Upload 12 files

T5ModelEncoderWorker.js

@@ -1,5 +1,5 @@
 //load Candle Bert Module wasm module
-import init, { ModelEncoder } from "./build/m.js";
+let init, ModelEncoder;
 
 async function fetchArrayBuffer(url) {
   const cacheName = "t5-candle-cache";
@@ -17,6 +17,13 @@ class Encoder {
   static instance = {};
 
   static async getInstance(weightsURL, tokenizerURL, configURL, modelID) {
+    if (modelID.includes("quantized")) {
+      ({ default: init, ModelEncoder } = await import(
+        "./build/m-quantized.js"
+      ));
+    } else {
+      ({ default: init, ModelEncoder } = await import("./build/m.js"));
+    }
     if (!this.instance[modelID]) {
       await init();
 

index.html

@@ -138,44 +138,42 @@
         <h1 class="text-5xl font-bold">Candle T5 Transformer</h1>
         <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
         <p class="max-w-lg">
-          This demo runs Text-To-Text Transfer Transformer
-          <a
+          This demo showcase Text-To-Text Transfer Transformer (<a
             href="https://blog.research.google/2020/02/exploring-transfer-learning-with-t5.html"
             target="_blank"
-            class="link">
-            T5
-          </a>
-          with
+            class="link"
+            >T5</a
+          >) models right in your browser, thanks to
           <a
             href="https://github.com/huggingface/candle/"
             target="_blank"
             class="link">
             Candle
           </a>
-          in the browser using rust/wasm. Models
+          ML framework and rust/wasm. You can choose from a range of available
+          models, including
           <a
             href="https://huggingface.co/t5-small"
             target="_blank"
             class="link">
-            t5-small </a
+            t5-small</a
           >,
-          <a href="https://huggingface.co/t5-base" target="_blank" class="link">
-            t5-base </a
+          <a href="https://huggingface.co/t5-base" target="_blank" class="link"
+            >t5-base</a
           >,
           <a
             href="https://huggingface.co/google/flan-t5-small"
             target="_blank"
             class="link"
-            >flan-t5-small
-          </a>
-          and a few
+            >flan-t5-small</a
+          >
+          and several t5
           <a
             href="https://huggingface.co/lmz/candle-quantized-t5/tree/main"
             target="_blank"
             class="link">
-            t5 quantized gguf
-          </a>
-          are available.
+            t5 quantized gguf</a
+          >.
         </p>
       </div>
 

utils.js

@@ -6,7 +6,7 @@ export async function extractEmbeddings(
   modelID,
   sentences,
   updateStatus,
-  normalize_embeddings
+  normalize_embeddings = true
 ) {
   return new Promise((resolve, reject) => {
     worker.postMessage({