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Auto api Value Error addition to Troubleshoot (#21708)

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* troubleshooting guide: added an error description for missing auto-mapping

* minor polishing

* changed the example

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/troubleshooting.mdx

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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docs/source/en/troubleshooting.mdx
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@ -174,3 +174,25 @@ tensor([[ 0.0082, -0.2307],
- Some models don't have a padding token. - Some models don't have a padding token.
- For some use-cases, users want a model to attend to a padding token. - For some use-cases, users want a model to attend to a padding token.
## ValueError: Unrecognized configuration class XYZ for this kind of AutoModel
Generally, we recommend using the [`AutoModel`] class to load pretrained instances of models. This class
can automatically infer and load the correct architecture from a given checkpoint based on the configuration. If you see
this `ValueError` when loading a model from a checkpoint, this means the Auto class couldn't find a mapping from
the configuration in the given checkpoint to the kind of model you are trying to load. Most commonly, this happens when a
checkpoint doesn't support a given task.
For instance, you'll see this error in the following example because there is no GPT2 for question answering:
```py
>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
>>> processor = AutoProcessor.from_pretrained("gpt2-medium")
>>> model = AutoModelForQuestionAnswering.from_pretrained("gpt2-medium")
ValueError: Unrecognized configuration class <class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'> for this kind of AutoModel: AutoModelForQuestionAnswering.
Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, ...
```
In rare cases, this can also happen when using some exotic models with architectures that don't map to any of the
AutoModelForXXX classes due to the specifics of their API. For example, you can use [`AutoProcessor`] to load BLIP-2's processor,
but to load a pretrained BLIP-2 model itself, you must explicitly use [`Blip2ForConditionalGeneration`] as even [`AutoModel`] won't work.