fTh;DSSKJrJrJrJr SSKJr SSKJr SSK J r SSK J r \ R"\5r"SS \5r"S S \5rS S /rg ) )AnyDictOptionalUnion)PretrainedConfig)rope_config_validation)logging)SiglipVisionConfigc^\rSrSrSrSrS/rSSSSSSSS.rS/S /4S S /S /4S /S /4S .rSU4S jjr Sr U=r $)Gemma3TextConfig!a This is the configuration class to store the configuration of a [`Gemma3TextModel`]. It is used to instantiate an Gemma3Text 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 Gemma3Text-7B. e.g. [google/gemma3_text-7b](https://huggingface.co/google/gemma3_text-7b) 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 262208): Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Gemma3TextModel`] hidden_size (`int`, *optional*, defaults to 2304): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 9216): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 26): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. 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://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 131072): 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 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *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. query_pre_attn_scalar (`float`, *optional*, defaults to 256): Scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in Gemma3Text, every other layer uses sliding window attention. This is the size of the sliding window. 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. cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings used in global attention. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE rope_local_base_freq (float, *optional*, defaults to 10000.0): The base period of the RoPE embeddings for local attention. sliding_window_pattern (`int`, *optional*, defaults to 6): Pattern for the sliding window attention. ```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 ``` rope_local_base_freq (float, *optional*, defaults to 10000.0): The base period of the RoPE embeddings for local attention. sliding_window_pattern (`int`, *optional*, defaults to 6): Pattern for the sliding window attention. gemma3_textpast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormc p>[TU]"SU UUUS.UD6 XlXlX lX0lX@lXPlXplX`l Xl Xl Xl UUl UUlUUlXlUUlUUlUUlUUlUUlUUlUUlUUl[3U5 g)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings)super__init__ vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headshead_dimnum_key_value_headsinitializer_range rms_norm_eps use_cache rope_thetaattention_biasattention_dropouthidden_activationquery_pre_attn_scalarsliding_windowfinal_logit_softcappingattn_logit_softcappingcache_implementationrope_local_base_freqsliding_window_pattern rope_scalingr )selfr#r%r&r'r(r*r)r1r$r+r,r-rrrrr.r/r0r2r3r4r5r6r9r7r8kwargs __class__s g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/gemma3/configuration_gemma3.pyr"Gemma3TextConfig.__init__s>  %%% 3    %'>$&!2!2#6  #6 !2("$,!2!2%:",'>$&<#$8!$8!&<#(t$)r/r0r5r6r4r)r1r%r+r&r$r(r'r*r2r,r7r9r.r3r8r-r#)i@i i$gelu_pytorch_tanhi{Gz?gư>Trr Tg.AFgrCiNNhybridNg@) __name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planr"__static_attributes__ __classcell__r<s@r=rr!swrJ#4"5%.%.%.%."+ )"+&(9:#%568IJ!"_$56- ' ! $#%% 9?%?%r?rc^\rSrSrSrSrSSSS.r\\S.r SS \ \ \\ \ \44S \ \ \\ \ \44S \S\S\S\S \4U4S jjjrSrU=r$) Gemma3Configa This is the configuration class to store the configuration of a [`Gemma3ForConditionalGeneration`]. It is used to instantiate an Gemma3ForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PaliGemma-2B. e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[Gemma3TextConfig, dict]`, *optional*): The config object of the text backbone. vision_config (`Union[AutoConfig, dict]`, *optional*): Custom vision config or dict. 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. image_token_index (`int`, *optional*, defaults to 262144): The image token index to encode the image prompt. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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 ```gemma3image_token_indexboi_token_indexeoi_token_index)image_token_id boi_token_id eoi_token_id) text_config vision_configr_r`mm_tokens_per_imager+c >Uc [5n[RS5 O [U[5(a [S0UD6n[U[5(a [ S0UD6nO"Uc[ 5n[RS5 XlX lX0lX@l XPl X`l Xpl [T U]8"S0UD6 g)Nz@text_config is None, using default Gemma3TextConfig text config.zFvision_config is None, using default SiglipVisionConfig vision config.r )rloggerinfo isinstancedictr r_r`rarZr[rYr+r!r") r:r_r`rarZr[rYr+r;r<s r=r"Gemma3Config.__init__*s  *,K KKZ [  T * **9[9K mT * *.??M  ".0M KK` a&*#6 ..!2!2 "6"r?)rZr[rYr+rar_r`)NNrCiiirE)rIrJrKrLrMrN attribute_maprr sub_configsrrrstrrintfloatr"rRrSrTs@r=rVrVs. `J-))M (+KJNMQ#&&&!(#'#e$4d38n$DEF# &8$sCx.&H IJ#! #  #  ##!##r?rVN)typingrrrrconfiguration_utilsrmodeling_rope_utilsr utilsr siglipr get_loggerrIrcrrV__all__r r?r=rtsX,.-39'   H %J%'J%Z[##[#| - .r?