"MPTForCausalLM not supported" error when using pipeline, but not when using from_pretrained
### System Info

Python 3.8.10 (default, Nov 14 2022, 12:59:47) 

transformers.__version__ is '4.30.2'

lambda labs 1xA100

invoking `generator = transformers.pipeline(task="text-generation", model="mosaicml/mpt-7b", trust_remote_code=True)` ends with this exception:

```
You are using config.init_device='cpu', but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:05<00:00,  2.73s/it]
Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers
pip install xformers.
The model 'MPTForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].
```

However, loading using:

```
model = transformers.AutoModelForCausalLM.from_pretrained(
  'mosaicml/mpt-7b',
  trust_remote_code=True
)
```

works fine.

How can I load this model in a `pipeline`?

### Who can help?

@Narsil @ArthurZucker @younesbelkada 



### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

1. `generator = transformers.pipeline(task="text-generation", model="mosaicml/mpt-7b", trust_remote_code=True)`


### Expected behavior

The pipeline would load OK, just as .from_pretrained works
Hey, I suggest you to open this issue on the repo, this is because the `auto_map ` attribute in the `config.json` file is not properly set. We are probably going to add this model to transformers soon too! 