Add instructions to convert Hugging Face models to PyTorch (#3523)

Summary:
As titled. It's pretty common that users download the LLM models in safetensor format. Add instructions and example script to convert them to PyTorch format so that export_llama script can accept. It leverages the utils from TorchTune.

Thanks l3utterfly and kartikayk for the discussions and suggestions!

More context in #3303

Pull Request resolved: #3523

Reviewed By: mergennachin

Differential Revision: D57026658

Pulled By: iseeyuan

fbshipit-source-id: 11badf709920ff945cdfdd2b244c52c750943412

examples/models/llama2/README.md

@@ -117,6 +117,33 @@ You can export and run the original Llama3 8B model.
 
     Due to the larger vocabulary size of Llama3, we recommend quantizing the embeddings with `--embedding-quantize 4,32` to further reduce the model size.
 
+### Option D: Download models from Hugging Face and convert from safetensor format to state dict
+
+You can also download above models from [Hugging Face](https://huggingface.co/). Since ExecuTorch starts from a PyTorch model, a script like below can be used to convert the Hugging Face safetensors format to PyTorch's state dict. It leverages the utils provided by [TorchTune](https://github.com/pytorch/torchtune).
+
+```Python
+from torchtune.utils import FullModelHFCheckpointer
+from torchtune.models import convert_weights
+import torch
+
+# Convert from safetensors to TorchTune. Suppose the model has been downloaded from Hugging Face
+checkpointer = FullModelHFCheckpointer(
+    checkpoint_dir='/home/.cache/huggingface/hub/models/snapshots/hash-number',
+    checkpoint_files=['model-00001-of-00002.safetensors', 'model-00002-of-00002.safetensors'],
+    output_dir='/the/destination/dir' ,
+    model_type='LLAMA3' # or other types that TorchTune supports
+)
+
+print("loading checkpoint")
+sd = checkpointer.load_checkpoint()
+
+# Convert from TorchTune to Meta (PyTorch native)
+sd = convert_weights.tune_to_meta(sd['model'])
+
+print("saving checkpoint")
+torch.save(sd, "/the/destination/dir/checkpoint.pth")
+```
+
 ## (Optional) Finetuning
 
 If you want to finetune your model based on a specific dataset, PyTorch provides [TorchTune](https://github.com/pytorch/torchtune) - a native-Pytorch library for easily authoring, fine-tuning and experimenting with LLMs.