Avoid hard failure for gpt-oss GGUF architecture by falling back to g…
Author: TheSanjBot
Pull request: yes



# What does this PR do?

This PR avoids a hard failure when loading GGUF models that declare the
`gpt-oss` architecture.

Currently, such models raise a `ValueError` during GGUF config loading.
This change maps `gpt-oss` to the closest supported architecture
(`gpt-neox`) and emits a clear warning to communicate current limitations.

The goal is to allow GGUF checkpoints using `gpt-oss` to be loaded without
crashing, enabling downstream tools (e.g. vLLM) to proceed.

### Notes / Limitations
- This does **not** implement full GPT-OSS support.
- MoE layers are not supported and inference correctness is **not guaranteed**.
- This is a best-effort fallback to avoid hard failure, not a claim of correctness.

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Fixes #43366


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

No tests were added as GGUF integration tests require large binary artifacts
and this change only affects architecture handling and error prevention.

## Who can review?

cc @SunMarc @Rocketknight1

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Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

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Comments:
CI failure seems unrelated to this PR.

The failing test (`test_sample_generate_dict_output` in GLM Image) is marked as FLAKY
and fails inside `modeling_glm_image.py`, which is not touched here.

Could you please rerun CI?

**[For maintainers]** Suggested jobs to run (before merge)

run-slow: grounding_dino