Saving Phi 3 vision fails due to tensor sharing
Author: EricLBuehler
Pull request: no

Hello and thank you for the great work here!

We are trying to save a Phi 3 vision mode, but are running into some issues saving it as safetensors.

Due to a shared weight, saving unfortunately fails when using safetensors. I was wondering if there is a solution to de-tie the weight? I attempted to de-tie manually by copying the tensor, but that did not work (perhaps I did it incorrectly, as there is another reference?).

## Minimum reproducible example
```py
from transformers import AutoModelForCausalLM

model_id = "lamm-mit/Cephalo-Phi-3-vision-128k-4b-alpha"

model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto"
)

model.save_pretrained("out", safe_serialization=True)
```

Comments:
@EricLBuehler Thanks for the report.

https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/tree/main seems to be working fine, so there must be something going on in that specific remote code.

Without a stack trace it's kind of hard to debug though.

I moved the issue to `transformers` since it seems the error is there, with a stacktrace+error message I might be able to help better.

Also weight sharing is not allowed in safetensors file, if you're lazy you can just call `contiguous()` on every member of your state dict, but that might have consequences downstream for finetuning for instance.
Some type of weight sharing is simply not allowed (like overlapping tensors with None being a superset of the other)

I am running in the same Problem, when i am using the Phi-3-Vision of Hugging Face. Our final goal currently is to convert a finetuned Phi3-Vision with LoRa to Onnx and inference it with the `onnxruntime-genai`.

Maybe some context how did i get here :

1. We trained an LoRa Adapater for Phi3-Vision
2. I tried to merge the Lora Adapter and save the Model with the adjusted LoRa Weights
3. I was always running in the Error posted below
4. I decided to remove the LoRa Adapter loading and only trying to save the Base Phi3-Vision and got the same error

The script to reproduce :

```
from transformers import AutoModelForCausalLM

model_id = "microsoft/Phi-3-vision-128k-instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto"
)

model.save_pretrained("out", safe_serialization=True)
```

The Error Message (i do not want to set safe_serialization=false)

```
Execution failed. User process 'Rank 0' exited with status code 1. Please check log file 'user_logs/std_log_process_0.txt' for error details. Error: Traceback (most recent call last):
  File "save_phi3_vision.py", line 9, in <module>
    model.save_pretrained("out", safe_serialization=True)
  File "/opt/conda/envs/ptca/lib/python3.8/site-packages/transformers/modeling_utils.py", line 2546, in save_pretrained
    raise RuntimeError(
RuntimeError: The weights trying to be saved contained shared tensors [{'model.embed_tokens.weight', 'model.vision_embed_tokens.wte.weight'}] that are mismatching the transformers base configuration. Try saving using `safe_serialization=False` or remove this tensor sharing.
```

Hey! I am seeing `trust_remote_code=True` which means the code being use is potentially not well supported. 
Try cloning weights of the embed token in the `model.vision_embed_tokens.wte` manually, could help 

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