fTh9SSKJrJrJr SSKrSSKJr SSKJr SSK J r SSK J r SSK Jr SS KJrJrJr SS KJr SS KJrJrJrJrJrJr \R8"\5r"S S \5r"SS\5r \ RB"\ 5 Sr""SS\5r#"SS\5r$"SS\5r%"SS\5r&"SS\5r'"SS\5r(/SQr)g))CallableOptionalTupleN)Cache)ALL_ATTENTION_FUNCTIONS)ALL_LAYERNORM_LAYERS)logging)LlamaPreTrainedModel LlamaRMSNormeager_attention_forward) OlmoConfig) OlmoAttentionOlmoDecoderLayerOlmoForCausalLM OlmoModelOlmoRotaryEmbeddingapply_rotary_pos_embc^\rSrSrSrSrSSSSSSSS.rS /S /4S S /S /4S /S /4S .rSU4SjjrSr U=r $) 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 ``` olmo2 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 >[TU]"S0SU_SU_SU_SU_SU_SU_SU_SU_S U _S U _S U _S U _S U _SU_SU_SU_SU_SU_UD6 UUlU?g)N 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_embeddings rope_theta rope_scalingattention_biasattention_dropout)super__init__ rms_norm_epsclip_qkv)selfr&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r;kwargs __class__s _/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/olmo2/modular_olmo2.pyr:Olmo2Config.__init__ws.  ! # 0 0  !4  !4  " %< 0   & & & !4 " &! "*# $0' ,) M)r;)iii+ rCNsiluig{Gz?TNigFg@NFgh㈵>) __name__ __module__ __qualname____firstlineno____doc__ model_typebase_model_tp_planbase_model_pp_planr:__static_attributes__ __classcell__r?s@r@rrsKZJ%2%2%2%2"+ )"+&(9:#%568IJ!"_$56  $!)..rBrc\rSrSrSrSrg) Olmo2RMSNormcURnUR[R5nUR S5R SSS9nU[R "X0R-5-nURU-RU5$)Nr T)keepdim) dtypetotorchfloat32powmeanrsqrtvariance_epsilonweight)r=r input_dtypevariances r@forwardOlmo2RMSNorm.forwardsw#)) %((7  $$Q',,R,>% H?T?T4T(UU  m+// <