Make llama and llava more prominent in top-level readmes (#5471)

Summary:
Pull Request resolved: #5471

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Reviewed By: malfet

Differential Revision: D62964593

fbshipit-source-id: e7965f051bc655d80bc02d5302dd8bf36128744c

Author: mergennachin

README.md

@@ -22,6 +22,8 @@ please visit our documentation website [for the latest release](https://pytorch.
 
 Check out the [Getting Started](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) page for a quick spin.
 
+Check out the examples of [Llama](./examples/models/llama2/README.md), [Llava](./examples/models/llava/README.md) and [other models](./examples/README.md) running on edge devices using ExecuTorch.
+
 ## Feedback
 
 We welcome any feedback, suggestions, and bug reports from the community to help

examples/README.md

@@ -35,6 +35,10 @@ A user's journey may commence by exploring the demos located in the [`portable/`
 
 [This page](./models/llama2/README.md) demonstrates how to run Llama 2 7B and Llama 3 8B models on mobile via ExecuTorch. We use XNNPACK to accelerate the performance and 4-bit groupwise PTQ quantization to fit the model on Android and iOS mobile phones.
 
+## Demo of Llava1.5 7B
+
+[This page](./models/llava/README.md) demonstrates how to run [Llava 1.5 7B](https://github.com/haotian-liu/LLaVA) model on mobile via ExecuTorch. We use XNNPACK to accelerate the performance and 4-bit groupwise PTQ quantization to fit the model on Android and iOS mobile phones.
+
 ## Demo of Selective Build
 
 To understand how to deploy the ExecuTorch runtime with optimization for binary size, explore the demos available in the [`selective_build/`](./selective_build) directory. These demos are specifically designed to illustrate the [Selective Build](../docs/source/kernel-library-selective_build.md), offering insights into reducing the binary size while maintaining efficiency.