g6*jdZddlmZddlmZddlmZejeZ GddeZ dS)zStableLM model configuration)PretrainedConfig)rope_config_validation)loggingcZeZdZdZdZdgZ dfd ZxZS)StableLmConfiga  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 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). 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_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. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE 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. partial_rotary_factor (`float`, *optional*, defaults to 0.25): Percentage of the query and keys which will have rotary embedding. 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. Example: ```python >>> from transformers import StableLmModel, StableLmConfig >>> # Initializing a StableLM stablelm-3b style configuration >>> configuration = StableLmConfig() ```stablelmpast_key_values silu{Gz?h㈵>TF'N?c ||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_||_||_||_||_|j d|j vr|j d|j d<t'|t)jd||| d|dS)Ntype rope_type) bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_rangelayer_norm_eps use_cache rope_theta rope_scaling use_qkv_bias qk_layernormuse_parallel_residualhidden_dropoutattention_dropoutpartial_rotary_factorrsuper__init__)selfrr rr!r"r#r$rr%r&r'rr(r)r*r+r,r-r.r/rrkwargs __class__s o/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/stablelm/configuration_stablelm.pyr1zStableLmConfig.__init__s4%'>$&!2!2#6 #6 $!2,"$(((%:",!2%:"   (Vt7H-H-H-1->v-FD k *t$$$ %% 3        )r r r r r r rrrrTFrNFFFrrrrr)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer1 __classcell__)r4s@r5rrsn n `J#4"5 $!#"/: : : : : : : : : : r6rN) r:configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr7loggerrrr6r5rCs#"333333999999  H % %n n n n n %n n n n n r6