diff --git a/.all-contributorsrc b/.all-contributorsrc
index f6ea4f17..888341cb 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -241,6 +241,20 @@
         "maintenance",
         "code"
       ]
+    },
+    {
+      "login": "haZya",
+      "name": "Hasitha Wickramasinghe",
+      "avatar_url": "https://avatars.githubusercontent.com/u/63403456?v=4",
+      "profile": "https://hazya.dev",
+      "contributions": [
+        "code",
+        "doc",
+        "example",
+        "ideas",
+        "infra",
+        "test"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 00000000..2b360231
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,19 @@
+# top-most EditorConfig file
+root = true
+
+# all files
+[*]
+end_of_line = lf
+insert_final_newline = true
+indent_style = space
+indent_size = 2
+charset = utf-8
+trim_trailing_whitespace = true
+max_line_length = 80
+quote_type = double
+
+[*.{js,ts}]
+quote_type = double
+curly_bracket_next_line = false
+spaces_around_brackets = inside
+indent_brace_style = BSD KNF
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 6574ff89..97078704 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,5 +10,8 @@ examples/manual-testing/data/*
 !examples/manual-testing/data/images/
 !examples/manual-testing/data/videos/
 
+/models/**/*.js
+/models/**/*.min.js
+
 # Local Netlify folder
 .netlify
diff --git a/.npmignore b/.npmignore
index c95da7bb..edb692a3 100644
--- a/.npmignore
+++ b/.npmignore
@@ -17,11 +17,27 @@ _art/
 yarn-error.log
 bundle.js
 .DS_Store
+.all-contributorsrc
+.editorconfig
+.gitignore
+bundle-model.mjs
+jest.config.js
+tsconfig.cjs.json
+tsconfig.esm.json
+tsconfig.json
+.circleci/
+.github/
+__tests__/
+
+# Models
+models/
 
 # Ignore intermediate browserify files
 # Mountpoint
-dist/nsfwjs.d.ts
-dist/nsfwjs.js
+dist/cjs/nsfwjs.d.ts
+dist/cjs/nsfwjs.js
+dist/esm/nsfwjs.d.ts
+dist/esm/nsfwjs.js
 # Unminified bundle
-dist/bundle.js
+dist/browser/bundle.js
 
diff --git a/README.md b/README.md
index a08531d9..71ec6b1b 100644
--- a/README.md
+++ b/README.md
@@ -16,16 +16,18 @@ Why would this be useful? [Check out the announcement blog post](https://shift.i
 </p>
 
 ## **Table of Contents**
+
 <!-- START doctoc generated TOC please keep comment here to allow auto update -->
 <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
 
 - [QUICK: How to use the module](#quick-how-to-use-the-module)
 - [Library API](#library-api)
-    - [`load` the model](#load-the-model)
-    - [`classify` an image](#classify-an-image)
+  - [`load` the model](#load-the-model)
+  - [Caching](#caching)
+  - [`classify` an image](#classify-an-image)
 - [Production](#production)
 - [Install](#install)
-    - [Host your own model](#host-your-own-model)
+  - [Host your own model](#host-your-own-model)
 - [Run the Examples](#run-the-examples)
   - [Tensorflow.js in the browser](#tensorflowjs-in-the-browser)
   - [Browserify](#browserify)
@@ -34,8 +36,8 @@ Why would this be useful? [Check out the announcement blog post](https://shift.i
   - [NSFW Filter](#nsfw-filter)
 - [Learn TensorFlow.js](#learn-tensorflowjs)
 - [More!](#more)
-    - [Open Source](#open-source)
-    - [Need the experts? Hire Infinite Red for your next project](#need-the-experts-hire-infinite-red-for-your-next-project)
+  - [Open Source](#open-source)
+  - [Need the experts? Hire Infinite Red for your next project](#need-the-experts-hire-infinite-red-for-your-next-project)
 - [Contributors](#contributors)
 
 <!-- END doctoc generated TOC please keep comment here to allow auto update -->
@@ -48,79 +50,94 @@ The library categorizes image probabilities in the following 5 classes:
 - `Porn` - pornographic images, sexual acts
 - `Sexy` - sexually explicit images, not pornography
 
-> _The demo is a continuous deployment source - Give it a go: http://nsfwjs.com/_
+> _The demo is a continuous deployment source - Give it a go: http://nsfwjs.com_
 
