U ,-e $@sDdZddlmZddlmZeeZdddZGdddeZ d S) z Falcon configuration)PretrainedConfig)loggingzAhttps://huggingface.co/tiiuae/falcon-40b/resolve/main/config.jsonz@https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json)ztiiuae/falcon-40bztiiuae/falcon-7bcsLeZdZdZdZdgZdfdd ZeddZeddZ ddZ Z S) FalconConfiga This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon 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 [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture. 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 65024): Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FalconModel`] hidden_size (`int`, *optional*, defaults to 4544): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 71): Number of attention heads for each attention layer in the Transformer encoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for MLP layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for attention layers. num_kv_heads (`int`, *optional*): Number of key-value heads to use per attention layer. If unset, defaults to the same value as `num_attention_heads`. alibi (`bool`, *optional*, defaults to `False`): Whether to use ALiBi positional biases during self-attention. new_decoder_architecture (`bool`, *optional*, defaults to `False`): Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn` arguments are ignored, as the new decoder always uses parallel attention. multi_query (`bool`, *optional*, defaults to `True`): Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`. parallel_attn (`bool`, *optional*, defaults to `True`): Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`. bias (`bool`, *optional*, defaults to `False`): Whether to use bias on Linear layers. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained Falcon models with RoPE support up to 2048 tokens. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. bos_token_id (`int`, *optional*, defaults to 11): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 11): The id of the "end-of-sequence" token. Example: ```pytho >>> from transformers import FalconModel, FalconConfig >>> # Initializing a small (2-layer) Falcon configuration >>> configuration = FalconConfig(num_hidden_layers=2) >>> # Initializing a model from the small configuration >>> model = FalconModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZfalconZpast_key_values Gh㈵>{Gz?TNF@ c s||_|dd}|dkr|n||_||_||_||_||_||_||_| |_ ||_ ||_ | dkrf|n| |_ | |_ | |_| |_||_||_||_||_||_|tjf||d|dS)Nn_embed) bos_token_id eos_token_id) vocab_sizepop hidden_sizenum_hidden_layersnum_attention_headslayer_norm_epsiloninitializer_range use_cachehidden_dropoutattention_dropoutrr num_kv_headsalibinew_decoder_architecture multi_query parallel_attnbiasmax_position_embeddings rope_theta rope_scaling_rope_scaling_validationsuper__init__)selfrrrrrrrrrrrrrr r!r"r#r$rrkwargsr __class__p/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/falcon/configuration_falcon.pyr'ns. zFalconConfig.__init__cCs |j|jSN)rrr(r,r,r-head_dimszFalconConfig.head_dimcCs|j Sr.)rr/r,r,r-rotaryszFalconConfig.rotarycCs|jdkrdS|jrtdt|jtr6t|jdkrFtd|j|jdd}|jdd}|dksr|dkrtd||dkst|tr|d krtd |dS) z< Validate the `rope_scaling` configuration. Nz7`rope_scaling` is not supported when `alibi` is `True`.zS`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got typefactor)ZlinearZdynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z8`rope_scaling`'s factor field must be an float > 1, got )r$r1 ValueError isinstancedictlengetfloat)r(Zrope_scaling_typeZrope_scaling_factorr,r,r-r%s   z%FalconConfig._rope_scaling_validation)rrrrr r Tr r NFFTTFr r Nrr) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferencer'propertyr0r1r% __classcell__r,r,r*r-rs:N3  rN) r>Zconfiguration_utilsrutilsrZ get_loggerr;loggerZ$FALCON_PRETRAINED_CONFIG_ARCHIVE_MAPrr,r,r,r-s