T^(h$,ddlmZGddeZdgZy))PretrainedConfigceZdZdZdZdgZddddddddZd gd gfd d gd gfd gd gfd Z dfd ZdZ xZ S) Olmo2Configa This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2 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/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-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 Olmo2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Olmo2Model`] 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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. ```python >>> from transformers import Olmo2Model, Olmo2Config >>> # Initializing a Olmo2 7B style configuration >>> configuration = Olmo2Config() >>> # Initializing a model from the Olmo2 7B style configuration >>> model = Olmo2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` olmo2past_key_values colwise_rep rowwise_repcolwiserowwise)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_tokenslayersnormc (t|d| | | |d|||_||_||_||_||_||_||}||_||_ | |_ | |_ ||_ ||_ |j||_||_||_y)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings)super__init__ 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_dropout rms_norm_eps)selfrrrrr r!r"rr#r$rrrrr%r&r(r)r*kwargs __class__s k/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/olmo2/configuration_olmo2.pyrzOlmo2Config.__init__js.  %%% 3    %'>$&!2!2#6   &"5 #6 $!2"$( %%',!2(c|jyt|jtrt|jdk7rt d|j|jj dd}|jj dd}||dvrt d||t|t r|dkrt d |y) z< Validate the `rope_scaling` configuration. NzN`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got typefactor)lineardynamiczF`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+rope_scaling_typerope_scaling_factors r.r'z$Olmo2Config._rope_scaling_validations    $ $++T2c$:K:K6LPQ6Q`aearar`st !--11&$?"//33HdC  $(9AV(VXYjXkl   &j9Le.TXkorXrVWjVklm mYsr/)iii+ r>Nsiluig{Gz?TNigFg@NFggh㈵>) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planrr' __classcell__)r-s@r.rr sKZJ#4"5%2%2%2%2"+ )"+&(9:#%568IJ!"_$56  $!)3)jnr/rN)configuration_utilsrr__all__rr/r.rLs%4fn"fnR /r/