failure converting pretrained litgpt checkpoints to HF format: a reproducible example

Nobody has claimed this yet.

### Bug description

there will be an OS File Error if following this example

```
## 1. pretrain the model in litgpt format
mkdir -p custom_texts
curl https://www.gutenberg.org/cache/epub/24440/pg24440.txt --output custom_texts/book1.txt
curl https://www.gutenberg.org/cache/epub/26393/pg26393.txt --output custom_texts/book2.txt

litgpt download EleutherAI/pythia-14m --tokenizer_only True

litgpt pretrain EleutherAI/pythia-14m \
--tokenizer_dir EleutherAI/pythia-14m \
--data TextFiles \
--data.train_data_path "custom_texts/" \
--train.max_tokens 10_000_000 \
--out_dir out/custom-model

## 2. convert to hf format
litgpt convert_from_litgpt out/custom-model/final converted_dir
cp out/custom-model/final/config.json converted_dir

```

```
## 3. load the model in python
import torch
from transformers import AutoModel

model_pth_path = 'converted_dir/model.pth'
model_dir_path = 'converted_dir/'
state_dict = torch.load(model_pth_path)
model = AutoModel.from_pretrained(model_dir_path, state_dict=state_dict, local_files_only=True)

OSError: Error no file named pytorch_model.bin, tf_model.h5, model.ckpt.index or flax_model.msgpack found in directory converted_dir/.
```

Edit:

There two approaches though work.
```
import torch
from transformers import AutoModel

model_pth_path = 'converted_dir/model.pth'
model_dir_path = 'converted_dir/'

state_dict = torch.load(model_pth_path)
model = AutoModel.from_pretrained('EleutherAI/pythia-14m', state_dict=state_dict)
model.embed_in._parameters['weight'][0][:2]
state_dict['gpt_neox.embed_in.weight'][0][:2]
AutoModel.from_pretrained('EleutherAI/pythia-14m').embed_in._parameters['weight'][0][:2] # compare with huggingface's version to ensure it is actually loaded
```

```
import torch
from transformers import AutoConfig, AutoModelForCausalLM

model_pth_path = 'converted_dir/model.pth'
model_dir_path = 'converted_dir/'

state_dict = torch.load(model_pth_path)
config = AutoConfig.from_pretrained(model_dir_path)
model = AutoModelForCausalLM.from_config(config)
model.load_state_dict(state_dict)
```

### What operating system are you using?

macOS

### LitGPT Version

```
Version: 0.5.3
```

Start by running the documented pretrain and `litgpt convert_from_litgpt` commands from the issue, then inspect the conversion command's output handling. Compare the generated `converted_dir` contents with what `transformers.AutoModel.from_pretrained` expects. Done means the converted checkpoint can be loaded from the output directory using the documented Python example.

Written by the indexing model from the issue text.