o ¾e¦iCã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zStableLM model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc,s eZdZdZdZdgZ             d(dedBdedBdedBdedBdedBdedBdedBdedBdedBdedBde dBde dBde e ee fBdBde dBde dBd e dBd!edBd"edBd#edBd$edBd%edBf*‡fd&d'„ Z ‡Z S))ÚStableLmConfiga  This is the configuration class to store the configuration of a [`~StableLmModel`]. It is used to instantiate an StableLM 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 StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture. 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 StableLM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`StableLmModel`]. intermediate_size (`int`, *optional*, defaults to 6912): Dimension of the MLP representations. hidden_size (`int`, *optional*, defaults to 2560): Number of hidden layers in the Transformer decoder. 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 encoder. num_key_value_heads (`int`, *optional*, defaults to 32): 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). max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. 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`. use_qkv_bias (`bool`, *optional*, defaults to `False`): Whether or not the model should use bias for qkv layers. qk_layernorm (`bool`, *optional*, defaults to `False`): Whether or not to normalize, per head, the Queries and Keys after projecting the hidden states. use_parallel_residual (`bool`, *optional*, defaults to `False`): Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training speedup at large scales. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. bos_token_id (int, *optional*, defaults to 0): The id of the `BOS` token in the vocabulary. eos_token_id (int, *optional*, defaults to 0): The id of the `EOS` token in the vocabulary. pad_token_id (int, *optional*): The id of the `PAD` token in the vocabulary. Example: ```python >>> from transformers import StableLmModel, StableLmConfig >>> # Initializing a StableLM stablelm-3b style configuration >>> configuration = StableLmConfig() ```ÚstablelmÚpast_key_valuesé€Äéé é Úsiluéç{®Gáz”?çñh㈵øä>TFNçéÚ vocab_sizeÚintermediate_sizeÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚlayer_norm_epsÚ use_cacheÚtie_word_embeddingsÚrope_parametersÚ use_qkv_biasÚ qk_layernormÚuse_parallel_residualÚhidden_dropoutÚattention_dropoutÚ bos_token_idÚ eos_token_idÚ pad_token_idc s ||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_||_| |_| dd¡||_||_||_| |_tƒjdi|¤ŽdS)NÚpartial_rotary_factorgÐ?©)rrrrrrrrrrrrr r!r"r#rÚ setdefaultr$r%r&rÚsuperÚ__init__)Úselfrrrrrrrrrrrrrrr r!r"r#r$r%r&Úkwargs©Ú __class__r(úq/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/stablelm/configuration_stablelm.pyr+is. zStableLmConfig.__init__)rr r r r r r r rrTFNFFFrrrrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚintÚstrÚfloatÚboolrÚdictr+Ú __classcell__r(r(r.r0rsˆMêþýüûúùø ÷ ö õ ô óòñðïîíìëêrN) r4Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr1ÚloggerrÚ__all__r(r(r(r0Ús