Cannot Load .pt model
Author: ivanhe123
Pull request: no

### System Info

Python version 3.11
- `transformers` version: 4.42.3
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.11.0
- Huggingface_hub version: 0.23.4
- Safetensors version: 0.4.2
- Accelerate version: not installed
- Accelerate config: not found
- PyTorch version (GPU?): 2.3.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA GeForce RTX 4060 Laptop GPU


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

1. Finetuned model using https://www.kaggle.com/code/chlorinecl/notebook4101d69eb6
2. Download .pt model and load it using
```
import torch
from transformers import AutoProcessor, SeamlessM4TModel
new_model = torch.load("./expt4_m4tM.pt")
processor = AutoProcessor.from_pretrained("seamless-m4t-medium")
model_seam = SeamlessM4TModel.from_pretrained("seamless-m4t-medium")
model_seam.load_state_dict(new_model)
model_seam.save_pretrained("./new_seamless-m4t-medium")
```
3. Outputs:
```
D:\projects\GNNNER\venv\Lib\site-packages\transformers\deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
  warnings.warn(
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Traceback (most recent call last):
  File "D:\projects\GNNNER\convert_bin_to_pt.py", line 6, in <module>
    model_seam.load_state_dict(new_model)
  File "D:\projects\GNNNER\venv\Lib\site-packages\torch\nn\modules\module.py", line 2189, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for SeamlessM4TModel:
	Missing key(s) in state_dict: "shared.weight", "text_encoder.embed_tokens.weight", "text_encoder.layers.0.self_attn.k_proj.weight", "text_encoder.layers.0.self_attn.k_proj.bias", "text_encoder.layers.0.self_attn.v_proj.weight", "text_encoder.layers.0.self_attn.v_proj.bias", "text_encoder.layers.0.self_attn.q_proj.weight", "text_encoder.layers.0.self_attn.q_proj.bias", "text_encoder.layers.0.self_attn.out_proj.weight", "text_encoder.layers.0.self_attn.out_proj.bias", "text_encoder.layers.0.self_attn_layer_norm.weight", "text_encoder.layers.0.self_attn_layer_norm.bias", "text_encoder.layers.0.ffn.fc1.weight", "text_encoder.layers.0.ffn.fc1.bias", "text_encoder.layers.0.ffn.fc2.weight", "text_encoder.layers.0.ffn.fc2.bias", "text_encoder.layers.0.ffn_layer_norm.weight", "text_encoder.layers.0.ffn_layer_norm.bias", "text_encoder.layers.1.self_attn.k_proj.weight", "text_encoder.layers.1.self_attn.k_proj.bias", "text_encoder.layers.1.self_attn.v_proj.weight", 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	...
	Unexpected key(s) in state_dict: "model_name", "model". 
```

### Expected behavior

Expected to load the new fintuned model and then save it to a new model file.

Comments:
Hi @ivanhe123, thanks for opening this issue! 

From the error, it looks like the keys in the state dict `new_model` do not match those in the model `SeamlessM4TModel`. You can check the expected keys in the model by doing `model_seam.state_dict().keys()`.

Note, it's not necessary for you to download and load in a pretrained checkpoint and then load in new weights. You can initialize a new model with the same architecture and empty weights by just downloading the config: 

```py
import torch
from accelerate import init_empty_weights
from transformers import AutoConfig, SeamlessM4TModel

config = AutoConfig.from_pretrained("facebook/hf-seamless-m4t-medium")

with init_empty_weights():
    model = SeamlessM4TModel(config)

new_model = torch.load("./expt4_m4tM.pt")
model.load_state_dict(new_model)
```

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