Update README.md

README.md

@@ -46,9 +46,9 @@ import { VectorSearch } from 'https://cdn.jsdelivr.net/gh/jasonmayes/VectorSearc
 
 // Embedding Model Configuration.
 const MODEL_RUNTIME = 'litertjs'; // OR 'transformersjs'
-const MODEL_URL = 'model/embeddinggemma-300M_seq1024_mixed-precision.tflite'; // OR 'Xenova/all-MiniLM-L6-v2' if transformersjs runtime.
+const MODEL_URL = 'model/embeddinggemma-300M_seq1024_mixed-precision.tflite'; // OR 'huggingworld/all-MiniLM-L6-v2' if transformersjs runtime.
 const SEQ_LENGTH = 1024;
-const TOKENIZER = 'onnx-community/embeddinggemma-300m-ONNX';
+const TOKENIZER = 'huggingworld/embeddinggemma-300m-ONNX';
 const EMBEDDING_MODEL_CONFIG = {
   runtime: MODEL_RUNTIME,
   url: MODEL_URL,
@@ -155,7 +155,7 @@ This model is available to download from HuggingFace which you must do yourself
 const MODEL_RUNTIME = 'litertjs';
 const MODEL_URL = 'model/embeddinggemma-300M_seq1024_mixed-precision.tflite';
 const SEQ_LENGTH = 1024;
-const TOKENIZER = 'onnx-community/embeddinggemma-300m-ONNX';
+const TOKENIZER = 'huggingworld/embeddinggemma-300m-ONNX';
 const EMBEDDING_MODEL_CONFIG = {
   runtime: MODEL_RUNTIME,
   url: MODEL_URL,
@@ -178,9 +178,9 @@ If you wish to use the all-MiniLM-L6-v2 embedding model instead for speed you ca
 ```javascript
 // Embedding Model Configuration.
 const MODEL_RUNTIME = 'transformersjs';
-const MODEL_URL = 'Xenova/all-MiniLM-L6-v2';
+const MODEL_URL = 'huggingworld/all-MiniLM-L6-v2';
 const SEQ_LENGTH = 128;
-const TOKENIZER = 'onnx-community/embeddinggemma-300m-ONNX';
+const TOKENIZER = 'huggingworld/embeddinggemma-300m-ONNX';
 const EMBEDDING_MODEL_CONFIG = {
   runtime: MODEL_RUNTIME,
   url: MODEL_URL,
@@ -199,29 +199,7 @@ However please note this model is faster for a few reasons:
 
 ### LiteRT.js Wasm files (optional self host)
 
-See the demo folder in this repo that contains a "wasm" sub folder with all the Web Assembly files needed for the LiteRT.js runtime. You can choose to serve these files yourself and update the config object if you do so but remember to enable CORS headers on your server so the files can be used if you do that. If you are curious to learn more about these files see the [official LiteRT.js documentation](https://ai.google.dev/edge/litert/web).
-
-By default the library pulls in these Wasm files from JSDeliver CDN.
-
-If your hosted version is not in the same location update the config object to specify the new Wasm folder location on your webserver as follows:
-
-```javascript
-// Embedding Model Configuration.
-const MODEL_RUNTIME = 'litertjs';
-const MODEL_URL = 'model/embeddinggemma-300M_seq1024_mixed-precision.tflite';
-const SEQ_LENGTH = 1024;
-const TOKENIZER = 'onnx-community/embeddinggemma-300m-ONNX';
-const EMBEDDING_MODEL_CONFIG = {
-  runtime: MODEL_RUNTIME,
-  litertjsWasmUrl: '/wasm', // Specify your path to your custom hosted Wasm files here!
-  url: MODEL_URL,
-  sequenceLength: SEQ_LENGTH,
-  tokenizer: TOKENIZER
-};
-
-// Instantiate VectorSearch Master Class.
-const VECTOR_SEARCH = new VectorSearch(EMBEDDING_MODEL_CONFIG);
-```
+Clone the repo of the space, git clone --depth 1 https://huggingface.co/spaces/huggingworld/vectorsearch-turbo-webgpu, all assets have been included.
 
 Note when you call load you can also optionally specify a HTML element to render loading status updates to like this: