§ `ƒi¾1ãó4—ddlmZGd„de¦«ZdgZdS)é)ÚPretrainedConfigcó~‡—eZdZdZdZddiZdgZ dˆfd„ ZˆxZS) Ú Zamba2Configas This is the configuration class to store the configuration of a [`Zamba2Model`]. It is used to instantiate a Zamba2 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 Zamba2 model. [Zyphra/Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B) 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 32000): Vocabulary size of the Zamba2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Zamba2Model`] max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 54): Number of hidden layers in the model. layers_block_type (`list`, *optional*): List of layer types, which can be either "mamba" or "hybrid". mamba_d_state (`int`, *optional*, defaults to 64): shape of the state space latents. mamba_d_conv (`int`, *optional*, defaults to 4): Size of the convolution kernel. mamba_expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. mamba_ngroups (`int`, *optional*, defaults to 1): Number of groups for the evolution matrices of mamba 2. 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*): Accepted range of time step values. n_mamba_heads (`int`, *optional*, defaults to 8): Number of heads for the evolution matrices of mamba 2. use_conv_bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the convolution layer of the mixer block. chunk_size (`int`, *optional*, defaults to 256): Size of the chunks that will comprise the sequence. use_mem_eff_path (`bool`, *optional*, defaults to `False`): Whether or not to use the fused conv1d and scan in mamba2 layers. add_bias_linear (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in various layers intermediate_size (`int`, *optional*, defaults to 4 * hidden_size): Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the MLP. 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=None`, 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, check out [this paper](https://huggingface.co/papers/2305.13245). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_mem_blocks (`int`, *optional*, defaults to 1): Number of unshared transformer blocks. use_shared_attention_adapter (`bool`, *optional*, defaults to `False`): If True, unshared adapters (formally the same as LoRA but used in the base model) will be added to the q, k, v projectors in the shared attention layers. adapter_rank (`int`, *optional*, defaults to 128): Rank of the adapter in the shared MLP and shared attention layers. use_mem_rope (`bool`, *optional*, defaults to `False`): If True, includes RoPE in the shared attention layers. rope_theta (`float`, *optional*, defaults to `10000.0`): The base period of the RoPE embeddings. 