o ¾e¦iˆ#ã@s2ddlmZddlmZGdd„deƒZdgZdS)é)ÚPreTrainedConfig)ÚRopeParametersc*s:eZdZdZdZdgZdddddddœZdgdgfd d gd gfd gd gfd œZ                  d0dedBdedBdedBdedBdedBdedBd e dBd!edBd"e dBd#edBd$e dBd%edBd&edBd'edBd(e dBd)e e e e fBdBd*e dBd+e dBd,e dBd-edBf(‡fd.d/„ Z‡ZS)1Ú ArceeConfiga„ This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee 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 AFM-4.5B-Base. Pre-trained weights are available at [arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) and were used to build the examples below. 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 32000): Vocabulary size of the Arcee model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ArceeModel`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18432): 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://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. 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*): Padding token id. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): 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`, *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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads ```python >>> from transformers import ArceeModel, ArceeConfig >>> # Initializing an Arcee AFM-4.5B-Base style configuration >>> configuration = ArceeConfig() >>> # Initializing a model from the AFM-4.5B-Base style configuration >>> model = ArceeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ÚarceeÚpast_key_valuesÚcolwiseÚrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnormé}é éHé NÚrelu2éç{®Gáz”?çñh㈵øä>TéôéôFçÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚmlp_biasÚhead_dimc s®||_||_||_||_||_||_|dur|}||_||_| |_| |_ | |_ ||_ ||_ ||_ |dur6|n|j|j|_||_||_| |_| |_||_tƒjdi|¤ŽdS)N©)rr"rrrrr r!r#r$r%r+r,r-r.r*r)r&r'r(ÚsuperÚ__init__)Úselfrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.Úkwargs©Ú __class__r/úk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/arcee/configuration_arcee.pyr1ws.zArceeConfig.__init__)rrrrrNrrrrTNrrFNFrFN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planÚintÚstrÚfloatÚboolrÚdictr1Ú __classcell__r/r/r4r6rs˜Mú  ýëþýüûúùø ÷ ö õ ô óòñðïîíìërN)Úconfiguration_utilsrÚmodeling_rope_utilsrrÚ__all__r/r/r/r6Ús