Questions about loading a pre-trained model using lightnining CLI for continue training

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### Bug description

Hi, I tried to load a pre-trained model using lightnining cli for continue training, which works well for single gpu case. However, for multiple gpu case, I meet a bug in the optimization process:
RuntimeError: !tensors.empty() INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1712608853085/work/torch/csrc/distributed/c10d/reducer.cpp":2090, please report a bug to PyTorch.

In both rank 0 and rank 1 gpu cores. How to properly load models for multi gpu training with the help of cli? Thanks.

### What version are you seeing the problem on?

master

### How to reproduce the bug

```python
Please see the questions above.
```

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```

### More info

Nope

cc @mauvilsa

Start by reproducing the Lightning CLI pretrained-model loading flow with multiple GPUs, using the complete PyTorch, Lightning, CUDA, OS, and GPU environment details that are missing from the report. Compare it with the working single-GPU case and determine what is needed for continued training to complete without the PyTorch distributed reducer assertion.

Written by the indexing model from the issue text.