 ## QUICK: How to use the module
 
 With `async/await` support:
 
 ```js
-import * as nsfwjs from 'nsfwjs'
+import * as nsfwjs from "nsfwjs";
 
-const img = document.getElementById('img')
+const img = document.getElementById("img");
 
-// Load model from my S3.
-// See the section hosting the model files on your site.
-const model = await nsfwjs.load()
+// If you want to host models on your own or use different model from the ones available, see the section "Host your own model".
+const model = await nsfwjs.load();
 
 // Classify the image
-const predictions = await model.classify(img)
-console.log('Predictions: ', predictions)
+const predictions = await model.classify(img);
+console.log("Predictions: ", predictions);
 ```
 
 Without `async/await` support:
 
 ```js
-import * as nsfwjs from 'nsfwjs'
+import * as nsfwjs from "nsfwjs";
 
-const img = document.getElementById('img')
+const img = document.getElementById("img");
 
-// Load model from my S3.
-// See the section hosting the model files on your site.
-nsfwjs.load()
+// If you want to host models on your own or use different model from the ones available, see the section "Host your own model".
+nsfwjs
+  .load()
   .then(function (model) {
     // Classify the image
-    return model.classify(img)
+    return model.classify(img);
   })
   .then(function (predictions) {
-    console.log('Predictions: ', predictions)
-  })
+    console.log("Predictions: ", predictions);
+  });
 ```
 
 ## Library API
 
-#### `load` the model
+### `load` the model
+
+Before you can classify any image, you'll need to load the model.
+
+```js
+const model = nsfwjs.load(); // Default: "MobileNetV2"
+```
+
+You can use the optional first parameter to specify which model you want to use from the three that are bundled together. Defaults to: `"MobileNetV2"`. This supports tree-shaking on supported bundlers like Webpack, so you will only be loading the model you are using.
+
+```js
+const model = nsfwjs.load("MobileNetV2Mid"); // "MobileNetV2" | "MobileNetV2Mid" | "InceptionV3"
+```
 
-Before you can classify any image, you'll need to load the model. You should use the optional first parameter and load the model from your website, as explained in the install directions.
+You can also use same parameter and load the model from your website/server, as explained in the [Host your own model](#host-your-own-model) section. Doing so could reduce the bundle size for loading the model by approximately 1.33 times (33%) since you can directly use the binary files instead of the base64 that are bundled with the package. i.e. The `"MobileNetV2"` model bundled into the package is 3.5MB instead of 2.6MB for hosted binary files. This would only make a difference if you are loading the model every time (without [Caching](#caching)) on the client-side browser since on the server-side, you'd only be loading the model once at the server start.
 
-Model example - [224x224](https://github.com/infinitered/nsfwjs/blob/master/examples/nsfw_demo/public/quant_nsfw_mobilenet/)
+Model MobileNetV2 - [224x224](https://github.com/infinitered/nsfwjs/blob/master/models/mobilenet_v2/)
 
 ```js
-const model = nsfwjs.load('/path/to/model/directory/')
+const model = nsfwjs.load("/path/to/model/directory/");
 ```
 
 If you're using a model that needs an image of dimension other than 224x224, you can pass the size in the options parameter.
 
-Model example - [299x299](https://github.com/infinitered/nsfwjs/tree/master/examples/nsfw_demo/public/model)
+Model MobileNetV2Mid - [299x299](https://github.com/infinitered/nsfwjs/tree/master/models/inception_v3)
 
 ```js
-const model = nsfwjs.load('/path/to/different/model/', { size: 299 })
+const model = nsfwjs.load("/path/to/different/model/", { size: 299 });
 ```
 
 If you're using a graph model, you cannot use the infer method, and you'll need to tell model load that you're dealing with a graph model in options.
 
