Repository: huggingface/transformers
Pull request: fix links in `CONTRIBUTING.md`

# What does this PR do?

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Just a quick PR to swap relative doc links to published URLs, it was returning 404 errors


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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- [ ] Did you write any new necessary tests?


## Who can review?

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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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Changed files:
filename: CONTRIBUTING.md
patch: @@ -125,9 +125,9 @@ If you're contributing a **vision-language model** (or any multimodal model that
 All new models should use the modular architecture pattern. Create a `modular_<model_name>.py` file using the modular model converter:
 
 - Use the CLI, [`transformers add-new-model-like`](https://github.com/huggingface/transformers/blob/main/src/transformers/cli/add_new_model_like.py) to generate a modular skeleton and get started
-- All code should be in the modular file if possible. Modeling must be in it, it's better if configuration is in it as well. [Modular guide](./docs/source/en/modular_transformers.md#implementing-a-modular-file) shows a quick way to set up a modular file.
+- All code should be in the modular file if possible. Modeling must be in it, it's better if configuration is in it as well. [Modular guide](https://huggingface.co/docs/transformers/modular_transformers#implementing-a-modular-file) shows a quick way to set up a modular file.
 - Reuse existing patterns from similar models as much as possible
-- You can make the model compatible with inference engines such as vLLM or SGLang, and enable zero-effort integration. See specific requirements for model implementation in ["Transformers modeling backend"](./docs/source/en/transformers_as_backend.md#multimodal-models)
+- You can make the model compatible with inference engines such as vLLM or SGLang, and enable zero-effort integration. See specific requirements for model implementation in ["Transformers modeling backend"](https://huggingface.co/docs/transformers/transformers_as_backend#multimodal-models)
 
 To verify your modular file is correct, run: