qi 8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc.XeZdZdZdZdgZdZ ddedzdedzdedzd edzd edzd edzd edzd edzdedzde dzdedzdedzdedzde dzde e e e fzdzde dzdedzdedzdedzde dzde edzde e dzf,fd ZxZS) Lfm2ConfigaT This is the configuration class to store the configuration of a [`Lfm2Model`]. It is used to instantiate a LFM2 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 LFM2-1.2B model. e.g. [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B) 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 65536): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Lfm2Model`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): 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*, defaults to 8): 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. Lfm2 1 supports up to 2048 tokens, Lfm2 2 up to 4096, CodeLfm2 up to 16384. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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`. pad_token_id (`int`, *optional*, defaults to 0): 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 `True`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the conv layers. conv_L_cache (`int`, *optional*, defaults to 3): L_cache dim in the conv layers. block_multiple_of (`int`, *optional*, defaults to 256): Multiple for the `intermediate_size`. block_ffn_dim_multiplier (`float`, *optional*, defaults to 1.0): Multiplier for the `intermediate_size`. block_auto_adjust_ff_dim (`bool`, *optional*, defaults to `True`): Whether to adjust the dim of the `intermediate_size`. full_attn_idxs (`Optional`, *optional*): Index of the layers which use attention. layer_types (`Optional`, *optional*): Type of each layers. ```python >>> from transformers import Lfm2Model, Lfm2Config >>> # Initializing a LFM2 model >>> configuration = Lfm2Config() >>> # Initializing a model from the LFM2-1.2B style configuration >>> model = Lfm2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```lfm2past_key_valuesg.AN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_headsmax_position_embeddingsinitializer_rangenorm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parameters conv_bias conv_L_cacheblock_multiple_ofblock_ffn_dim_multiplierblock_auto_adjust_ff_dimfull_attn_idxs layer_typesc (||_||_||_||_| |_| |_||_||_||_||_ ||_ |jd||_ ||_ ||_||_||_|j <||nt#t%|}t%|Dcgc] }||vrdnd c}|_||_|jd|}||_| |_| |_| |_t1|ddi|ycc}w)N block_ff_dimfull_attentionconv tie_embedding)r r r rrrrr rrrgetr rrrrlistrangerrrrrsuper__init__)selfr r r r r rrrrrrrrrrrrrrrrrkwargsi __class__s ]/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/lfm2/configuration_lfm2.pyr)zLfm2Config.__init__hs.4%&!2'>$"  !2$7 #6 #("(NTTNFrg?TNN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference default_thetaintfloatboolrdictstrr&r) __classcell__)r-s@r.rrsN `J#4"5M"'"&(-(**,*+.5*.!(!%#$#$#$+/MQ!&#$(+1404+/(,/;#$J;#4Z;#: ;# : ;# !4Z ;#!4Z;#"%t;#!4<;#$,;#$;;#Dj;#Dj;#Dj;#"D[;# ($sN/B*CCdJ!;#"$;#;#$Dj%;#&:';#(#($,);#*#'++;#,S D(-;#.#Y%/;#;#rN)configuration_utilsrmodeling_rope_utilsrr__all__r$rBr.rFs&41P#!P#f .rB