qi^%<ddlmZmZddlmZGddeZdgZy))PreTrainedConfiglayer_type_validation)RopeParametersc*peZdZdZdZdgZddddddddZd gd gfd d gd gfd gd gfd Z d$dedzdedzdedzdedzdedzdedzde dzdedzde dzde dzdedzdedzdedzde dzde e e e fzdzde dzde dzd e dzd!edzd"ee dzf(fd# ZxZS)% Olmo3Configa This is the configuration class to store the configuration of a [`Olmo3Model`]. It is used to instantiate an OLMo3 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-3-0725-1B](https://huggingface.co/allenai/OLMo-3-0725-1B). 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 Olmo3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Olmo3Model`] 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, 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. 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_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`, 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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. sliding_window (`int`, *optional*, defaults to 4096): Size of the sliding window for sliding window attention. layer_types (`list`, *optional*): Attention pattern for each layer. Defaults to sliding window attention for 3 out of 4 layers, and full attention for every 4th layer. ```python >>> from transformers import Olmo3Model, Olmo3Config >>> # Initializing a Olmo3 7B style configuration >>> configuration = Olmo3Config() >>> # Initializing a model from the Olmo3 7B style configuration >>> model = Olmo3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` olmo3past_key_valuescolwise_gather_outputrowwise_split_inputcolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropout rms_norm_epssliding_window layer_typesc ||_||_||_||_||_||_||}||_||_| |_| |_ ||_ ||_ ||_ | |_ | |_| |_||_||_||_|j$5t'|jDcgc]}|dzdzdk7rdndc}|_t)|j$|j||_t-|\di|ycc}w)Nsliding_attentionfull_attention)rrrrrrrrrrr$r%r"rr r!r&r'r(rangerr#super__init__)selfrrrrrrrrrrrr r!r"r#r$r%r&r'r(kwargsi __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/olmo3/configuration_olmo3.pyr2zOlmo3Config.__init__ws#0%'>$&!2!2#6   &"5 #6 $!2",!2#6 ((((,&    #W\]a]s]sWt RSA{a'7#=MM D  d..0F0FG. "6" s.D)ii+ r9Nsiluig{Gz?Tr*NigFNFggh㈵>r8N)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictlistr2 __classcell__)r6s@r7rrsJXJ#4"5%<%<%<%:"+ )"+&(9:#%568IJ!"_$56"'"&(-(**,*.!'.2*.!%#$#'#(+0MQ&+*-%)%)(,+9#$J9#4Z9#: 9# : 9# !4Z 9#!4Z9#$J9#"%t9#!4<9#$;9#Dj9#Dj9#Dj9#"D[9# ($sN/B*CCdJ!9#"t #9#$!4<%9#&dl'9#(d )9#*#Y%+9#9#rN)configuration_utilsrrmodeling_rope_utilsrr__all__r/rJr7rNs'*K1W#"W#t /rJ