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[Vision] Support Phi-3.5-vision, the first VLM in WebLLM (mlc-ai#563)
This PR supports the first Vision Language Model, Phi-3.5-vision. For a full example, see `examples/vision-model`. Overall usage follows OpenAI API. We add `Phi-3.5-vision-instruct-q4f16_1-MLC` and `Phi-3.5-vision-instruct-q4f16_1-MLC` to prebuilt model list.
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‎examples/README.md‎

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@@ -36,6 +36,7 @@ These examples demonstrate various capabilities via WebLLM's OpenAI-like API.
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- [json-schema](json-schema): besides guaranteeing output to be in JSON, ensure output to adhere to a specific JSON schema specified the user
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- [seed-to-reproduce](seed-to-reproduce): use seeding to ensure reproducible output with fields `seed`.
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- [function-calling](function-calling) (WIP): function calling with fields `tools` and `tool_choice` (with preliminary support).
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- [vision-model](vision-model): process request with image input using Vision Language Model (e.g. Phi3.5-vision)
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#### Chrome Extension
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‎examples/vision-model/README.md‎

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# WebLLM Get Started App
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This folder provides a minimum demo to show WebLLM API in a webapp setting.
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To try it out, you can do the following steps under this folder
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```bash
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npm install
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npm start
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```
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Note if you would like to hack WebLLM core package.
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You can change web-llm dependencies as `"file:../.."`, and follow the build from source
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instruction in the project to build webllm locally. This option is only recommended
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if you would like to hack WebLLM core package.

‎examples/vision-model/package.json‎

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{
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"name": "get-started",
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"version": "0.1.0",
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"private": true,
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"scripts": {
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"start": "parcel src/vision_model.html --port 8888",
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"build": "parcel build src/vision_model.html --dist-dir lib"
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},
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"devDependencies": {
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"buffer": "^5.7.1",
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"parcel": "^2.8.3",
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"process": "^0.11.10",
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"tslib": "^2.3.1",
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"typescript": "^4.9.5",
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"url": "^0.11.3"
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},
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"dependencies": {
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"@mlc-ai/web-llm": "file:../.."
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}
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}

‎examples/vision-model/src/utils.ts‎

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export function getImageDataFromURL(url: string): Promise<ImageData> {
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return new Promise((resolve, reject) => {
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// Converts img to any, and later `as CanvasImageSource`, otherwise build complains
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const img: any = new Image();
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img.crossOrigin = "anonymous"; // Important for CORS
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img.onload = () => {
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const canvas: HTMLCanvasElement = document.createElement("canvas");
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const ctx: CanvasRenderingContext2D = canvas.getContext("2d")!;
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canvas.width = img.width;
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canvas.height = img.height;
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ctx.drawImage(img as CanvasImageSource, 0, 0);
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const imageData = ctx.getImageData(0, 0, img.width, img.height);
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resolve(imageData);
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};
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img.onerror = () => reject(new Error("Failed to load image"));
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img.src = url;
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});
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}
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export async function imageURLToBase64(url: string): Promise<string> {
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const imageData: ImageData = await getImageDataFromURL(url);
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const canvas = document.createElement("canvas");
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const ctx = canvas.getContext("2d");
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canvas.width = imageData.width;
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canvas.height = imageData.height;
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ctx!.putImageData(imageData, 0, 0);
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return canvas.toDataURL();
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}

‎examples/vision-model/src/vision_model.html‎

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<!doctype html>
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<html>
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<script>
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webLLMGlobal = {};
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</script>
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<body>
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<h2>WebLLM Test Page</h2>
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Open console to see output
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<br />
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<br />
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<label id="init-label"> </label>
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<h3>Prompt</h3>
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<label id="prompt-label"> </label>
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<h3>Response</h3>
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<label id="generate-label"> </label>
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<br />
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<label id="stats-label"> </label>
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<script type="module" src="./vision_model.ts"></script>
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</body>
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</html>

