Fix save_pretrained writing incorrect tie_word_embeddings=True config after PEFT merge

[Cursx]

What does this PR do?

After the merge_and_unload() operation in PEFT, embed_tokens and lm_head become independent tensors with different values, but config.tie_word_embeddings remains True. The load-side already detects this using torch.equal in tie_weights() and skips tying, but save_pretrained() writes the incorrect config as-is.（tie_word_embeddings=True is already in a "semantic error" state in memory. Changing it to False is closer to the actual situation.）

issue # 45127——PEFT merge_and_unload() creates an inconsistent state (weights have been separated, but configuration has not been updated).
impact: Downstream tools (GGUF converters, quantization scripts) trust this config directly, leading to silent weight corruption.

test

I wrote a simple script to reproduce the problem and tested it locally.

I ran make fix-repo and performed the following related tests：
test_save_pretrained_auto_fixes_diverged_tied_embeddings（new test）
test_tied_weights_are_not_tied_if_both_present_but_different（load-side ）
test_tied_weights_are_tied_if_both_present_and_similar
test_tied_weights_are_always_tied_from_config

The CI test is also passed.

Fixes #45127

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• I confirm that this is not a pure code agent PR.

I used multiple AI models (Gemini, Claude, Kimi) to cross-validate edge cases and boundary conditions — different models behave differently around tied embeddings, which made CI failures harder to predict than expected. AI helped me locate these edge cases and I verified they weren't hallucinations.

I have read CONTRIBUTING.md, and tried my best to follow the instructions therein.

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline,
  Pull Request section?
• Was this discussed/approved via a Github issue or the forum? Please add a link
  to it if that's the case.[Bug] Model collapse after merging LoRA with extended vocabulary on models with tie_word_embeddings=True (e.g., Qwen2.5 0.5B) #45127
• Did you make sure to update the documentation with your changes? Here are the
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  here are tips on formatting docstrings.
• Did you write any new necessary tests?

Who can review?

@Cyrilvallez @BenjaminBossan

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