Failed to load the finetuned model with `AutoModelForCausalLM.from_pretrained(name, state_dict=state_dict)`

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I fine-tuned llama3-8b with Lora and followed the tutorial in the repository to convert the final result into `model.pth`. However, when I try to load the fine-tuned weights into the model using `AutoModelForCausalLM.from_pretrained`, I am unable to do so correctly. Below is my test:
```python
state_dict = torch.load('out/convert/hf-llama3-instruct-esconv/model.pth')
print("state_dict: ", state_dict)
model = AutoModelForCausalLM.from_pretrained('checkpoints/meta-llama/Meta-Llama-3-8B',
device_map=device_map, torch_dtype=torch.float16,
state_dict=state_dict)

print("model.weights", model.state_dict())
```
But I found that the `state_dict` of `torch.load` doesn't equal to the `model.state_dict()`, as shown following:
torch.load:
model.state_dict()

I noticed that even though I passed the `state_dict`, `from_pretrained` still returns the weights of the model loaded by name. Did I make any mistakes in my code, and how can I solve this? Thanks!

Start with the repository tutorial's conversion to model.pth, then reproduce the AutoModelForCausalLM.from_pretrained call using the saved state_dict. Compare the keys and loading behavior with model.state_dict(); done means explaining whether this loading path is supported and documenting the correct way to load the converted weights.

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