a ½ Àh—ã@s2ddlmZddlmZGdd„deƒZdgZdS)é)ÚOptionalé)ÚPretrainedConfigcsleZdZdZdZdgZdeeeeeeeeeeeeeeeeeeeee e ee e e dœ‡fdd„ Z ‡Z S)Ú 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 ```Zlfm2Zpast_key_valueséé é0é ééôç{®Gáz”?çñh㈵øä>Tréé瀄.AFréçð?N)Ú vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚmax_position_embeddingsÚinitializer_rangeÚnorm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚ rope_thetaÚ conv_biasÚ conv_L_cacheÚblock_multiple_ofÚblock_ffn_dim_multiplierÚblock_auto_adjust_ff_dimÚfull_attn_idxsÚ layer_typesc sÜ||_||_||_| d|¡|_||_| |_| |_||_||_ ||_ ||_ ||_ | d|¡|_ ||_||_||_||_|jdur°ˆdurŒˆn tt|ƒƒ‰‡fdd„t|ƒDƒ|_| d|¡}tƒjf| | | |dœ|¤ŽdS)NÚthetaZ block_ff_dimcsg|]}|ˆvrdnd‘qS)Zfull_attentionÚconv©)Ú.0Úi©r'r+úg/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/lfm2/configuration_lfm2.pyÚ ™óz'Lfm2Config.__init__..Z tie_embedding)rrrr )rrrÚgetr!rrrrrrr"r#rr$r%r&r(ÚlistÚrangeÚsuperÚ__init__)Úselfrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(Úkwargs©Ú __class__r.r/r6es:  üûzLfm2Config.__init__)rrrr r r r r r TrrrTrFrrrTNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferenceÚintÚfloatÚboolrr3Ústrr6Ú __classcell__r+r+r9r/rsbNé  érN)ÚtypingrZconfiguration_utilsrrÚ__all__r+r+r+r/Ús