‎examples/vision-model/src/vision_model.ts‎

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import * as webllm from "@mlc-ai/web-llm";
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import { imageURLToBase64 } from "./utils";
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function setLabel(id: string, text: string) {
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const label = document.getElementById(id);
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if (label == null) {
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throw Error("Cannot find label " + id);
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}
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label.innerText = text;
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}
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const proxyUrl = "https://cors-anywhere.herokuapp.com/";
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const url_https_street = "https://www.ilankelman.org/stopsigns/australia.jpg";
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const url_https_tree = "https://www.ilankelman.org/sunset.jpg";
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const url_https_sea =
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"https://www.islandvulnerability.org/index/silhouette.jpg";
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async function main() {
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// can feed request with either base64 or http url
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const url_base64_street = await imageURLToBase64(proxyUrl + url_https_street);
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const initProgressCallback = (report: webllm.InitProgressReport) => {
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setLabel("init-label", report.text);
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};
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const selectedModel = "Phi-3.5-vision-instruct-q4f16_1-MLC";
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const engine: webllm.MLCEngineInterface = await webllm.CreateMLCEngine(
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selectedModel,
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{
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initProgressCallback: initProgressCallback,
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logLevel: "INFO", // specify the log level
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},
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{
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context_window_size: 6144,
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},
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);
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// 1. Single image input (with choices)
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const messages: webllm.ChatCompletionMessageParam[] = [
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{
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role: "user",
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content: [
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{ type: "text", text: "List the items in each image concisely." },
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{
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type: "image_url",
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image_url: {
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url: url_base64_street,
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},
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},
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{
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type: "image_url",
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image_url: {
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url: proxyUrl + url_https_sea,
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},
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},
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],
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},
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];
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const request0: webllm.ChatCompletionRequest = {
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stream: false, // can be streaming, same behavior
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messages: messages,
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};
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const reply0 = await engine.chat.completions.create(request0);
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const replyMessage0 = await engine.getMessage();
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console.log(reply0);
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console.log(replyMessage0);
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console.log(reply0.usage);
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// 2. A follow up text-only question
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messages.push({ role: "assistant", content: replyMessage0 });
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messages.push({ role: "user", content: "What is special about each image?" });
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const request1: webllm.ChatCompletionRequest = {
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stream: false, // can be streaming, same behavior
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messages: messages,
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};
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const reply1 = await engine.chat.completions.create(request1);
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const replyMessage1 = await engine.getMessage();
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console.log(reply1);
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console.log(replyMessage1);
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console.log(reply1.usage);
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// 3. A follow up multi-image question
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messages.push({ role: "assistant", content: replyMessage1 });
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messages.push({
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role: "user",
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content: [
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{ type: "text", text: "What about this image? Answer concisely." },
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{
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type: "image_url",
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image_url: { url: proxyUrl + url_https_tree },
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},
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],
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});
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const request2: webllm.ChatCompletionRequest = {
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stream: false, // can be streaming, same behavior
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messages: messages,
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};
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const reply2 = await engine.chat.completions.create(request2);
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const replyMessage2 = await engine.getMessage();
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console.log(reply2);
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console.log(replyMessage2);
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console.log(reply2.usage);
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}
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main();

‎src/config.ts‎

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export enum ModelType {
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"LLM",
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"embedding",
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"VLM", // vision-language model
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}
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/**
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context_window_size: 1024,
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},
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},
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// Phi-3.5-vision-instruct
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{
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model:
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"https://huggingface.co/mlc-ai/Phi-3.5-vision-instruct-q4f16_1-MLC",
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model_id: "Phi-3.5-vision-instruct-q4f16_1-MLC",
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model_lib:
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modelLibURLPrefix +
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modelVersion +
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"/Phi-3.5-vision-instruct-q4f16_1-ctx4k_cs2k-webgpu.wasm",
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vram_required_MB: 3952.18,
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low_resource_required: true,
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overrides: {
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context_window_size: 4096,
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},
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model_type: ModelType.VLM,
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},
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{
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model:
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"https://huggingface.co/mlc-ai/Phi-3.5-vision-instruct-q4f32_1-MLC",
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model_id: "Phi-3.5-vision-instruct-q4f32_1-MLC",
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model_lib:
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modelLibURLPrefix +
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modelVersion +
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"/Phi-3.5-vision-instruct-q4f32_1-ctx4k_cs2k-webgpu.wasm",
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vram_required_MB: 5879.84,
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low_resource_required: true,
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overrides: {
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context_window_size: 4096,
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},
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model_type: ModelType.VLM,
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},
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// Mistral variants
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{
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model:

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