# Copyright 2025 Mistral AI and 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. """Ministral model configuration""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class Ministral3Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Ministral3Model`]. It is used to instantiate an Mistral 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 mistralai/Ministral-3-8B-Base-2512, mistralai/Ministral-3-8B-Instruct-2512 or mistralai/Ministral-3-8B-Reasoning-2512. [mistralai/Ministral-3-8B-Base-2512](https://huggingface.co/mistralai/Ministral-3-8B-Base-2512) [mistralai/Ministral-3-8B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512) [mistralai/Ministral-3-8B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512) 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 (`Optional`, *optional*, defaults to 131072): Vocabulary size of the Ministral3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Ministral3Model`]. hidden_size (`Optional`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. intermediate_size (`Optional`, *optional*, defaults to 14336): Dimensionality of the intermediate (feed-forward) layer. num_hidden_layers (`Optional`, *optional*, defaults to 34): Number of hidden layers in the Transformer decoder. num_attention_heads (`Optional`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`Optional`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA); if `num_key_value_heads=1`, the model will use Multi Query Attention (MQA); otherwise GQA is used. head_dim (`Optional`, *optional*, defaults to 128): The attention head dimension. If not specified, will default to `hidden_size // num_attention_heads`. hidden_act (`Optional`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`Optional`, *optional*, defaults to 262144): The maximum sequence length that this model might ever be used with. initializer_range (`Optional`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`Optional`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`Optional`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`Optional`, *optional*, defaults to 11): The id of the padding token. bos_token_id (`Optional`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`Optional`, *optional*, defaults to 2): The id of the "end-of-sequence" token. tie_word_embeddings (`Optional`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_parameters (`Union`, *optional*, defaults to `{'type': 'yarn', 'rope_theta': 1000000.0, 'factor': 16.0, 'original_max_position_embeddings': 16384, 'beta_fast': 32.0, 'beta_slow': 1.0, 'mscale_all_dim': 1.0, 'mscale': 1.0, 'llama_4_scaling_beta': 0.1}`): Dictionary containing the configuration parameters for the RoPE embeddings, including optional Yarn scaling settings such as `factor`, `original_max_position_embeddings`, `mscale`, and `llama_4_scaling_beta`. sliding_window (`Optional`, *optional*): Sliding window attention window size. If `None`, full attention is used. attention_dropout (`Optional`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. Example: ```python >>> from transformers import Ministral3Config, Ministral3ForCausalLM, Mistral3Config, Mistral3ForConditionalGeneration, PixtralVisionConfig >>> # Initializing a Pixtral-vision config >>> vision_config = PixtralVisionConfig() >>> # Initializing a Ministral3 config >>> text_config = Ministral3Config() >>> # Initializing a Mistral3 configuration >>> configuration = Mistral3Config(vision_config, text_config) >>> # Initializing a model from the Ministral3 configuration >>> text_model = Ministral3ForCausalLM(text_config) >>> # Initializing a model from the Mistral3 configuration >>> model = Mistral3ForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "ministral3" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `MistralModel` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size: int | None = 131072, hidden_size: int | None = 4096, intermediate_size: int | None = 14336, num_hidden_layers: int | None = 34, num_attention_heads: int | None = 32, num_key_value_heads: int | None = 8, head_dim: int | None = 128, hidden_act: str | None = "silu", max_position_embeddings: int | None = 262144, initializer_range: float | None = 0.02, rms_norm_eps: float | None = 1e-5, use_cache: bool | None = True, pad_token_id: int | None = 11, bos_token_id: int | None = 1, eos_token_id: int | None = 2, tie_word_embeddings: bool | None = False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, sliding_window: int | None = None, attention_dropout: float | None = 0.0, **kwargs, ): if rope_parameters is None: rope_parameters = { "type": "yarn", "rope_theta": 1000000.0, "factor": 16.0, "original_max_position_embeddings": 16384, "max_position_embeddings": max_position_embeddings, "beta_fast": 32.0, "beta_slow": 1.0, "mscale_all_dim": 1.0, "mscale": 1.0, "llama_4_scaling_beta": 0.1, } self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings 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.sliding_window = sliding_window self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_dropout = attention_dropout if "layer_types" in kwargs: logger.warning_once( "Detected Mistral model with layer_types. Consider using AutoModel or Ministral classes instead to enable alternating attention compatibility." ) self.rope_parameters = rope_parameters self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.tie_word_embeddings = tie_word_embeddings super().__init__( ignore_keys_at_rope_validation={"llama_4_scaling_beta", "max_position_embeddings"}, **kwargs, ) __all__ = ["Ministral3Config"]