wehub-resource-sync
67 lines
3.1 KiB
Python
67 lines
3.1 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from <path_to_diff_file.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 diff. If any change should be done, please apply the change to the
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# diff.py file directly.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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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 transformers.models.gemma2.configuration_gemma2 import Gemma2Config
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class CostWiseGemmaConfig(Gemma2Config):
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r"""
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This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
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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 Gemma-7B.
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e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
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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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start_layer (`int`, *optional*, defaults to 28):
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The start layer to output score.
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layer_sep (`int`, *optional*, defaults to 28):
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The sep layer from the start layer to output score.
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layer_wise (`bool`, *optional*, defaults to `False`):
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Whether or not the model should be layerwise.
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```python
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>>> from transformers import Gemma2Model, Gemma2Config
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>>> # Initializing a Gemma2 gemma2-9b style configuration
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>>> configuration = Gemma2Config()
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>>> # Initializing a model from the gemma2-9b style configuration
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>>> model = Gemma2Model(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 = "cost_wise_gemma"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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start_layer: int = 28,
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layer_sep: int = 28,
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layer_wise: bool = False,
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**kwargs,
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):
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self.start_layer = start_layer
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self.layer_sep = layer_sep
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self.layer_wise = layer_wise
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super().__init__(
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**kwargs,
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) |