Enhance documentation to explain chat-based few-shot prompting
Author: MostHumble
Pull request: yes

Updates the documentation on few-shot prompting to illustrate how to structure examples using the chat-based format for instruction-tuned models.

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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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- diffusers: [different repo](https://github.com/huggingface/diffusers)
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Comments:
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~Hi @MostHumble, I'm not sure we can accept this PR! The reason is that few-shot prompting was mostly a technique used on **causal language models that had not been trained for chat or instruction-following**. Those models really did just "continue the text", and so one of the only ways to get them to do what you wanted was to few-shot prompt them, so that they understood the pattern you wanted them to continue.~

~With instruction-tuned and chat-tuned models, the models generally obey instructions immediately. I'm not sure that adding a few-shot prompt will actually improve their performance since it pushes them "out of distribution", particularly if you write responses for them that don't match their normal style.~

~If there's any paper or research suggesting that this works for chat models, I'm curious to see it! But I suspect it will actually **hurt** performance for those models, so I don't want to encourage it in our docs until I see data supporting it.~

EDIT: My colleague @nielsrogge pointed out that I was completely wrong, and few-shot / many-shot in-context learning does [absolutely work very well for chat models](https://arxiv.org/abs/2404.11018)! Reviewing the PR now.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_37828). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Great to see this addition! Few-shot prompting is also how I typically ensure the model follows the exact format I want it to generate. Perhaps we can add a similar guide to prompting VLMs, I recently [shared some notebooks](https://www.linkedin.com/posts/niels-rogge-a3b7a3127_excited-to-share-2-new-guides-i-worked-on-activity-7320104135138168832-yFcK/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAB9DSA8BPsvimE8aW0sXjqc-UM3dsFmOfEg) on them.

However, I'm not sure all models work well on the few-shot setting if they haven't been trained for this multi-turn of "user", then "assistant", then "user" etc. case (perhaps that could be added as a disclaimer). Closed-source models like Gemini work exceptionally well on them, as the paper above also shows. It also worked well for me for Qwen2.5-VL and llama-4 as shown [here](https://github.com/NielsRogge/tutorials/blob/main/notebooks/How_I_prompt_VLMs_in_2025_(Inference_Providers).ipynb). 

@stevhliu Should be good now :))