Error loading converted litgpt checkpoints in `pytorch_model.bin` format using huggingface `AutoModelForCausalLM`

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Hi, we're using the litgpt framework to train models and then would like to export them to huggingface format for continued tuning and evaluation.

The steps we're using after completing training are:
1. `scripts/convert_pretrained_checkpoint.py` to "finalize" the model
2. `scripts/convert_lit_checkpoint.py` to conform it to the huggingface saved model format
3. Load using `transformers.AutoModelForCausalLM.from_pretrained("/path/to/converted/checkpoint/dir")`

The actual load step 3. throws an error because it tries to call `torch.load(checkpoint_file, weights_only=True)` internally when it sees that no safetensors format checkpoint is available: [transformers/modeling_utils.py#L529-L535](https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L529-L535)

This can be bypassed by setting `weights_only=False` but this is not the desired solution, rather, it would be great if there was a way to export a trained litgpt model to `model.safetensors` format directly, rather than to the `pytorch_model.bin` file format.
What do you think?

I couldn't find any mention of this hiccup within litgpt, or elsewhere really actually - the only "safetensors" related things here are on the `scripts/download.py` side for bringing hf safetensors format models _into_ litgpt.

Read scripts/convert_pretrained_checkpoint.py and scripts/convert_lit_checkpoint.py first, then compare their output with the Transformers loading path linked in the issue. Confirm how the converted checkpoint is written and identify the supported export path for model.safetensors. Done means a converted LitGPT checkpoint can be loaded by AutoModelForCausalLM.from_pretrained without disabling weights-only loading.

Written by the indexing model from the issue text.