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107 lines
4.3 KiB
107 lines
4.3 KiB
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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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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"""Pixtral model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class PixtralVisionConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PixtralVisionModel`]. It is used to instantiate an
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Pixtral vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to the vision encoder used by Pixtral-12B.
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e.g. [pixtral-hf/pixtral-9b](https://huggingface.co/pixtral-hf/pixtral-9b)
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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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hidden_size (`int`, *optional*, defaults to 1024):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 4096):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of input channels in the input images.
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image_size (`int`, *optional*, defaults to 1024):
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Max dimension of the input images.
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patch_size (`int`, *optional*, defaults to 16):
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Size of the image patches.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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Activation function used in the hidden layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for the attention layers.
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rope_parameters (`RopeParameters`, *optional*):
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The RopeParameters
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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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Example:
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```python
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>>> from transformers import PixtralVisionModel, PixtralVisionConfig
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>>> # Initializing a Pixtral-12B style configuration
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>>> config = PixtralVisionConfig()
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>>> # Initializing a model (with randomly initialized weights) from the configuration
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>>> model = PixtralVisionModel(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 = "pixtral"
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def __init__(
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self,
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hidden_size: int | None = 1024,
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intermediate_size: int | None = 4096,
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num_hidden_layers: int | None = 24,
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num_attention_heads: int | None = 16,
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num_channels: int | None = 3,
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image_size: int | None = 1024,
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patch_size: int | None = 16,
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hidden_act: str | None = "gelu",
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attention_dropout: float | None = 0.0,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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initializer_range: float | None = 0.02,
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**kwargs,
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):
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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.hidden_act = hidden_act
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self.head_dim = hidden_size // num_attention_heads
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self.initializer_range = initializer_range
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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__all__ = ["PixtralVisionConfig"]
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