-Model example - [Graph](https://github.com/infinitered/nsfwjs/tree/master/examples/nsfw_demo/public/quant_mid)
+Model InceptionV3 - [Graph](https://github.com/infinitered/nsfwjs/tree/master/models/mobilenet_v2_mid)
 
 ```js
-const model = nsfwjs.load('/path/to/different/model/', { type: 'graph' })
+const model = nsfwjs.load("/path/to/different/model/", { type: "graph" });
 ```
 
+### Caching
+
 If you're using in the browser and you'd like to subsequently load from indexed db or local storage you can save the underlying model using the appropriate scheme and load from there.
 
 ```js
-const initialLoad = await nsfwjs.load('/path/to/different/model')
-await initialLoad.model.save('indexeddb://model')
-const model = await nsfwjs.load('indexeddb://model')
+const initialLoad = await nsfwjs.load(
+  "/path/to/different/model" /*{ ...options }*/
+);
+await initialLoad.model.save("indexeddb://model");
+const model = await nsfwjs.load("indexeddb://model" /*{ ...options }*/);
 ```
 
 **Parameters**
@@ -132,13 +149,13 @@ const model = await nsfwjs.load('indexeddb://model')
 
 - Ready to use NSFWJS model object
 
-#### `classify` an image
+### `classify` an image
 
 This function can take any browser-based image elements (`<img>`, `<video>`, `<canvas>`) and returns an array of most likely predictions and their confidence levels.
 
 ```js
 // Return top 3 guesses (instead of all 5)
-const predictions = await model.classify(img, 3)
+const predictions = await model.classify(img, 3);
 ```
 
 **Parameters**
@@ -155,14 +172,14 @@ const predictions = await model.classify(img, 3)
 Tensorflow.js offers two flags, `enableProdMode` and `enableDebugMode`. If you're going to use NSFWJS in production, be sure to enable prod mode before loading the NSFWJS model.
 
 ```js
-import * as tf from '@tensorflow/tfjs'
-import * as nsfwjs from 'nsfwjs'
-tf.enableProdMode()
+import * as tf from "@tensorflow/tfjs";
+import * as nsfwjs from "nsfwjs";
+tf.enableProdMode();
 //...
-let model = await nsfwjs.load(`${urlToNSFWJSModel}`)
+let model = await nsfwjs.load(`${urlToNSFWJSModel}`);
 ```
 
-**NOTE:** Please be sure to download and host the model before releasing to production ([instructions](#host-your-own-model)). The hosted model can be moved without notice.
+**NOTE:** Consider downloading and hosting the model yourself before moving to production as explained in the [Host your own model](#host-your-own-model) section. This could potentially improve the initial load time of the model. Furthermore, consider [Caching](#caching) the model, if you are using it in the browser.
 
