U 5Afj"@s:dZddlmZddlmZeeZGdddeZdS)zOLMo model configuration)PretrainedConfig)loggingcs4eZdZdZdZdgZdfdd ZddZZS) OlmoConfiga This is the configuration class to store the configuration of a [`OlmoModel`]. It is used to instantiate an OLMo 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 [allenai/OLMo-7B-hf](https://huggingface.co/allenai/OLMo-7B-hf). 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 50304): Vocabulary size of the OLMo model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OlmoModel`] 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 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). 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. 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 1): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 50279): End of stream token id. 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 a 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`, *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. clip_qkv (`float`, *optional*): If not `None`, elements of query, key and value attention states are clipped so that their absolute value does not exceed this value. ```python >>> from transformers import OlmoModel, OlmoConfig >>> # Initializing a OLMo 7B style configuration >>> configuration = OlmoConfig() >>> # Initializing a model from the OLMo 7B style configuration >>> model = OlmoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZolmoZpast_key_values+ Nsilu{Gz?TgF@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 use_cache rope_theta rope_scaling_rope_scaling_validationattention_biasattention_dropoutclip_qkvsuper__init__)selfrrrrrrrrrrrrrrrrr!r"r#kwargs __class__O/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/olmo/configuration_olmo.pyr%ns4zOlmoConfig.__init__cCs|jdkrdSt|jtr(t|jdkr8td|j|jdd}|jdd}|dksd|dkrrtd||dkst|tr|dkrtd |dS) z< Validate the `rope_scaling` configuration. NzN`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got typeZfactor)ZlinearZdynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z7`rope_scaling`'s factor field must be a float > 1, got )r isinstancedictlen ValueErrorgetfloat)r&Zrope_scaling_typeZrope_scaling_factorr*r*r+r s  z#OlmoConfig._rope_scaling_validation)rrrrrNr r r Tr Nr FrNFrN) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferencer%r __classcell__r*r*r(r+rs0M5rN) r7Zconfiguration_utilsrutilsrZ get_loggerr4loggerrr*r*r*r+s