[Bugfix] FIx TorchAO config bugs

[jwpark33]

Purpose

Fix --hf-overrides normalization for TorchAO (and general HF overrides) so CLI inputs are handled consistently.

This PR adds normalization logic in EngineArgs to support:

• JSON object string input, e.g. --hf-overrides '{"quantization_config_file": "..."}'
• JSON file input via @path, e.g. --hf-overrides '@/path/to/overrides.json'

It also adds tests and docs updates:

• vllm/engine/arg_utils.py: add _normalize_hf_overrides()
• tests/engine/test_arg_utils.py: add normalization tests for string/file/invalid cases
• vllm/config/model.py: clarify accepted string formats in docstring
• docs/features/quantization/torchao.md: add online TorchAO usage examples with --hf-overrides

Test Plan

Run targeted arg-utils tests for hf-overrides:

pytest -q tests/engine/test_arg_utils.py

Test Result

All passed

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[mergify]

Documentation preview: https://vllm--34430.org.readthedocs.build/en/34430/

[gemini-code-assist]

Code Review

This pull request introduces a useful enhancement by allowing --hf-overrides to be specified as either a JSON string or a file path. The implementation is clean, and the inclusion of tests and documentation is commendable. I've identified a couple of minor correctness issues where the loaded JSON is not validated to be a dictionary, which could lead to less clear error messages downstream. I've provided suggestions to address these. Overall, this is a solid improvement.

[gemini-code-assist]

The loaded JSON from the file should be validated to be a dictionary. The hf_overrides argument is expected to be a dictionary of overrides. Currently, if the file contains a JSON primitive (like a string or a number), it will be parsed successfully but will cause an AttributeError later when .items() is called on it. This can be confusing for the user. It's better to fail early with a clear error message.

                    loaded_json = json.load(handle)
                    if not isinstance(loaded_json, dict):
                        raise ValueError(
                            f"hf_overrides file must contain a JSON object: {path}"
                        )
                    self.hf_overrides = loaded_json

[gemini-code-assist]

Similar to the file loading case, the loaded JSON from the string should be validated to be a dictionary. If the string is a valid JSON primitive (e.g., \"a string\" or 123), it will be parsed successfully but will cause an AttributeError later. It's better to fail early with a clear error message ensuring the provided string is a JSON object.

                loaded_json = json.loads(raw)
                if not isinstance(loaded_json, dict):
                    raise ValueError("hf_overrides string must be a JSON object.")
                self.hf_overrides = loaded_json

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @jwpark33.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork