`i@ddlmZddlmZGddeZdgZdS))Optional)PretrainedConfigc/eZdZdZdZdgZ d-dedededededededededed ed!ed"ed#ed$ed%ed&ed'ed(ed)ed*e e ed+e e e f,fd, Z xZ S). Lfm2Configa{ 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_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE 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_values 0 {Gz?h㈵>Tr.AFr?N 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_embeddings rope_theta conv_bias conv_L_cacheblock_multiple_ofblock_ffn_dim_multiplierblock_auto_adjust_ff_dimfull_attn_idxs layer_typesc >||_||_||_|d||_||_| |_| |_||_||_ ||_ ||_ ||_ |d||_ ||_||_||_||_|j@nt%t'|fdt'|D|_|d|}t)jd| | | |d|dS)Ntheta block_ff_dimc g|] }|vrdnd S)full_attentionconv).0ir+s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/lfm2/configuration_lfm2.py z'Lfm2Config.__init__..s*vvvXYA4G4G 0 0Vvvv tie_embedding)r!r"r#r$r3)rrrgetr%rr rrrrr&r'rr(r)r*r,listrangesuper__init__)selfrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,kwargs __class__s ` r6r>zLfm2Config.__init__esU4%&!2 **Wj99'>$"  !2$7 #6 #("(N __classcell__)rAs@r6rrsL L \J#4"5 !&!##%#$'.#'!$(%!$*-)-.2+//= = = =  =  = ! = != "%= != = = = = = "= != "#= $%= &'= (#()= *#'+= ,!c+-= .d3i(/= = = = = = = = = = r8rN)typingrconfiguration_utilsrr__all__r3r8r6rPsl333333O O O O O !O O O d .r8