# Vendored from transformers' LFM3 code (not in a release yet) at 69da1b47a11e362fbb15bfe5d5c8da0790c688c5, src/transformers/models/lfm3/configuration_lfm3.py: # its relative imports made absolute, nothing else changed. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/lfm3/modular_lfm3.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_lfm3.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 huggingface_hub.dataclasses import strict from transformers.configuration_utils import PreTrainedConfig from transformers.utils import auto_docstring @auto_docstring(checkpoint="LiquidAI/LFM3-1.7B") @strict class Lfm3Config(PreTrainedConfig): r""" rope_parameters (`dict[str, RopeParameters]`, *optional*): RoPE parameters keyed by attention layer type, for example `{"full_attention": {"rope_type": "default", "rope_theta": 1000000.0, "partial_rotary_factor": 0.5}, "sliding_attention": {"rope_type": "default", "rope_theta": 10000.0}}`. `conv` layers do not use RoPE and have no entry. Full-attention layers rotate the first `partial_rotary_factor * head_dim` dimensions and leave the rest unrotated. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the conv layers. conv_L_cache (`int`, *optional*, defaults to 7): L_cache dim in the conv layers. layer_types (`list[str]`, *optional*): Per-layer operator type, each one of `"full_attention"`, `"sliding_attention"` or `"conv"`. Defaults to the LFM3 pattern of a full-attention layer every fourth layer starting from the third, a sliding-attention layer in between, and gated short convolutions elsewhere. sliding_window (`int`, *optional*, defaults to 256): Sliding-window size of the `sliding_attention` layers. use_sandwich_norm (`bool`, *optional*, defaults to `True`): Whether to apply an RMSNorm to the operator and feed-forward outputs before they enter the residual stream. ```python >>> from transformers import Lfm3Model, Lfm3Config >>> # Initializing a LFM3 model >>> configuration = Lfm3Config() >>> # Initializing a model from the LFM3-1.7B style configuration >>> model = Lfm3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "lfm3" keys_to_ignore_at_inference = ["past_key_values"] default_theta = 1000000.0 vocab_size: int = 128_000 hidden_size: int = 2048 intermediate_size: int = 7680 num_hidden_layers: int = 24 num_attention_heads: int = 16 num_key_value_heads: int = 4 max_position_embeddings: int = 128_000 initializer_range: float = 0.02 norm_eps: float = 0.00001 use_cache: bool = True pad_token_id: int | None = 124_893 bos_token_id: int | None = 124_894 eos_token_id: int | list[int] | None = 124_900 tie_word_embeddings: bool = True rope_parameters: dict | None = None conv_bias: bool = False conv_L_cache: int = 7 layer_types: list[str] | None = None _attention_layer_types = ("full_attention", "sliding_attention") head_dim: int | None = None sliding_window: int | None = 256 use_sandwich_norm: bool = True def __post_init__(self, **kwargs): if self.head_dim is None: self.head_dim = self.hidden_size // self.num_attention_heads if self.rope_parameters is None: self.rope_parameters = { "full_attention": {"rope_type": "default", "rope_theta": 1_000_000.0, "partial_rotary_factor": 0.5}, "sliding_attention": {"rope_type": "default", "rope_theta": 10_000.0}, } if self.layer_types is None: self.layer_types = [ "full_attention" if i % 4 == 2 else "sliding_attention" if i % 4 == 0 and i > 0 else "conv" for i in range(self.num_hidden_layers) ] self.tie_word_embeddings = kwargs.pop("tie_embedding", self.tie_word_embeddings) super().__post_init__(**kwargs) def validate_architecture(self): """Part of ``@strict``-powered validation.""" if len(self.layer_types) != self.num_hidden_layers: raise ValueError( f"`layer_types` has {len(self.layer_types)} entries but `num_hidden_layers` is {self.num_hidden_layers}." ) allowed_layer_types = (*self._attention_layer_types, "conv") invalid_layer_types = [layer_type for layer_type in self.layer_types if layer_type not in allowed_layer_types] if invalid_layer_types: raise ValueError(f"`layer_types` entries must be one of {allowed_layer_types}; got {invalid_layer_types}.") if "sliding_attention" in self.layer_types and self.sliding_window is None: raise ValueError("`layer_types` has `sliding_attention` layers but `sliding_window` is None.") missing_rope = [ layer_type for layer_type in self._attention_layer_types if layer_type in self.layer_types and layer_type not in self.rope_parameters ] if missing_rope: raise ValueError(f"`rope_parameters` has no entry for the {missing_rope} layers in `layer_types`.") for layer_type, rope_params in self.rope_parameters.items(): if not isinstance(rope_params, dict): continue if ( rope_params.get("rope_type", "default") in ("dynamic", "longrope") and rope_params.get("partial_rotary_factor", 1.0) < 1 ): raise ValueError( f"`{rope_params['rope_type']}` RoPE recomputes its frequencies during the forward pass over the " f"rotated dimension only and cannot keep LFM3's partial rotation on `{layer_type}` layers." ) __all__ = ["Lfm3Config"]