heads = decoder_num_key_value_heads self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of self.encoder_hidden_act = encoder_hidden_act self.decoder_hidden_act = decoder_hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.is_encoder_decoder = is_encoder_decoder self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.tie_word_embeddings = tie_word_embeddings self.rope_parameters = rope_parameters kwargs.setdefault("partial_rotary_factor", 0.9) # assign default for BC super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) __all__ = ["MoonshineConfig"]