o ¾e¦i„ã@s6dZddlmZddlmZGdd„deƒZdgZdS)zDiffLlama model configurationé)ÚPreTrainedConfig)ÚRopeParametersc*seZdZdZdZdgZ             d)ded Bded Bded Bded Bded Bded Bded Bded Bded Bded Bde d Bded Bded Bd ed Bd!e d Bd"e e ee fBd Bd#e d Bd$ed Bd%ed Bd&ed Bf(‡fd'd(„ Z ‡Z S)*ÚDiffLlamaConfiga This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama 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 [kajuma/DiffLlama-0.3B-handcut](https://huggingface.co/kajuma/DiffLlama-0.3B-handcut). 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 DiffLlama model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DiffLlamaModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 16): 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*): 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`. 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. 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-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*): 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 `False`): 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`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. lambda_std_dev (`float`, *optional*, defaults to 0.1): The standard deviation for initialization of parameter lambda in attention layer. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_heads ```python >>> from transformers import DiffLlamaModel, DiffLlamaConfig >>> # Initializing a DiffLlama diffllama-7b style configuration >>> configuration = DiffLlamaConfig() >>> # Initializing a model from the diffllama-7b style configuration >>> model = DiffLlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Ú diffllamaÚpast_key_valuesé}éé éé NÚsiluç{®Gáz”?çñh㈵øä>TééFççš™™™™™¹?Ú vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚlambda_std_devÚhead_dimc s®||_||_||_||_||_||_|dur|}||_||_| |_| |_ | |_ ||_ ||_ ||_ |dur6|n|j|j|_||_||_| |_| |_||_tƒjdi|¤ŽdS)N©)rrrrrrrrrrrr#r$r%r&r"r!rrr ÚsuperÚ__init__)Úselfrrrrrrrrrrrrrr r!r"r#r$r%r&Úkwargs©Ú __class__r'ús/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/diffllama/configuration_diffllama.pyr)es.zDiffLlamaConfig.__init__)rrr r r Nr rr rTNrrFNFrrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚintÚstrÚfloatÚboolrÚdictr)Ú __classcell__r'r'r,r.rs‚Jëþýüûúùø ÷ ö õ ô óòñðïîíìërN)r2Úconfiguration_utilsrÚmodeling_rope_utilsrrÚ__all__r'r'r'r.Ús