# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/olmo_hybrid/modular_olmo_hybrid.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_olmo_hybrid.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2026 the HuggingFace 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, layer_type_validation class OlmoHybridConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`OlmoHybridModel`]. It is used to instantiate an OLMo Hybrid 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 [allenai/Olmo-Hybrid-7B](https://huggingface.co/allenai/Olmo-Hybrid-7B) model. 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 100352): Vocabulary size of the OlmoHybrid model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OlmoHybridModel`]. hidden_size (`int`, *optional*, defaults to 3840): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 30): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): 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. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 65536): 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. 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`. pad_token_id (`int`, *optional*, defaults to 100277): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 100257): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. Can be `None` to disable RoPE (e.g., during long context extension). 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. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. layer_types (`list`, *optional*): Attention pattern for each layer. Can contain `"full_attention"` or `"linear_attention"`. Defaults to linear attention for most layers with full attention for every 4th layer. linear_num_key_heads (`int`, *optional*): Number of key heads for the linear attention layers. Defaults to `num_attention_heads`. linear_num_value_heads (`int`, *optional*): Number of value heads for the linear attention layers. Defaults to `num_attention_heads`. linear_key_head_dim (`int`, *optional*): Dimension of each key head in linear attention layers. Defaults to `0.75 * hidden_size / linear_num_key_heads`. linear_value_head_dim (`int`, *optional*): Dimension of each value head in linear attention layers. Defaults to `2 * linear_key_head_dim`. linear_a_log_min (`float`, *optional*, defaults to 0.0): Minimum value for uniform initialization of A_log in GatedDeltaNet layers. linear_a_log_max (`float`, *optional*, defaults to 16.0): Maximum value for uniform initialization of A_log in GatedDeltaNet layers. linear_dt_min (`float`, *optional*, defaults to 0.001): Minimum value for dt initialization in GatedDeltaNet layers. linear_dt_max (`float`, *optional*, defaults to 0.1): Maximum value for dt initialization in GatedDeltaNet layers. linear_dt_init_floor (`float`, *optional*, defaults to 0.0001): Floor value for clamping dt during initialization in GatedDeltaNet layers. linear_conv_kernel_dim (`int`, *optional*, defaults to 4): Kernel size for the short convolution applied to queries, keys, and values in linear attention layers. linear_allow_neg_eigval (`bool`, *optional*, defaults to `True`): Whether to allow negative eigenvalues in the GatedDeltaNet recurrence. When `True`, the beta parameter is scaled by 2.0 to allow values in range [0, 2] instead of [0, 1]. ```python >>> from transformers import OlmoHybridModel, OlmoHybridConfig >>> # Initializing an OlmoHybrid style configuration >>> configuration = OlmoHybridConfig() >>> # Initializing a model from the OlmoHybrid style configuration >>> model = OlmoHybridModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "olmo_hybrid" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k "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 = 100352, hidden_size: int | None = 3840, intermediate_size: int | None = 11008, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 30, num_key_value_heads: int | None = None, hidden_act: str | None = "silu", max_position_embeddings: int | None = 65536, initializer_range: float | None = 0.02, use_cache: bool | None = True, pad_token_id: int | None = 100277, bos_token_id: int | None = None, eos_token_id: int | None = 100257, tie_word_embeddings: bool | None = False, rope_parameters=None, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, rms_norm_eps: float | None = 1e-06, layer_types: list[str] | None = None, linear_num_key_heads: int | None = None, linear_num_value_heads: int | None = None, linear_key_head_dim: int | None = None, linear_value_head_dim: int | None = None, linear_a_log_min: float = 0.0, linear_a_log_max: float = 16.0, linear_dt_min: float = 0.001, linear_dt_max: float = 0.1, linear_dt_init_floor: float = 1e-4, linear_conv_kernel_dim: int = 4, linear_allow_neg_eigval: bool = True, **kwargs, ): if layer_types is None: # Default: linear attention for most layers, full attention every 4th layer layer_types = ["linear_attention"] * int(num_hidden_layers) for i in range(int(num_hidden_layers)): if i % 4 == 3: layer_types[i] = "full_attention" # Ensure at least one full attention layer for small num_hidden_layers if "full_attention" not in layer_types: layer_types[-1] = "full_attention" layer_type_validation(layer_types, num_hidden_layers) if "linear_attention" not in layer_types: raise ValueError("OLMoHybrid expects at least one 'linear_attention' layer.") if all(t == "linear_attention" for t in layer_types): raise ValueError("OLMoHybrid expects at least one attention layer.") self.layer_types = layer_types if linear_num_key_heads is None: linear_num_key_heads = num_attention_heads if linear_num_value_heads is None: linear_num_value_heads = num_attention_heads if linear_key_head_dim is None: linear_key_head_dim = int(0.75 * hidden_size / linear_num_key_heads) if linear_value_head_dim is None: linear_value_head_dim = 2 * linear_key_head_dim self.linear_num_key_heads = linear_num_key_heads self.linear_num_value_heads = linear_num_value_heads self.linear_key_head_dim = linear_key_head_dim self.linear_value_head_dim = linear_value_head_dim self.linear_a_log_min = linear_a_log_min self.linear_a_log_max = linear_a_log_max self.linear_dt_min = linear_dt_min self.linear_dt_max = linear_dt_max self.linear_dt_init_floor = linear_dt_init_floor self.linear_conv_kernel_dim = linear_conv_kernel_dim self.linear_allow_neg_eigval = linear_allow_neg_eigval 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 # 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_bias = attention_bias self.attention_dropout = attention_dropout self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) __all__ = ["OlmoHybridConfig"]