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-05): 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`. num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly. pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. use_long_context (`bool`, *optional*, defaults to `False`): Activates the context-extended version of Zamba by modifying RoPE. ```python >>> from transformers import Zamba2Model, Zamba2Config >>> # Initializing a Zamba2-2.7B style configuration >>> configuration = Zamba2Config() >>> # Initializing a model from the Zamba2-2.7B style configuration >>> model = Zamba2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Úzamba2Úhead_dimÚattention_head_dimÚpast_key_valuesé}éé é6Né@éééçü©ñÒMbP?çš™™™™™¹?ç-Cëâ6?éTéFÚgelué çé€é'ç{®Gáz”?çñh㈵øä>éc% óö•—t¦«jd |!|"|#dœ|%¤Ž||_||_||_|€ d|z|_n||_||_||_||_||_ d|z|_ d|jz|jz|_ ||_ ||_ |$|_|r|$rd}&||&|j |j dz z zz}||_||_||_||_||_| |_||_t-||z¦«|z|_||_||_| |_||_||_| |_| |_| |_|$rd|_|€|}||_ ||_|j|jz|_!|j|_"|€4dgdgdzdgzd zzdgdzzdgzdgd zzdgzdgdzz|_#n||_#||_$||_%||_&| |_'d „tQ|j#¦«D¦«|_)||_*dS) N)Ú pad_token_idÚ bos_token_idÚ eos_token_idrrri@ÚmambaéÚhybridércó$—g|] \}}|dk¯ |‘ŒS)r%©)Ú.0ÚindexÚtypes úƒ/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/zamba2/configuration_zamba2.pyú z)Zamba2Config.__init__..ís(€Ð pÐ pÐ p©;¨5°$Ð_cÐgoÒ_oÐ_o Ð_oÐ_oÐ_oór()+ÚsuperÚ__init__Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚintermediate_sizeÚ hidden_actÚnum_hidden_layersÚnum_attention_headsÚnum_mem_blocksÚattention_hidden_sizerÚattention_dropoutÚ use_mem_ropeÚuse_long_contextÚ rope_thetaÚ mamba_d_stateÚ mamba_d_convÚ mamba_expandÚadd_bias_linearÚ mamba_ngroupsÚ n_mamba_headsÚintÚ mamba_headdimÚ use_conv_biasÚ chunk_sizeÚtime_step_limitÚuse_shared_attention_adapterÚ adapter_rankÚ time_step_minÚ time_step_maxÚtime_step_floorÚnum_key_value_headsÚ kv_channelsÚnum_query_groupsÚlayers_block_typeÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚnum_logits_to_keepÚ enumerateÚhybrid_layer_idsÚuse_mem_eff_path)(Úselfr1r2r3r6rQr>r?r@rBrKrLrMrHrCrFrGrXrAr4r5r7rNr:r8rIrJr;r=rRrSrTrUr r!r"r<ÚkwargsÚaÚ __class__s( €r,r0zZamba2Config.__init__ƒs®ø€ðP ‰ŒÔð Ø%Ø%Ø%ð ð ðð  ð ð ð %ˆŒØ'>ˆÔ$Ø&ˆÔØ Ð $Ø%&¨¡_ˆDÔ "Ð "à%6ˆDÔ "Ø$ˆŒØ!2ˆÔØ#6ˆÔ Ø,ˆÔØ%&¨¡_ˆÔ"Ø"# dÔ&6Ñ"6¸$Ô:RÑ"RˆÔØ!2ˆÔØ(ˆÔØ 0ˆÔØ ð eÐ,ð e؈AØ# a¨DÔ,CÀtÔG^ÐabÑGbÑ,cÑ&dÑdˆJØ$ˆŒØ*ˆÔØ(ˆÔØ(ˆÔØ.ˆÔØ*ˆÔØ*ˆÔÝ  ° Ñ!;Ñ<Ô<À ÑMˆÔØ*ˆÔØ$ˆŒØ.ˆÔØ,HˆÔ)Ø(ˆÔØ*ˆÔØ*ˆÔØ.ˆÔØ ð 1Ø+0ˆDÔ (Ø Ð &Ø"5Ð Ø#6ˆÔ Ø#6ˆÔ ØÔ+¨tÔ/GÑGˆÔØ $Ô 8ˆÔà Ð $à Ø9˜q‘= H :Ñ-°Ñ2ñ3à)˜a‘-ñ ð*ñð)˜a‘-ñ  ð *ñ ð )˜a‘-ñ  ð Ô "Ð "ð&7ˆDÔ "Ø!2ˆÔØ(ˆÔØ"ˆŒØ"4ˆÔØ pÐ p½)ÀDÔDZÑ:[Ô:[Ð pÑ pÔ pˆÔØ 0ˆÔÐÐr.)$r r r r NrrrrrrrNrTrFFNrrNrrFrFrrrTrrrrF) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚ attribute_mapÚkeys_to_ignore_at_inferencer0Ú __classcell__)r\s@r,rrsÞø€€€€€ðb ðb ðH€JØÐ!5Ð6€MØ#4Ð"5ÐðØ $ØØØØØØØØØØØØØØØØØØØØ ØØØ%*ØØØØØØØØØØØðKk1ðk1ðk1ðk1ðk1ðk1ðk1ðk1ðk1ðk1r.rN)Úconfiguration_utilsrrÚ__all__r(r.r,úrgs[ðð.4Ð3Ð3Ð3Ð3Ð3ðT1ðT1ðT1ðT1ðT1Ð#ñT1ôT1ðT1ðn Ð €€€r.