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examples/modular-transformers/modular_my_new_model2.py

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+from transformers.models.gemma.modeling_gemma import GemmaForSequenceClassification
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+# Example where we only want to only modify the docstring
+class MyNewModel2Config(LlamaConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the Gemma-7B.
+    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 256000):
+            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GemmaModel`]
+    ```python
+    >>> from transformers import GemmaModel, GemmaConfig
+    >>> # Initializing a Gemma gemma-7b style configuration
+    >>> configuration = GemmaConfig()
+    >>> # Initializing a model from the gemma-7b style configuration
+    >>> model = GemmaModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+
+# Example where alllllll the dependencies are fetched to just copy the entire class
+class MyNewModel2ForSequenceClassification(GemmaForSequenceClassification):
+    pass