# coding=utf-8 # 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 ..auto import CONFIG_MAPPING, AutoConfig class VoxtralEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VoxtralEncoder`]. It is used to instantiate a Voxtral 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 architecture. e.g. [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507) 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 51866): 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 20): Number of attention heads for each attention layer in the Transformer encoder. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by dividing by sqrt(hidden_size) if True. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `VoxtralProcessor` class. max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features 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. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import VoxtralEncoderConfig, VoxtralEncoder >>> # Initializing a VoxtralEncoderConfig >>> configuration = VoxtralEncoderConfig() >>> # Initializing a VoxtralEncoder (with random weights) >>> model = VoxtralEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "voxtral_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=51866, hidden_size=1280, intermediate_size=5120, num_hidden_layers=32, num_attention_heads=20, scale_embedding=False, activation_function="gelu", num_mel_bins=128, max_source_positions=1500, initializer_range=0.02, attention_dropout=0.0, **kwargs, ): super().__init__(**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.scale_embedding = scale_embedding # scale factor will be sqrt(hidden_size) if True self.activation_function = activation_function self.num_mel_bins = num_mel_bins self.max_source_positions = max_source_positions self.initializer_range = initializer_range # TODO: @eustlb, we do not use dropout and layerdrop, yet we need to hardcode them # to be able to use Whisper with modular (here actually from Qwen2-Audio and copied from). # After a future Whisper refactor, we should remove this. self.dropout = 0.0 self.layerdrop = 0.0 self.activation_dropout = 0.0 self.attention_dropout = attention_dropout class VoxtralConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VoxtralForConditionalGeneration`]. It is used to instantiate an Voxtral 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-Mini-3B. e.g. [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507) 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. audio_token_id (`int`, *optional*): The image token index to encode the image prompt. projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function (function or string) in the multi-modal projector. ```python >>> from transformers import VoxtralForConditionalGeneration, VoxtralConfig >>> # Initializing a Voxtral configuration >>> configuration = VoxtralConfig(audio_token_id=24, projector_hidden_act="gelu") >>> # Initializing a 3B model with random weights >>> model = VoxtralForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "voxtral" sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig} _default_text_config_kwargs = { "vocab_size": 131072, "hidden_size": 3072, "intermediate_size": 8192, "num_hidden_layers": 30, "num_key_value_heads": 8, "max_position_embeddings": 131072, "rms_norm_eps": 1e-05, "use_cache": True, "rope_theta": 100000000.0, "head_dim": 128, } def __init__( self, audio_config=None, text_config=None, audio_token_id=None, projector_hidden_act="gelu", **kwargs, ): if isinstance(audio_config, dict): audio_config["model_type"] = audio_config.get("model_type", "voxtral_encoder") audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config) elif audio_config is None: audio_config = CONFIG_MAPPING["voxtral_encoder"]() self.audio_config = audio_config if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "llama") text_config = CONFIG_MAPPING[text_config["model_type"]]( **{**self._default_text_config_kwargs, **text_config} ) elif text_config is None: text_config = CONFIG_MAPPING["llama"](**self._default_text_config_kwargs) self.text_config = text_config self.vocab_size = text_config.vocab_size self.hidden_size = text_config.hidden_size self.audio_token_id = audio_token_id self.projector_hidden_act = projector_hidden_act super().__init__(**kwargs) __all__ = ["VoxtralEncoderConfig", "VoxtralConfig"]