Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+---
+
+# litert-community/Gecko-110m-en
+
+This model provides a few variants of the embedding model published in the [Gecko paper](https://arxiv.org/abs/2403.20327) that are ready for deployment on Android or iOS using [LiteRT stack](https://ai.google.dev/edge/litert) or [google ai edge RAG SDK](https://ai.google.dev/edge/mediapipe/solutions/genai/rag).
+
+## Use the models
+
+### Android
+
+* Try out the gecko embedding model in the [google ai edge RAG SDK](https://ai.google.dev/edge/mediapipe/solutions/genai/rag)
+* Follow the instructions in the [android guide](https://ai.google.dev/edge/mediapipe/solutions/genai/rag/android)
+
+## Performance
+
+### Android
+
+Note that all benchmark stats are from a Samsung S23 Ultra.
+
+<table border="1">
+  <tr>
+   <th></th>
+   <th>Backend</th>
+   <th>Max sequence length</th>
+   <th>Init time (ms)</th>
+   <th>Inference time (ms)</th>
+   <th>Memory (RSS in MB)</th>
+   <th>Model size (MB)</th>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">GPU</p></td>
+   <td><p style="text-align: right">256</p></td>
+   <td><p style="text-align: right">1306.06</p></td>
+   <td><p style="text-align: right">76.2</p></td>
+   <td><p style="text-align: right">604.5</p></td>
+   <td><p style="text-align: right">114</p></td>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">GPU</p></td>
+   <td><p style="text-align: right">512</p></td>
+   <td><p style="text-align: right">1363.38</p></td>
+   <td><p style="text-align: right">173.2</p></td>
+   <td><p style="text-align: right">604.6</p></td>
+   <td><p style="text-align: right">120</p></td>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">GPU</p></td>
+   <td><p style="text-align: right">1024</p></td>
+   <td><p style="text-align: right">1419.87</p></td>
+   <td><p style="text-align: right">397</p></td>
+   <td><p style="text-align: right">871.1</p></td>
+   <td><p style="text-align: right">145</p></td>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">CPU</p></td>
+   <td><p style="text-align: right">256</p></td>
+   <td><p style="text-align: right">11.03</p></td>
+   <td><p style="text-align: right">147.6</p></td>
+   <td><p style="text-align: right">126.3</p></td>
+   <td><p style="text-align: right">114</p></td>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">CPU</p></td>
+   <td><p style="text-align: right">512</p></td>
+   <td><p style="text-align: right">30.04</p></td>
+   <td><p style="text-align: right">353.1</p></td>
+   <td><p style="text-align: right">225.6</p></td>
+   <td><p style="text-align: right">120</p></td>
+  </tr>
+  <tr>
+   <td><p style="text-align: right">dynamic_int8</p></td>
+   <td><p style="text-align: right">CPU</p></td>
+   <td><p style="text-align: right">1024</p></td>
+   <td><p style="text-align: right">79.17</p></td>
+   <td><p style="text-align: right">954</p></td>
+   <td><p style="text-align: right">619.5</p></td>
+   <td><p style="text-align: right">145</p></td>
+  </tr>
+</table>
+
+*   Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models)
+*   Memory: indicator of peak RAM usage
+*   The inference is run on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads
+*   The inference on GPU is accelerated via LiteRT GPU delegate.
+*   Benchmark is done assuming XNNPACK cache is enabled
+*   dynamic_int8: quantized model with int8 weights and float activations.