# 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, RotaryEmbeddingConfigMixin from ..auto import CONFIG_MAPPING, AutoConfig from ..mistral.configuration_mistral import MistralConfig class VoxtralRealtimeTextConfig(MistralConfig): r""" This is the configuration class to store the configuration of a [`VoxtralRealtimeText`]. It is used to instantiate a Voxtral Realtime text decoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text decoder of the Voxtral Realtime architecture. e.g. [mistralai/Voxtral-Mini-4B-Realtime-2602](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) """ model_type = "voxtral_realtime_text" class VoxtralRealtimeEncoderConfig(PreTrainedConfig, RotaryEmbeddingConfigMixin): r""" This is the configuration class to store the configuration of a [`VoxtralRealtimeEncoder`]. It is used to instantiate a Voxtral Realtime audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the Voxtral Realtime architecture. e.g. [mistralai/Voxtral-Mini-4B-Realtime-2602](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) 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 131072): Vocabulary size of the model. hidden_size (`int`, *optional*, defaults to 1280): Dimensionality of the hidden representations. intermediate_size (`int`, *optional*, defaults to 5120): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `VoxtralRealtimeProcessor` class. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. hidden_act (`str`, *optional*, defaults to `"silu"`): The activation function used in the MLP layers. max_position_embeddings (`int`, *optional*, defaults to 1500): The maximum sequence length that this model might ever be used with. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the RMS normalization layers. rope_parameters (`Union[RopeParameters, dict]`, *optional*): The parameters for the rotary position embeddings. sliding_window (`int`, *optional*, defaults to 750): The sliding window size for local attention. head_dim (`int`, *optional*, defaults to 64): The dimension of each attention head. ```python >>> from transformers import VoxtralRealtimeEncoderConfig, VoxtralRealtimeEncoder >>> # Initializing a VoxtralRealtimeEncoderConfig >>> configuration = VoxtralRealtimeEncoderConfig() >>> # Initializing a VoxtralRealtimeEncoder (with random weights) >>> model = VoxtralRealtimeEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "voxtral_realtime_encoder" attribute_map = { "d_model": "hidden_size", "encoder_layers": "num_hidden_layers", "encoder_attention_heads": "num_attention_heads", "encoder_ffn_dim": "intermediate_size", "encoder_layerdrop": "layerdrop", } def __init__( self, vocab_size=131072, hidden_size=1280, intermediate_size=5120, num_hidden_layers=32, num_attention_heads=32, activation_function="gelu", num_mel_bins=128, initializer_range=0.02, attention_dropout=0.0, hidden_act="silu", max_position_embeddings=1500, rms_norm_eps=1e-05, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, sliding_window=750, head_dim=64, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.activation_function = activation_function self.num_mel_bins = num_mel_bins self.initializer_range = initializer_range self.num_key_value_heads = num_attention_heads self.rms_norm_eps = rms_norm_eps self.max_position_embeddings = max_position_embeddings self.rope_parameters = rope_parameters self.hidden_act = hidden_act self.sliding_window = sliding_window self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads self.attention_dropout = attention_dropout super().__init__(**kwargs) class VoxtralRealtimeConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`VoxtralRealtimeForConditionalGeneration`]. It is used to instantiate a Voxtral Realtime 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 Voxtral Realtime. e.g. [mistralai/Voxtral-Mini-4B-Realtime-2602](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: audio_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the audio encoder. text_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the text model. projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function (function or string) in the multi-modal projector. audio_length_per_tok (`int`, *optional*, defaults to 8): The number of audio frames corresponding to each text token. default_num_delay_tokens (`int`, *optional*, defaults to 6): The default number of delay tokens used for streaming. downsample_factor (`int`, *optional*, defaults to 4): The downsampling factor applied to audio features before projection. ```python >>> from transformers import VoxtralRealtimeForConditionalGeneration, VoxtralRealtimeConfig >>> # Initializing a VoxtralRealtime configuration >>> configuration = VoxtralRealtimeConfig() >>> # Initializing a model with random weights >>> model = VoxtralRealtimeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "voxtral_realtime" sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig} _default_text_config_kwargs = { "vocab_size": 131072, "hidden_size": 3072, "intermediate_size": 9216, "num_hidden_layers": 26, "num_attention_heads": 32, "num_key_value_heads": 8, "max_position_embeddings": 131072, "rms_norm_eps": 1e-05, "use_cache": True, "rope_theta": 1000000.0, "head_dim": 128, "tie_word_embeddings": True, "sliding_window": 8192, } def __init__( self, audio_config=None, text_config=None, projector_hidden_act="gelu", audio_length_per_tok=8, default_num_delay_tokens=6, downsample_factor=4, **kwargs, ): if isinstance(audio_config, dict): audio_config["model_type"] = audio_config.get("model_type", "voxtral_realtime_encoder") audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config) elif audio_config is None: audio_config = CONFIG_MAPPING["voxtral_realtime_encoder"]() self.audio_config = audio_config if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "voxtral_realtime_text") text_config = CONFIG_MAPPING[text_config["model_type"]]( **{**self._default_text_config_kwargs, **text_config} ) elif text_config is None: text_config = CONFIG_MAPPING["voxtral_realtime_text"](**self._default_text_config_kwargs) self.text_config = text_config self.hidden_size = text_config.hidden_size self.projector_hidden_act = projector_hidden_act self.audio_length_per_tok = audio_length_per_tok self.default_num_delay_tokens = default_num_delay_tokens self.downsample_factor = downsample_factor super().__init__(**kwargs) __all__ = ["VoxtralRealtimeEncoderConfig", "VoxtralRealtimeConfig", "VoxtralRealtimeTextConfig"]