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docs: Move Llama 4 instructions in a collapsed section (#1936)

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# What does this PR do?

Currently the instructions for Llama 4 take quite some space before
people can see the overview and other sections about Llama Stack. Moving
this to a collapsed section would make it less verbose.
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Yuan Tang 2025-04-14 08:14:59 -04:00 • committed by GitHub
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@ -9,15 +9,16 @@
[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb)
### ✨🎉 Llama 4 Support 🎉✨ ### ✨🎉 Llama 4 Support 🎉✨
We released [Version 0.2.0](https://github.com/meta-llama/llama-stack/releases/tag/v0.2.0) with support for the Llama 4 herd of models released by Meta. We released [Version 0.2.0](https://github.com/meta-llama/llama-stack/releases/tag/v0.2.0) with support for the Llama 4 herd of models released by Meta.
You can now run Llama 4 models on Llama Stack. <details>
<summary>👋 Click here to see how to run Llama 4 models on Llama Stack </summary>
\
*Note you need 8xH100 GPU-host to run these models* *Note you need 8xH100 GPU-host to run these models*
```bash ```bash
pip install -U llama_stack pip install -U llama_stack
@ -67,6 +68,9 @@ print(f"Assistant> {response.completion_message.content}")
As more providers start supporting Llama 4, you can use them in Llama Stack as well. We are adding to the list. Stay tuned! As more providers start supporting Llama 4, you can use them in Llama Stack as well. We are adding to the list. Stay tuned!
</details>
### Overview ### Overview
Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides