Models not in eval()-mode when loaded with from_config()
### System Info

The docs say that models loaded with `from_pretrained()` are done so with `model.eval()` mode on by default. But when using `from_config()` that's not the case, even though loading configs and tokenizers would be using `from_pretrained()` like so:
```python
config = AutoConfig.from_pretrained(
    MODEL_NAME,
    padding='max_length',
    truncation=True,
    output_hidden_states=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, config=config)
model = AutoModelForSequenceClassification.from_config(config)
```
I'd like to argue that we should put the model in `eval()` mode when using `from_config()`. I know at least 2 other people who have spent a great number of hours validating and hunting for that. Similar reasoning to https://github.com/huggingface/transformers/issues/695#issuecomment-502964803 I think it's important to make things deterministic out of the box.

Or, open to understanding why that wouldn't be the case.

### Who can help?

_No response_

### Information

- [ ] 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

Try something like this:
```python
config = AutoConfig.from_pretrained(
    MODEL_NAME,
    padding='max_length',
    truncation=True,
    output_hidden_states=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, config=config)
model = AutoModelForSequenceClassification.from_config(config)
```

### Expected behavior

I'd expect the model to be loaded in `eval()` mode.
A model created with `from_config` will have random weights and is thus not suitable for inference. This is why it is put in training mode, as the documentation clearly states. In any case, it has been the case for such a long time that reverting this would surprise way more users with a breaking change.<|||||>The code snippet I posted above does not load random weights.<|||||>Yes it does.<|||||>Well that explains a lot. I stand corrected, thank you.