Merge pull request #50 from google-ai-edge:smilingday-more-links

PiperOrigin-RevId: 918176226

Author: copybara-github

README.md

@@ -6,9 +6,17 @@ including converting, quantizing, compiling, running, benchmarking and
 visualizing LiteRT (TFLite) models on various hardware (CPU / GPU / NPU) across
 platforms (desktop, mobile, or cloud).
 
-🚀 [Installation](#-installation)  |  ⚡ [Quick start](#-quick-start)  |  💡 [Common commands](#-common-commands)
-<br>
-📓 [Try Colab](#-try-colab) &nbsp;|&nbsp; 🌟 [Quick demos](#-quick-demos) &nbsp;|&nbsp; 🤖 [Use in coding agent](#-use-in-coding-agent)
+🚀 [Installation](#-installation) | ⚡ [Quick start](#-quick-start) | 💡
+[Common commands](#-common-commands) ｜ 📓 [Try Colab](#-try-colab) | 🌟
+[Quick demos](#-quick-demos) | 🤖 [Use in coding agent](#-use-in-coding-agent)
+
+LiteRT CLI is built on top of [Google AI Edge](https://ai.google.dev/edge)
+stacks, including [LiteRT](https://github.com/google-ai-edge/LiteRT),
+[LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM),
+[LiteRT Torch](https://github.com/google-ai-edge/LiteRT-Torch),
+[AI Edge Quantizer](https://github.com/google-ai-edge/ai-edge-quantizer),
+[AI Edge Portal](https://ai.google.dev/edge/ai-edge-portal), and
+[Model Explorer](https://ai.google.dev/edge/model-explorer).
 
 > [!NOTE] It's still an early preview under active development, thus has limited
 > platform and feature support, plus possible bugs. We appreciate your patience
@@ -124,26 +132,25 @@ Add the LiteRT CLI skill
 into your coding agent (like [Google Antigravity](https://antigravity.google/))
 and try prompts such as:
 
-*   "Download LiteRT model `litert-community/efficientnet_b1` and run it on CPU"
-*   "Benchmark LiteRT model `litert-community/efficientnet_b1` on my Android
-    GPU"
-*   "Compile LiteRT model `litert-community/efficientnet_b1` for NPU target
-    `sm8750`"
-*   "Visualize LiteRT model `litert-community/efficientnet_b1`"
-*   "Download the FP32 EfficientNet model `litert-community/efficientnet_b1`
-    from HuggingFace. Quantize it to INT8 dynamic range (`--recipe
-    dynamic_wi8_afp32`), then benchmark both the original FP32 model and the
-    newly quantized INT8 model on the GPU of my connected Android device.
-    Compare the average latency and report the throughput speedup."
-*   "Convert the model `Qwen/Qwen1.5-0.5B-Chat` from HuggingFace Hub to LiteRT
-    format, and run it locally using the prompt 'Explain edge machine learning
-    in one sentence'."
-*   "Download EfficientNet from huggingface repo
-    `litert-community/efficientnet_b1` . Offline compile (AOT) the model for the
+*   *Download LiteRT model `litert-community/efficientnet_b1` and run it on CPU*
+*   *Benchmark LiteRT model `litert-community/efficientnet_b1` on my Android
+    GPU*
+*   *Compile LiteRT model `litert-community/efficientnet_b1` for NPU target
+    `sm8750`*
+*   *Visualize LiteRT model `litert-community/efficientnet_b1`*
+*   *Download the FP32 model `litert-community/efficientnet_b1` , quantize it to
+    INT8 dynamic range (`--recipe dynamic_wi8_afp32`), then benchmark both the
+    original FP32 model and the newly quantized INT8 model on the GPU of my
+    connected Android device. Compare the average latency and report the
+    throughput speedup.*
+*   *Convert the model `Qwen/Qwen1.5-0.5B-Chat` from HuggingFace, and run it
+    locally using the prompt 'Explain edge machine learning one sentence'*
+*   *Download EfficientNet from huggingface repo
+    `litert-community/efficientnet_b1`, offline compile (AOT) the model for the
     `sm8750` target NPU, and output the compiled model into `./models/compiled`.
     Then, run an on-device inference and benchmark using this newly compiled AOT
     model on the connected Android device's NPU (`--npu`). Confirm that the
-    graph loads directly without dynamic JIT compilation warmup latency."
+    graph loads directly without dynamic JIT compilation warmup latency.*
 
 The agent will automatically install the necessary tools, including Python
 virtual environments, `litert-cli-nightly`, and all required dependencies.