fTh hSrSSKrSSKJr SSKJr \R "\5r"SS\5r S/r g)zMAMBA2 configurationN)PretrainedConfig)loggingc|^\rSrSrSrSrSSSSSSSS S S S S S SSSSSSSSSS\"S54SSSSS4U4SjjrSrU=r $) Mamba2ConfigaP This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2 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 MAMBA2 [state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_heads (`int`, *optional*, defaults to 128): Number of heads for the evolution matrices of mamba 2. head_dim (`int`, *optional*, defaults to 64): Dimension of each head. vocab_size (`int`, *optional*, defaults to 32768): Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Mamba2Model`]. hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. state_size (`int`, *optional*, defaults to 128): shape of the state space latents. num_hidden_layers (`int`, *optional*, defaults to 64): Number of hidden layers in the model. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 0): The id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 2): The id of the end of sentence token in the vocabulary. expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel. n_groups (`int`, *optional*, defaults to 8): Number of groups for the evolution matrices of mamba 2. use_bias (`bool`, *optional*, defaults to `False`): Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block use_conv_bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the convolution layer of the mixer block. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. initializer_range (`float`, *optional*, defaults to 0.1): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. residual_in_fp32 (`bool`, *optional*, defaults to `True`): Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`): Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)` time_step_min (`float`, *optional*, defaults to 0.001): Minimum `time_step` used to bound `dt_proj.bias`. time_step_max (`float`, *optional*, defaults to 0.1): Maximum `time_step` used to bound `dt_proj.bias`. time_step_floor (`float`, *optional*, defaults to 0.0001): Minimum clamping value of the `dt_proj.bias` layer initialization. time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`): Accepted range of time step values. rescale_prenorm_residual (`bool`, *optional*, defaults to `False`): Whether or not to rescale `out_proj` weights when initializing. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the cache should be used. rms_norm (`bool`, *optional*, defaults to `True`): Whether to use RMS norm or not. chunk_size (`int`, *optional*, defaults to 256): Size of the chunks that will comprise the sequence. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie word embeddings or not. Example: ```python >>> from transformers import Mamba2Config, Mamba2Model >>> # Initializing a Mamba2 configuration >>> configuration = Mamba2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = Mamba2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```mamba2@iigh㈵>rFTsilug?autogMbP?g-C6?ginfc 8>XK-X-:wa[SXK-SX-S35eX0lX@lXPlX`lXplXlXlXlXl Xl Xl Xl UUl UUlUS:Xa#[R "URS- 5OUUlUUlUUlUUlUUlUUlUUlXlXlX lUUlXPlUUlUUlUUl[>TU]"SU U UUS.UD6 g)Nz2Inconsistent configuration: hidden_size * expand (z#) must equal num_heads * head_dim (z).r) bos_token_id eos_token_id pad_token_idtie_word_embeddings)! ValueError vocab_size hidden_size state_sizenum_hidden_layerslayer_norm_epsilon conv_kernelexpandrrruse_bias use_conv_bias hidden_actinitializer_rangemathceiltime_step_rank time_step_min time_step_maxtime_step_floorrescale_prenorm_residualresidual_in_fp32 use_cachen_groups num_headshead_dimrms_norm chunk_sizetime_step_limitrsuper__init__)selfr1r2rrrrr rrrr"r!r0r#r$r%r&r.r)r*r+r,r5r-r/r3r4rkwargs __class__s g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/mamba2/configuration_mamba2.pyr7Mamba2Config.__init__osI@  i&: ;()*()-  %&$!2"4& ((( *$!2BPTZBZdii(8(82(=>`n**.(@% 0" "   $$.#6   %%% 3    )rr4r!rr"r2r%rr&r r0r1rrr-r.r3rrr,r5r+r*r)r#r/r$r) __name__ __module__ __qualname____firstlineno____doc__ model_typefloatr7__static_attributes__ __classcell__)r:s@r;rrs|P dJeEl+!&!;L L r=r) rBr'configuration_utilsrutilsr get_loggerr>loggerr__all__rr=r;rLs? 3   H %a #a H  r=