o ¾e¦i^%ã@s6ddlmZmZddlmZGdd„deƒZdgZdS)é)ÚPreTrainedConfigÚlayer_type_validation)ÚRopeParametersc*s@eZdZdZdZdgZddddddddœZd gd gfd d gd gfd gd gfd œZ                    d2dedBdedBdedBdedBd edBd!edBd"e dBd#edBd$e dBd%e dBd&edBd'edBd(edBd)e dBd*e e e e fBdBd+e dBd,e dBd-e dBd.edBd/ee dBf(‡fd0d1„ Z‡ZS)3Ú 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 ``` Úolmo3Úpast_key_valuesÚcolwise_gather_outputÚrowwise_split_inputÚcolwiseÚrowwise)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_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnormé€Äéé+é NÚsiluéç{®Gáz”?TéégÄFççñh㈵øä>Ú vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚ rms_norm_epsÚsliding_windowÚ layer_typesc sÈ||_||_||_||_||_||_|dur|}||_||_| |_| |_ ||_ ||_ ||_ | |_ | |_| |_||_||_||_|jdurOdd„t|jƒDƒ|_t|j|jƒ||_tƒjdi|¤ŽdS)NcSs$g|]}|dddkrdnd‘qS)rééÚsliding_attentionÚfull_attention©)Ú.0Úir6r6úk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/olmo3/configuration_olmo3.pyÚ ©sÿz(Olmo3Config.__init__..r6)rr%rr r!r"r#r$r&r'r-r.r+r(r)r*r/r0r1Úrangerr,ÚsuperÚ__init__)Úselfrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1Úkwargs©Ú __class__r6r9r=ws8 ÿzOlmo3Config.__init__)rrrrrNrrrTrNrFNFrrrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planÚintÚstrÚfloatÚboolrÚdictÚlistr=Ú __classcell__r6r6r@r9rsšLù  ýëþýüûúùø ÷ ö õ ô óòñðïîíì ërN)Úconfiguration_utilsrrÚmodeling_rope_utilsrrÚ__all__r6r6r6r9Ús