DOC Add entry to solve unknown config argument (#2340)

There have been multiple issues and forum posts in the past asking about
errors like:

TypeError: LoraConfig.__init__() got an unexpected keyword argument ...

This error can occur when the adapter that is being loaded is trained
with a more recent PEFT version than the one currently being used. I
thus added a section to the Troubleshooting part of our docs to describe
the solutions.

Note that we already added changes to PEFT in #2038 to make configs
forward compatible. But since users who encounter this problem have, by
definition, older PEFT versions, they don't benefit from this.

---------

Co-authored-by: Steven Liu <[email protected]>

docs/source/developer_guides/troubleshooting.md

@@ -290,3 +290,31 @@ config = LoraConfig(
 ```
 
 Depending on the type of model you use, the batch norm layers could have different names than `"normalization"`, so please ensure that the name matches your model architecture.
+
+## Version mismatch
+
+### Error while loading the config because of an unexpected keyword argument
+
+When you encounter an error like the one shown below, it means the adapter you're trying to load was trained with a more recent version of PEFT than the version you have installed on your system.
+
+```
+TypeError: LoraConfig.__init__() got an unexpected keyword argument <argument-name>
+```
+
+The best way to resolve this issue is to install the latest PEFT version:
+
+```sh
+python -m pip install -U PEFT
+```
+
+If the adapter was trained from a source install of PEFT (an unreleased version of PEFT), then you also need to install PEFT from source.
+
+```sh
+python -m pip install -U git+https://github.com/huggingface/peft.git
+```
+
+If it is not possible for you to upgrade PEFT, there is a workaround you can try.
+
+Assume the error message says that the unknown keyword argument is named `foobar`. Search inside the `adapter_config.json` of this PEFT adapter for the `foobar` entry and delete it from the file. Then save the file and try loading the model again.
+
+This solution works most of the time. As long as it is the default value for `foobar`, it can be ignored. However, when it is set to some other value, you will get incorrect results. Upgrading PEFT is the recommended solution.