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193 lines
11 KiB
193 lines
11 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/moonshine/modular_moonshine.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_moonshine.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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class MoonshineConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine
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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 Moonshine
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[UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny).
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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 32768):
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Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MoonshineModel`].
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hidden_size (`int`, *optional*, defaults to 288):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 1152):
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Dimension of the MLP representations.
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encoder_num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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decoder_num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer decoder.
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encoder_num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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encoder_num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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decoder_num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`decoder_num_attention_heads`.
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pad_head_dim_to_multiple_of (`int`, *optional*):
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Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain
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optimized attention implementations.
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encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder.
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decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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decoder_start_token_id (`int`, *optional*, defaults to 1):
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Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
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are provided to the `generate` function. It is used to guide the model`s generation process depending on
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the task.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Whether the model is used as an encoder/decoder or not.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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bos_token_id (`int`, *optional*, defaults to 1):
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Denotes beginning of sequences token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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Denotes end of sequences token id.
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pad_token_id (`int`, *optional*):
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Padding token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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Example:
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```python
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>>> from transformers import MoonshineModel, MoonshineConfig
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>>> # Initializing a Moonshine style configuration
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>>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny")
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>>> # Initializing a model from the configuration
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>>> model = MoonshineModel(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 = "moonshine"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_key_value_heads": "encoder_num_key_value_heads",
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"num_attention_heads": "encoder_num_attention_heads",
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"num_hidden_layers": "encoder_num_hidden_layers",
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}
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def __init__(
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self,
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vocab_size: int | None = 32768,
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hidden_size: int | None = 288,
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intermediate_size: int | None = 1152,
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encoder_num_hidden_layers: int | None = 6,
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decoder_num_hidden_layers: int | None = 6,
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encoder_num_attention_heads: int | None = 8,
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decoder_num_attention_heads: int | None = 8,
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encoder_num_key_value_heads: int | None = None,
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decoder_num_key_value_heads: int | None = None,
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pad_head_dim_to_multiple_of: int | None = None,
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encoder_hidden_act: str | None = "gelu",
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decoder_hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 512,
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initializer_range: float | None = 0.02,
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decoder_start_token_id: int | None = 1,
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use_cache: bool | None = True,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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is_encoder_decoder: bool | None = True,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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pad_token_id: int | None = None,
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tie_word_embeddings: bool | None = 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.intermediate_size = intermediate_size
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self.encoder_num_hidden_layers = encoder_num_hidden_layers
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self.decoder_num_hidden_layers = decoder_num_hidden_layers
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self.encoder_num_attention_heads = encoder_num_attention_heads
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self.decoder_num_attention_heads = decoder_num_attention_heads
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if encoder_num_key_value_heads is None:
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encoder_num_key_value_heads = encoder_num_attention_heads
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self.encoder_num_key_value_heads = encoder_num_key_value_heads
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if decoder_num_key_value_heads is None:
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decoder_num_key_value_heads = decoder_num_attention_heads
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self.decoder_num_key_value_heads = decoder_num_key_value_heads
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self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of
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self.encoder_hidden_act = encoder_hidden_act
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self.decoder_hidden_act = decoder_hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.decoder_start_token_id = decoder_start_token_id
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self.use_cache = use_cache
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self.is_encoder_decoder = is_encoder_decoder
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.decoder_start_token_id = decoder_start_token_id
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self.tie_word_embeddings = tie_word_embeddings
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self.rope_parameters = rope_parameters
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kwargs.setdefault("partial_rotary_factor", 0.9) # assign default for BC
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super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
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__all__ = ["MoonshineConfig"]
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