fTh)<SrSSKJr SSKJr "SS\5rS/rg)zDiffLlama model configuration)PretrainedConfig)rope_config_validationcd^\rSrSrSrSrS/rSU4SjjrSrU=r $)DiffLlamaConfiga^ This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama 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 [kajuma/DiffLlama-0.3B-handcut](https://huggingface.co/kajuma/DiffLlama-0.3B-handcut). 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 DiffLlama model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DiffLlamaModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 16): 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. 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 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): 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. 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', 'diffllama3'], 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 'diffllama3'. 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 'diffllama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'diffllama3'. Scaling factor applied to high frequency components of the RoPE 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. lambda_std_dev (`float`, *optional*, defaults to 0.1): The standard deviation for initialization of parameter lambda in attention layer. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_heads ```python >>> from transformers import DiffLlamaModel, DiffLlamaConfig >>> # Initializing a DiffLlama diffllama-7b style configuration >>> configuration = DiffLlamaConfig() >>> # Initializing a model from the diffllama-7b style configuration >>> model = DiffLlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` diffllamapast_key_valuesc >XlXlX lX0lX@lXPlUcUnX`lXplXlXl Xl UUl UUl UUl UUlUUlUbUOURUR -UlURb,SUR;aURSURS'[#U5 [$TU]L"SU U UUS.UD6 g)Ntype rope_type) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_theta rope_scalingattention_biasattention_dropoutlambda_std_devhead_dimrsuper__init__)selfrrrrrrrrrrrr rrrrrrr r!r"kwargs __class__s m/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/diffllama/configuration_diffllama.pyr$DiffLlamaConfig.__init__s2%'>$&!2!2#6   &"5 #6 $!2("$(,!2,$,$8d>N>NRVRjRj>j     (Vt7H7H-H-1->->v-FD  k *t$  %%% 3    )rr r"rrrrr!rrrrrrrrr)i}i  Nsilur+g{Gz?gh㈵>TNFg@NFgg?N) __name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inferencer$__static_attributes__ __classcell__)r's@r(rrsek ZJ#4"5  $!-; ; r*rN)r5configuration_utilsrmodeling_rope_utilsrr__all__rr*r(r=s*$$39l &l ^  r*