Add custom config to quicktour (#18115)

* 📝 first draft of new quicktour

* make style

* 🖍 edit and review

* 🖍 small fixes

* 🖍 only add custom config section

* 🖍 use autoclass instead
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@ -389,3 +389,42 @@ One particularly cool 🤗 Transformers feature is the ability to save a model a
```
</tf>
</frameworkcontent>
## Custom model builds
You can modify the model's configuration class to change how a model is built. The configuration specifies a model's attributes, such as the number of hidden layers or attention heads. You start from scratch when you initialize a model from a custom configuration class. The model attributes are randomly initialized, and you'll need to train the model before you can use it to get meaningful results.
Start by importing [`AutoConfig`], and then load the pretrained model you want to modify. Within [`AutoConfig.from_pretrained`], you can specify the attribute you want to change, such as the number of attention heads:
```py
>>> from transformers import AutoConfig
>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
```
<frameworkcontent>
<pt>
Create a model from your custom configuration with [`AutoModel.from_config`]:
```py
>>> from transformers import AutoModel
>>> my_model = AutoModel.from_config(my_config)
```
</pt>
<tf>
Create a model from your custom configuration with [`TFAutoModel.from_config`]:
```py
>>> from transformers import TFAutoModel
>>> my_model = TFAutoModel.from_config(my_config)
```
</tf>
</frameworkcontent>
Take a look at the [Create a custom architecture](./create_a_model) guide for more information about building custom configurations.
## What's next?
Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!