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199 lines
9.5 KiB
199 lines
9.5 KiB
# Copyright 2025 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Ministral model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class Ministral3Config(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Ministral3Model`]. It is used to instantiate an
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Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
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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.
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[mistralai/Ministral-3-8B-Base-2512](https://huggingface.co/mistralai/Ministral-3-8B-Base-2512)
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[mistralai/Ministral-3-8B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512)
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[mistralai/Ministral-3-8B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`Optional`, *optional*, defaults to 131072):
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Vocabulary size of the Ministral3 model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`Ministral3Model`].
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hidden_size (`Optional`, *optional*, defaults to 4096):
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Dimensionality of the embeddings and hidden states.
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intermediate_size (`Optional`, *optional*, defaults to 14336):
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Dimensionality of the intermediate (feed-forward) layer.
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num_hidden_layers (`Optional`, *optional*, defaults to 34):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`Optional`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`Optional`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA); if
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`num_key_value_heads=1`, the model will use Multi Query Attention (MQA); otherwise GQA is used.
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head_dim (`Optional`, *optional*, defaults to 128):
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The attention head dimension. If not specified, will default to `hidden_size // num_attention_heads`.
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hidden_act (`Optional`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`Optional`, *optional*, defaults to 262144):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`Optional`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`Optional`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`Optional`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`Optional`, *optional*, defaults to 11):
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The id of the padding token.
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bos_token_id (`Optional`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`Optional`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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tie_word_embeddings (`Optional`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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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}`):
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Dictionary containing the configuration parameters for the RoPE embeddings, including optional Yarn scaling
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settings such as `factor`, `original_max_position_embeddings`, `mscale`, and `llama_4_scaling_beta`.
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sliding_window (`Optional`, *optional*):
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Sliding window attention window size. If `None`, full attention is used.
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attention_dropout (`Optional`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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Example:
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```python
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>>> from transformers import Ministral3Config, Ministral3ForCausalLM, Mistral3Config, Mistral3ForConditionalGeneration, PixtralVisionConfig
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>>> # Initializing a Pixtral-vision config
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>>> vision_config = PixtralVisionConfig()
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>>> # Initializing a Ministral3 config
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>>> text_config = Ministral3Config()
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>>> # Initializing a Mistral3 configuration
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>>> configuration = Mistral3Config(vision_config, text_config)
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>>> # Initializing a model from the Ministral3 configuration
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>>> text_model = Ministral3ForCausalLM(text_config)
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>>> # Initializing a model from the Mistral3 configuration
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>>> model = Mistral3ForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "ministral3"
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keys_to_ignore_at_inference = ["past_key_values"]
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# Default tensor parallel plan for base model `MistralModel`
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size: int | None = 131072,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 14336,
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num_hidden_layers: int | None = 34,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = 8,
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head_dim: int | None = 128,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 262144,
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initializer_range: float | None = 0.02,
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rms_norm_eps: float | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = 11,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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tie_word_embeddings: bool | None = False,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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sliding_window: int | None = None,
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attention_dropout: float | None = 0.0,
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**kwargs,
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):
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if rope_parameters is None:
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rope_parameters = {
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"type": "yarn",
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"rope_theta": 1000000.0,
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"factor": 16.0,
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"original_max_position_embeddings": 16384,
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"max_position_embeddings": max_position_embeddings,
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"beta_fast": 32.0,
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"beta_slow": 1.0,
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"mscale_all_dim": 1.0,
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"mscale": 1.0,
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"llama_4_scaling_beta": 0.1,
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}
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_dropout = attention_dropout
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if "layer_types" in kwargs:
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logger.warning_once(
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"Detected Mistral model with layer_types. Consider using AutoModel or Ministral classes instead to enable alternating attention compatibility."
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)
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self.rope_parameters = rope_parameters
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(
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ignore_keys_at_rope_validation={"llama_4_scaling_beta", "max_position_embeddings"},
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**kwargs,
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)
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__all__ = ["Ministral3Config"]
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