U 9%e"@s>dZddlmZddlmZeeZiZGdddeZ dS)z LLaMA model configuration)PretrainedConfig)loggingcs4eZdZdZdZdgZdfdd ZddZZS) LlamaConfiga) This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA 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 LLaMA-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 LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. 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=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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. pretraining_tp (`int`, *optional*, defaults to `1`): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. 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-12): 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`. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings 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. attention_bias (`bool`, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. Example: ```python >>> from transformers import LlamaModel, LlamaConfig >>> # Initializing a LLaMA llama-7b style configuration >>> configuration = LlamaConfig() >>> # Initializing a model from the llama-7b style configuration >>> model = LlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```llamaZpast_key_values}+ Nsilu{Gz?ư>TF@c s||_||_||_||_||_||_|dkr0|}||_||_| |_| |_ ||_ | |_ ||_ ||_ |||_tjf| | ||d|dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_epspretraining_tp use_cache rope_theta rope_scaling_rope_scaling_validationattention_biassuper__init__)selfrrrrrrrrrrr rrrrrr!r"r$kwargs __class__l/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/llama/configuration_llama.pyr&ns4zLlamaConfig.__init__cCs|jdkrdSt|jtr(t|jdkr8td|j|jdd}|jdd}|dksd|dkrrtd||dkst|tr|dkrtd |dS) z< Validate the `rope_scaling` configuration. NrzS`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got typefactor)ZlinearZdynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z8`rope_scaling`'s factor field must be an float > 1, got )r" isinstancedictlen ValueErrorgetfloat)r'Zrope_scaling_typeZrope_scaling_factorr+r+r,r#s  z$LlamaConfig._rope_scaling_validation)rrrr r Nr r r r TNrrrFrNF) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferencer&r# __classcell__r+r+r)r,rs0K5rN) r8Zconfiguration_utilsrutilsrZ get_loggerr5loggerZ#LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAPrr+r+r+r,s