# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/modernbert_decoder/modular_modernbert_decoder.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_modernbert_decoder.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Johns Hopkins University, LightOn, 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. from ...configuration_utils import PretrainedConfig class ModernBertDecoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ModernBertDecoderModel`]. It is used to instantiate a ModernBert decoder 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 ModernBERT-base decoder. e.g. [blab-jhu/test-32m-dec](https://huggingface.co/blab-jhu/test-32m-dec) 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 50368): Vocabulary size of the ModernBert decoder model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ModernBertDecoderModel`] hidden_size (`int`, *optional*, defaults to 768): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 1152): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 22): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer decoder. hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu"` if not specified. max_position_embeddings (`int`, *optional*, defaults to 8192): 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. initializer_cutoff_factor (`float`, *optional*, defaults to 2.0): The cutoff factor for the truncated_normal_initializer for initializing all weight matrices. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. norm_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the normalization layers. pad_token_id (`int`, *optional*, defaults to 50283): Padding token id. eos_token_id (`int`, *optional*, defaults to 50282): End of stream token id. bos_token_id (`int`, *optional*, defaults to 50281): Beginning of stream token id. cls_token_id (`int`, *optional*, defaults to 50281): Classification token id. sep_token_id (`int`, *optional*, defaults to 50282): Separation token id. global_rope_theta (`float`, *optional*, defaults to 160000.0): The base period of the global RoPE embeddings. 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. embedding_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the embeddings. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the MLP layers. mlp_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the MLP layers. decoder_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the decoder layers. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the classifier. classifier_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the classifier. classifier_activation (`str`, *optional*, defaults to `"gelu"`): The activation function for the classifier. use_cache (`bool`, *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`. local_attention (`int`, *optional*, defaults to 128): The sliding window size for local attention. Only used for layers that use local attention. Note that for the decoder to match ModernBERT this is actually half of the sliding window size, so 128 => 64. global_attn_every_n_layers (`int`, *optional*, defaults to 3): Every `global_attn_every_n_layers` layers will use global attention instead of local attention. local_rope_theta (`float`, *optional*, defaults to 160000.0): The base period of the local RoPE embeddings. If not specified, defaults to 160000.0 layer_types (`list`, *optional*): List of layer types, one for each layer. If not specified, will be automatically generated based on `global_attn_every_n_layers`. Should contain "full_attention" or "sliding_attention". Examples: ```python >>> from transformers import ModernBertDecoderModel, ModernBertDecoderConfig >>> # Initializing a ModernBert decoder style configuration >>> configuration = ModernBertDecoderConfig() >>> # Initializing a model from the modernbert-base decoder style configuration >>> model = ModernBertDecoderModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "modernbert-decoder" attribute_map = {"rope_theta": "global_rope_theta"} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=50368, hidden_size=768, intermediate_size=1152, num_hidden_layers=22, num_attention_heads=12, hidden_activation="gelu", max_position_embeddings=8192, initializer_range=0.02, initializer_cutoff_factor=2.0, norm_eps=1e-5, norm_bias=False, pad_token_id=50283, eos_token_id=50282, bos_token_id=50281, cls_token_id=50281, sep_token_id=50282, global_rope_theta=160000.0, attention_bias=False, attention_dropout=0.0, embedding_dropout=0.0, mlp_bias=False, mlp_dropout=0.0, decoder_bias=True, classifier_dropout=0.0, classifier_bias=False, classifier_activation="gelu", use_cache=True, local_attention=128, global_attn_every_n_layers=3, local_rope_theta=160000.0, layer_types=None, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, cls_token_id=cls_token_id, sep_token_id=sep_token_id, **kwargs, ) 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.initializer_range = initializer_range self.initializer_cutoff_factor = initializer_cutoff_factor self.norm_eps = norm_eps self.norm_bias = norm_bias self.global_rope_theta = global_rope_theta self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.hidden_activation = hidden_activation self.embedding_dropout = embedding_dropout self.mlp_bias = mlp_bias self.mlp_dropout = mlp_dropout self.decoder_bias = decoder_bias self.classifier_dropout = classifier_dropout self.classifier_bias = classifier_bias self.classifier_activation = classifier_activation self.use_cache = use_cache self.global_attn_every_n_layers = global_attn_every_n_layers self.local_rope_theta = local_rope_theta # for consistency with ModernBert self.reference_compile = False # Set up layer_types for standardized layer type detection self.layer_types = layer_types if self.layer_types is None: # Create layer_types based on the alternating pattern self.layer_types = [] for layer_id in range(num_hidden_layers): if layer_id % global_attn_every_n_layers != 0: self.layer_types.append("sliding_attention") else: self.layer_types.append("full_attention") # NOTE: sliding window numbers matches ModernBERT but is only half of it self.sliding_window = local_attention // 2 if local_attention else -1 __all__ = ["ModernBertDecoderConfig"]