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| """ RWKV configuration"""
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| from transformers.configuration_utils import PretrainedConfig
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| from transformers.utils import logging
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| logger = logging.get_logger(__name__)
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| RWKV7_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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| class Rwkv7Config(PretrainedConfig):
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| """
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| This is the configuration class to store the configuration of a [`Rwkv7Model`]. It is used to instantiate a RWKV7
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| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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| defaults will yield a similar configuration to that of the RWVK-7
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| [RWKV/v7-Goose-1.6B-Pile-HF](https://huggingface.co/RWKV/v7-Goose-1.6B-Pile-HF) architecture.
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| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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| documentation from [`PretrainedConfig`] for more information.
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| Args:
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| vocab_size (`int`, *optional*, defaults to 65536):
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| Vocabulary size of the RWKV7 model. Defines the number of different tokens that can be represented by the
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| `inputs_ids` passed when calling [`Rwkv7Model`].
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| hidden_size (`int`, *optional*, defaults to 768):
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| Dimensionality of the embeddings and hidden states.
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| num_hidden_layers (`int`, *optional*, defaults to 24):
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| Number of hidden layers in the model.
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| attention_hidden_size (`int`, *optional*):
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| Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
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| num_attention_heads (`int`, *optional*, defaults to 64):
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| The attention heads to use in rwkv7 self_attention module.
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| head_size (`int`, *optional*, defaults to 64): head_size of rwkv7 self_attention module.
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| intermediate_size (`int`, *optional*):
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| Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
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| layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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| The epsilon to use in the layer normalization layers.
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| bos_token_id (`int`, *optional*, defaults to 0):
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| The id of the beginning of sentence token in the vocabulary. Defaults to 0.
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| eos_token_id (`int`, *optional*, defaults to 0):
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| The id of the end of sentence token in the vocabulary. Defaults to 0.
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| tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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| Whether or not to tie the word embeddings with the input token embeddings.
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| use_cache (`bool`, *optional*, defaults to `True`):
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| Whether or not the model should return the last state.
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| Example:
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| ```python
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| >>> from transformers import Rwkv7Config, Rwkv7Model
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| >>> # Initializing a Rwkv7 configuration
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| >>> configuration = Rwkv7Config()
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| >>> # Initializing a model (with random weights) from the configuration
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| >>> model = Rwkv7Model(configuration)
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| >>> # Accessing the model configuration
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| >>> configuration = model.config
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| ```"""
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| model_type = "rwkv7"
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| def __init__(
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| self,
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| vocab_size=65536,
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| hidden_size=768,
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| num_hidden_layers=24,
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| attention_hidden_size=None,
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| head_size=64,
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| intermediate_size=None,
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| lora_rank_decay=None,
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| lora_rank_iclr=None,
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| lora_rank_value_residual_mix=None,
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| lora_rank_gate=None,
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| layer_norm_epsilon=1e-5,
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| bos_token_id=0,
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| eos_token_id=0,
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| tie_word_embeddings=False,
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| use_cache=True,
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| **kwargs,
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| ):
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| self.vocab_size = vocab_size
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| self.hidden_size = hidden_size
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| self.num_hidden_layers = num_hidden_layers
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| self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
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| self.head_size = head_size
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| self.intermediate_size = intermediate_size
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| self.lora_rank_decay = lora_rank_decay
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| self.lora_rank_iclr = lora_rank_iclr
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| self.lora_rank_value_residual_mix = lora_rank_value_residual_mix
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| self.lora_rank_gate = lora_rank_gate
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| self.layer_norm_epsilon = layer_norm_epsilon
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| self.use_cache = use_cache
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| super().__init__(
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| tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
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| )
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|
