# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/gemma3/modular_gemma3.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_gemma3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Google Inc. 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 typing import Any from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring, logging from ..siglip import SiglipVisionConfig logger = logging.get_logger(__name__) @auto_docstring(checkpoint="google/gemma-3-4b-it") @strict class Gemma3TextConfig(PreTrainedConfig): r""" query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores final_logit_softcapping (`float`, *optional*): Scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*): Scaling factor when applying tanh softcapping on the attention scores. use_bidirectional_attention (`bool`, *optional*, defaults to `False`): If True, the model will attend to all text tokens instead of using a causal mask. This does not change behavior for vision tokens. ```python >>> from transformers import Gemma3TextModel, Gemma3TextConfig >>> # Initializing a Gemma3Text gemma3_text-7b style configuration >>> configuration = Gemma3TextConfig() >>> # Initializing a model from the gemma3_text-7b style configuration >>> model = Gemma3TextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "gemma3_text" keys_to_ignore_at_inference = ["past_key_values"] 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.q_norm": "replicated_with_grad_allreduce", "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce", "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"]), } vocab_size: int = 262_208 hidden_size: int = 2304 intermediate_size: int = 9216 num_hidden_layers: int = 26 num_attention_heads: int = 8 num_key_value_heads: int = 4 head_dim: int = 256 hidden_activation: str = "gelu_pytorch_tanh" max_position_embeddings: int = 131_072 initializer_range: float = 0.02 rms_norm_eps: float = 1e-6 use_cache: bool = True pad_token_id: int | None = 0 eos_token_id: int | list[int] | None = 1 bos_token_id: int | None = 2 tie_word_embeddings: bool = True rope_parameters: dict | None = None attention_bias: bool = False attention_dropout: int | float | None = 0.0 query_pre_attn_scalar: int = 256 sliding_window: int | None = 4096 layer_types: list[str] | None = None final_logit_softcapping: float | None = None attn_logit_softcapping: float | None = None use_bidirectional_attention: bool | None = False default_theta = {"global": 1_000_000.0, "local": 10_000.0} def __post_init__(self, **kwargs): if self.use_bidirectional_attention: self.sliding_window = (self.sliding_window // 2) + 1 # due to fa we set exclusive bounds # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6) if self.layer_types is None: self.layer_types = [ "sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention" for i in range(self.num_hidden_layers) ] super().__post_init__(**kwargs) def validate_architecture(self): """Part of `@strict`-powered validation. Validates the architecture of the config.""" if self.hidden_size % self.num_attention_heads != 0: raise ValueError( f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " f"heads ({self.num_attention_heads})." ) def convert_rope_params_to_dict(self, **kwargs): rope_scaling = kwargs.pop("rope_scaling", None) # Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters` # as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format default_rope_params = { "sliding_attention": {"rope_type": "default"}, "full_attention": {"rope_type": "default"}, } self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params if rope_scaling is not None: self.rope_parameters["full_attention"].update(rope_scaling) # Set default values if not present if self.rope_parameters.get("full_attention") is None: self.rope_parameters["full_attention"] = {"rope_type": "default"} self.rope_parameters["full_attention"].setdefault( "rope_theta", kwargs.pop("rope_theta", self.default_theta["global"]) ) if self.rope_parameters.get("sliding_attention") is None: self.rope_parameters["sliding_attention"] = {"rope_type": "default"} self.rope_parameters["sliding_attention"].setdefault( "rope_theta", kwargs.pop("rope_local_base_freq", self.default_theta["local"]) ) # Standardize and validate the correctness of rotary position embeddings parameters self.standardize_rope_params() return kwargs @auto_docstring(checkpoint="google/gemma-3-4b-it") @strict class Gemma3Config(PreTrainedConfig): r""" mm_tokens_per_image (`int`, *optional*, defaults to 256): The number of tokens per image embedding. boi_token_index (`int`, *optional*, defaults to 255999): The begin-of-image token index to wrap the image prompt. eoi_token_index (`int`, *optional*, defaults to 256000): The end-of-image token index to wrap the image prompt. Example: ```python >>> from transformers import Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig >>> # Initializing a Siglip-like vision config >>> vision_config = SiglipVisionConfig() >>> # Initializing a Gemma3 Text config >>> text_config = Gemma3TextConfig() >>> # Initializing a Gemma3 gemma-3-4b style configuration >>> configuration = Gemma3Config(vision_config, text_config) >>> # Initializing a model from the gemma-3-4b style configuration >>> model = Gemma3TextConfig(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gemma3" attribute_map = { "image_token_id": "image_token_index", "boi_token_id": "boi_token_index", "eoi_token_id": "eoi_token_index", } sub_configs = { "text_config": Gemma3TextConfig, "vision_config": SiglipVisionConfig, } text_config: Gemma3TextConfig | dict[str, Any] | None = None vision_config: SiglipVisionConfig | dict[str, Any] | None = None mm_tokens_per_image: int | None = 256 boi_token_index: int | None = 255_999 eoi_token_index: int | None = 256_000 image_token_index: int | None = 262_144 initializer_range: float | None = 0.02 tie_word_embeddings: bool | None = True def __post_init__(self, **kwargs): if self.text_config is None: self.text_config = Gemma3TextConfig() logger.info("text_config is None, using default Gemma3TextConfig text config.") elif isinstance(self.text_config, dict): self.text_config = Gemma3TextConfig(**self.text_config) if isinstance(self.vision_config, dict): self.vision_config = SiglipVisionConfig(**self.vision_config) elif self.vision_config is None: self.vision_config = SiglipVisionConfig() logger.info("vision_config is None, using default SiglipVisionConfig vision config.") super().__post_init__(**kwargs) __all__ = ["Gemma3Config", "Gemma3TextConfig"]