a ½ Àh½#ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zPersimmon model configurationé)ÚPretrainedConfig)Úrope_config_validation)Úloggingcs,eZdZdZdZdgZd‡fdd„ Z‡ZS)ÚPersimmonConfiga, This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an Persimmon 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 [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base). 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 262144): Vocabulary size of the Persimmon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PersimmonModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 16384): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 16384): 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. layer_norm_eps (`float`, *optional*, defaults to 1e-5): 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 25000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE qk_layernorm (`bool`, *optional*, default to `True`): Whether or not to normalize the Queries and Keys after projecting the hidden states hidden_dropout (`float`, *optional*, default to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, default to 0.0): The dropout ratio after computing the attention scores. partial_rotary_factor (`float`, *optional*, default to 0.5): Percentage of the query and keys which will have rotary embedding. Example: ```python >>> from transformers import PersimmonModel, PersimmonConfig >>> # Initializing a Persimmon persimmon-7b style configuration >>> configuration = PersimmonConfig() ```Z persimmonZpast_key_valuesééé@é$é@Úrelu2ç{®Gáz”?çñh㈵øä>TFçjØ@Nççà?ééc s¬||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_||_|j dur„d|j vr„|j d|j d<t|ƒtƒjf|||| dœ|¤ŽdS)NÚtypeZ rope_type)Ú pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddings)Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚ hidden_actÚinitializer_rangeÚlayer_norm_epsÚ use_cacheÚ rope_thetaÚ rope_scalingÚ qk_layernormÚhidden_dropoutÚattention_dropoutÚpartial_rotary_factorrÚsuperÚ__init__)Úselfrrrrrrrrr r!rr"r#r$r%r&r'rrrÚkwargs©Ú __class__©úq/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/persimmon/configuration_persimmon.pyr)ys6üûzPersimmonConfig.__init__)rrrr r r rr r TFrNTrrrNrr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferencer)Ú __classcell__r.r.r,r/rs0\ërN) r3Zconfiguration_utilsrZmodeling_rope_utilsrÚutilsrZ get_loggerr0ÚloggerrÚ__all__r.r.r.r/Ús