cCiA`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)zMistral model configuration)PretrainedConfig)loggingc^\rSrSrSrSrS/rSSSSSSSS.rS/S /4S S /S /4S /S /4S .rSU4S jjr Sr U=r $) MistralConfiga This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an Mistral 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 Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1. [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) 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 Mistral model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MistralModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. 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 8): 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 `8`. head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`): The attention head dimension. 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 `4096*32`): The maximum sequence length that this model might ever be used with. Mistral's sliding window attention allows sequence of up to 4096*32 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-06): 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*): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. 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. sliding_window (`int`, *optional*, defaults to 4096): Sliding window attention window size. If not specified, will default to `4096`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import MistralModel, MistralConfig >>> # Initializing a Mistral 7B style configuration >>> configuration = MistralConfig() >>> # Initializing a model from the Mistral 7B style configuration >>> model = MistralModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```mistralpast_key_valuescolwiserowwise)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 *>XlXlX lX0lX@lXPlUUlXplUcUnX`lXl Xl Xl Xl UUl UUlSU;a[R!S5 ["TU]H"SU UUUS.UD6 g)N layer_typeszDetected Mistral model with layer_types. Consider using AutoModel or Ministral classes instead to enable alternating attention compatibility.) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headssliding_windowhead_dimnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_thetaattention_dropoutlogger warning_oncesuper__init__)selfrrrrrr"r!r#rr$r%r&rrrrr'r r(kwargs __class__s k/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/mistral/configuration_mistral.pyr,MistralConfig.__init__ts.%'>$&!2!2#6 ,   &"5 #6 $!2("$!2 F "   `   %%% 3    )r(r!r#rr$rrrrr"r%r'r r&r)i}i8 r4Nsiluig{Gz?gư>TNFg@r3g) __name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planr,__static_attributes__ __classcell__)r/s@r0rrsG RJ#4"5&/%.%.%."+ )"+&(9:#%568IJ!"_$56 )!)7 7 r2rN) r=configuration_utilsrutilsr get_loggerr9r)r__all__rr2r0rHs<"3   H %S $S l  r2