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from typing import Any |
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from transformers.configuration_utils import PretrainedConfig |
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__all__ = ["AIMv2Config"] |
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class AIMv2Config(PretrainedConfig): |
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"""This is the configuration class to store the configuration of an [`AIMv2Model`]. |
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Instantiating a configuration with the defaults will yield a similar configuration |
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to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224). |
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Args: |
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hidden_size: Dimension of the hidden representations. |
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intermediate_size: Dimension of the SwiGLU representations. |
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num_hidden_layers: Number of hidden layers in the Transformer. |
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num_attention_heads: Number of attention heads for each attention layer |
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in the Transformer. |
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num_channels: Number of input channels. |
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image_size: Image size. |
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patch_size: Patch size. |
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rms_norm_eps: Epsilon value used for the RMS normalization layer. |
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attention_dropout: Dropout ratio for attention probabilities. |
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projection_dropout: Dropout ratio for the projection layer after the attention. |
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qkv_bias: Whether to add a bias to the queries, keys and values. |
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use_bias: Whether to add a bias in the feed-forward and projection layers. |
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kwargs: Keyword arguments for the [`PretrainedConfig`]. |
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""" |
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model_type: str = "aimv2" |
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def __init__( |
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self, |
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hidden_size: int = 1024, |
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intermediate_size: int = 2816, |
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num_hidden_layers: int = 24, |
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num_attention_heads: int = 8, |
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num_channels: int = 3, |
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image_size: int = 224, |
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patch_size: int = 14, |
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rms_norm_eps: float = 1e-5, |
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attention_dropout: float = 0.0, |
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projection_dropout: float = 0.0, |
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qkv_bias: bool = False, |
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use_bias: bool = False, |
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**kwargs: Any, |
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): |
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super().__init__(**kwargs) |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.num_channels = num_channels |
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self.patch_size = patch_size |
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self.image_size = image_size |
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self.attention_dropout = attention_dropout |
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self.rms_norm_eps = rms_norm_eps |
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self.projection_dropout = projection_dropout |
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self.qkv_bias = qkv_bias |
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self.use_bias = use_bias |
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