loading models is painful and not HF compatible

To load a sliced model, we first load an uninitialized model, slice it, and load the checkpoint in. This is a pain for a few reasons:

1. Adding new models means adding a switch in this code: https://github.com/microsoft/TransformerCompression/blob/a369325425cdfd3560c078060f829acd5ed8eb15/src/slicegpt/hf_utils.py#L75

2. it's not easy for HF users to use our models directly, without running slicing themselves. It would be great if users could just do AutoModelForCausalLM.from_pretrained('microsoft/sliced-llama2-13B-30pc'). This would mean publishing such compatible models on HF, which would mean creating the model class explicitly.

Things to consider for a solution:

• we'd probably need to store the "new hidden size" in the config somehow
• we should make sure this doesn't block us from doing slicing with different levels per layer.
• adding new models should "just work", without the current if/else in hf_utils

### Tasks
- [ ] https://github.com/microsoft/TransformerCompression/pull/102