fTh 1lSrSSKJr SSKJr SSKJr \R"\5r "SS\5r S/r g)z$GraniteMoeHybrid model configuration)PretrainedConfig)rope_config_validation)loggingc^\rSrSrSrSrSS0rS/rS U4Sjjr\ S5r S r U=r $) GraniteMoeHybridConfiga@ This is the configuration class to store the configuration of a [`GraniteMoeHybridConfig`]. It is used to instantiate an GraniteMoeHybrid model according to the specified arguments, defining the model architecture. 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 32000): Vocabulary size of the GraniteMoeHybrid model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GraniteMoeHybridModel`] hidden_size (`int`, *optional*, defaults to 4096): 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 32): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). 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 2048): 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*): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. 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. embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier. logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits. residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier. attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier. num_local_experts (`int`, *optional*, defaults to 8): total number of experts. num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxialiary loss coefficient shared_intermediate_size (`int`, *optional*, defaults to 1024): intermediate size for shared experts. position_embedding_type (`str`, *optional*): Positional embedding type to be used; defaults to None. Allowed options: `[None, "rope"]` layer_types (`List`, *optional*): list of strings to be used as layer types. Allowed choices: "mamba", "attention". mamba_n_heads (`int`, *optional*, defaults to 128): The number of mamba heads used. mamba_n_groups (`int`, *optional*, defaults to 1): The number of the mamba groups used. mamba_d_state (`int`, *optional*, defaults to 256): The dimension the mamba latent state space. mamba_d_head (`int`, *optional*, defaults to `"auto"`): Head embedding dimension size. mamba_d_conv (`int`, *optional*, defaults to 4): The size of the mamba convolution kernel. mamba_expand (`int`, *optional*, defaults to 2): Expanding factor (relative to hidden_size) used to determine the mamba intermediate size. mamba_chunk_size (`int`, *optional*, defaults to 256): The chunks in which to break the sequence when doing prefill/training. mamba_conv_bias (`bool`, *optional*, defaults to `True`): Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block. mamba_proj_bias (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block. ```python >>> from transformers import GraniteMoeHybridModel, GraniteMoeHybridConfig >>> # Initializing a GraniteMoeHybrid config >>> configuration = GraniteMoeHybridConfig() >>> # Accessing the model configuration >>> configuration = model.config ```granitemoehybridlayers_block_type layer_typespast_key_valuesc( >XlXlX lX0lX@lXPlUcUnX`lXplXlXl Xl UUl UUl UUl UUlUUlUUlUUlUUlUUlUUlUUlUUlUUlUUlU$U-n)Ub"[3SU55(a [5S5eU)U-S:wa [5S5eU"S:XaU)U-n"U"U-U):wa [5S5eUUlU"UlU UlU!UlU#UlU%Ul U&Ul!U'Ul"U$Ul#UUl$[JT*U]"S U U UUS.U(D6 UR0S:Xa [OU5 gg) Nc3*# UH oS;v M g7f))mamba attentionN).0 layer_types {/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/granitemoehybrid/configuration_granitemoehybrid.py 2GraniteMoeHybridConfig.__init__..s*rfqXb=S+Sfqszr?r@kwargsmamba_intermediate __class__s+ rrCGraniteMoeHybridConfig.__init__sV%'>$&!2!2#6   &"5 #6 $!2("$(,$8!,#6 $8!!2!2#6 $8!$8!(@%'>$)K7  "s*rfq*r'r'r[\ \  - 2ST T 6 !->L - '+= =op p*(,*( 0..(&  %%% 3      ' '6 1 "4 ( 2c\UR(a UR$S/UR-$)Nr)r r")rDs rr (GraniteMoeHybridConfig.layers_block_types(#'#3#3t['TE[E[9[[rI)#r+r0r/r,r%r r&r!r r-r>r?r=r:r<rAr;r9r@rr#r2r"r$r1r3r6r.r'r*r)r4r5r(r)'i}ii+ rLNsiluig{Gz?gư>TNFg@NFg?rPrPrPrOFgMbP?iNNrNrrOrSTF) __name__ __module__ __qualname____firstlineno____doc__ model_type attribute_mapkeys_to_ignore_at_inferencerCpropertyr __static_attributes__ __classcell__)rGs@rrrsh T$J]M$5"5  $!  ""!% $Qm)`\\rIrN) rYconfiguration_utilsrmodeling_rope_utilsrutilsr get_loggerrUloggerr__all__rrIrrfsA +39   H %c\-c\L $ $rI