# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/smollm3/modular_smollm3.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_smollm3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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, layer_type_validation from ...modeling_rope_utils import RopeParameters class SmolLM3Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`SmolLM3Model`]. It is used to instantiate a SmolLM3 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 SmolLM3 3B. e.g. [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) 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 128256): Vocabulary size of the SmolLM3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SmolLM3Model`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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 checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `16`. 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 32768): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. 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 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 128000): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 128001): The id of the end of sentence token. 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`. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*): Sliding window attention (SWA) window size. If not specified, will default to `None`. no_rope_layers (`List[int]`, *optional*): List with at least the same length as the number of layers in the model. A `1` at an index position indicates that the corresponding layer will use RoPE, while a `0` indicates that it's a NoPE layer. no_rope_layer_interval (`int`, *optional*, defaults to 4): If `no_rope_layers` is `None`, it will be created using a NoPE layer every `no_rope_layer_interval` layers. layer_types (`list`, *optional*): Attention pattern for each layer. Automatically computed based on sliding window and NoPE settings. 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. ```python >>> from transformers import SmolLM3Model, SmolLM3Config >>> # Initializing a SmolLM3 style configuration >>> configuration = SmolLM3Config() >>> # Initializing a model from the SmolLM3 style configuration >>> model = SmolLM3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "smollm3" keys_to_ignore_at_inference = ["past_key_values"] default_theta = 2000000.0 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 = 128256, hidden_size: int | None = 2048, intermediate_size: int | None = 11008, num_hidden_layers: int | None = 36, num_attention_heads: int | None = 16, num_key_value_heads: int | None = 4, hidden_act: str | None = "silu", max_position_embeddings: int | None = 32768, initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-6, use_cache: bool | None = True, pad_token_id: int | None = 128004, bos_token_id: int | None = 128000, eos_token_id: int | None = 128001, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, use_sliding_window: bool | None = False, sliding_window: int | None = None, no_rope_layers: int | None = None, no_rope_layer_interval: int | None = 4, layer_types: int | None = None, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, mlp_bias: bool | None = False, tie_word_embeddings: bool | None = True, **kwargs, ): 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 self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.mlp_bias = mlp_bias 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.use_sliding_window = use_sliding_window self.sliding_window = sliding_window # 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 if no_rope_layers is None: self.no_rope_layers = [ int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(num_hidden_layers) ] else: self.no_rope_layers = no_rope_layers self.no_rope_layer_interval = no_rope_layer_interval # Update layer_types based on sliding window and NoPE pattern if layer_types is None: layer_types = [] for layer_idx in range(num_hidden_layers): has_rope = self.no_rope_layers[layer_idx] if use_sliding_window and sliding_window is not None and not has_rope: layer_types.append("sliding_attention") else: layer_types.append("full_attention") self.layer_types = layer_types layer_type_validation(self.layer_types, self.num_hidden_layers) self.rope_parameters = rope_parameters super().__init__(**kwargs) __all__ = ["SmolLM3Config"]