 ## Install
 
@@ -175,22 +192,23 @@ $ yarn add @tensorflow/tfjs
 $ yarn add nsfwjs
 ```
 
-For script tags add `<script type="text/javascript" src="https://unpkg.com/nsfwjs"></script>`. Then simply access the nsfwjs global variable. This requires that you've already imported TensorFlow.js as well.
+For script tags include all the bundles as shown [here](#browserify). Then simply access the nsfwjs global variable. This requires that you've already imported TensorFlow.js as well.
 
-#### Host your own model
+### Host your own model
 
-The magic that powers NSFWJS is the [NSFW detection model](https://github.com/gantman/nsfw_model). By default, this node module is pulling from my S3, but I make no guarantees that I'll keep that download link available forever. It's best for the longevity of your project that you download and host your own version of [the model files](https://github.com/infinitered/nsfwjs/tree/master/examples/nsfw_demo/public/model). You can then pass the relative URL to your hosted files in the `load` function. If you can come up with a way to bundle the model into the NPM package, I'd love to see a PR to this repo!
+The magic that powers NSFWJS is the [NSFW detection model](https://github.com/gantman/nsfw_model). By default, the models are bundled into this package. But you may want to host the models on your own server to reduce bundle size by loading them as raw binary files or to host your own custom model. If you want to host your own version of [the model files](https://github.com/infinitered/nsfwjs/tree/master/models), you can do so by following the steps below. You can then pass the relative URL to your hosted files in the `load` function along with the `options` if necessary.
 
 Here is how to install the default model on a website:
-1. Download the project directly into a subdomain such as labs.site.com: `git clone https://github.com/infinitered/nsfwjs.git`. Do not download it as a zip file or manually; this is important.
-2. Retrieve the URL (example: https://labs.site.com/nsfwjs/examples/nsfw_demo/public/quant_nsfw_mobilenet/) and put it into `nsfwjs.load('<url>')`.
-3. It should work normally by launching `node server`.
+
+1. Download the project by either downloading as zip or cloning `git clone https://github.com/infinitered/nsfwjs.git`. **_If downloading as zip does not work use cloning._**
+2. Extract the `models` folder from the root of the project and drop it in the `public` directory of your web application to serve them as static files along side your website. (You can host it anywhere such as on a s3 bucket as long as you can access it via URL).
+3. Retrieve the URL and put it into `nsfwjs.load()`. For example: `nsfwjs.load(https://yourwebsite.com/models/mobilenet_v2/model.json)`.
 
 ## Run the Examples
 
 ### Tensorflow.js in the browser
 
-The demo that powers https://nsfwjs.com/ is available in the `nsfw_demo` example folder.
+The demo that powers https://nsfwjs.com/ is available in the `examples/nsfw_demo` folder.
 
 To run the demo, run `yarn prep` which will copy the latest code into the demo. After that's done, you can `cd` into the demo folder and run with `yarn start`.
 
@@ -198,7 +216,14 @@ To run the demo, run `yarn prep` which will copy the latest code into the demo.
 
 A browserified version using nothing but promises and script tags is available in the `minimal_demo` folder.
 
-Please do not use the script tags hosted in this demo as a CDN. This can and should be hosted in your project along side the model files.
+```js
+<script src="/path/to/model/directory/model.min.js"></script>
+<script src="/path/to/model/directory/group1-shard1of2.min.js"></script>
+<script src="/path/to/model/directory/group1-shard2of2.min.js"></script>
+<script src="/path/to/bundle/nsfwjs.min.js"></script>
+```
+
+You should host the `nsfwjs.min.js` file and all the model bundles that you want to use alongside your project, and reference them using the `src` attribute in the script tags.
 
 ### React Native
 
@@ -217,70 +242,70 @@ $ npm install @tensorflow/tfjs-node
 ```
 
 ```javascript
-const axios = require('axios') //you can use any http client
-const tf = require('@tensorflow/tfjs-node')
-const nsfw = require('nsfwjs')
+const axios = require("axios"); //you can use any http client
+const tf = require("@tensorflow/tfjs-node");
+const nsfw = require("nsfwjs");
 async function fn() {
   const pic = await axios.get(`link-to-picture`, {
-    responseType: 'arraybuffer',
-  })
-  const model = await nsfw.load() // To load a local model, nsfw.load('file://./path/to/model/')
+    responseType: "arraybuffer",
+  });
+  const model = await nsfw.load(); // To load a local model, nsfw.load('file://./path/to/model/')
   // Image must be in tf.tensor3d format
   // you can convert image to tf.tensor3d with tf.node.decodeImage(Uint8Array,channels)
-  const image = await tf.node.decodeImage(pic.data,3)
-  const predictions = await model.classify(image)
-  image.dispose() // Tensor memory must be managed explicitly (it is not sufficient to let a tf.Tensor go out of scope for its memory to be released).
-  console.log(predictions)
+  const image = await tf.node.decodeImage(pic.data, 3);
+  const predictions = await model.classify(image);
+  image.dispose(); // Tensor memory must be managed explicitly (it is not sufficient to let a tf.Tensor go out of scope for its memory to be released).
+  console.log(predictions);
 }
-fn()
+fn();
 ```
 
 Here is another full example of a [multipart/form-data POST using Express](examples/node_demo), supposing you are using JPG format.
 
 ```javascript
-const express = require('express')
-const multer = require('multer')
-const jpeg = require('jpeg-js')
+const express = require("express");
+const multer = require("multer");
+const jpeg = require("jpeg-js");
 
-const tf = require('@tensorflow/tfjs-node')
-const nsfw = require('nsfwjs')
+const tf = require("@tensorflow/tfjs-node");
+const nsfw = require("nsfwjs");
 
-const app = express()
-const upload = multer()
+const app = express();
+const upload = multer();
 
-let _model
+let _model;
 
 const convert = async (img) => {
   // Decoded image in UInt8 Byte array
-  const image = await jpeg.decode(img, { useTArray: true })
+  const image = await jpeg.decode(img, { useTArray: true });
 
-  const numChannels = 3
-  const numPixels = image.width * image.height
-  const values = new Int32Array(numPixels * numChannels)
+  const numChannels = 3;
+  const numPixels = image.width * image.height;
+  const values = new Int32Array(numPixels * numChannels);
 
   for (let i = 0; i < numPixels; i++)
     for (let c = 0; c < numChannels; ++c)
-      values[i * numChannels + c] = image.data[i * 4 + c]
+      values[i * numChannels + c] = image.data[i * 4 + c];
 
-  return tf.tensor3d(values, [image.height, image.width, numChannels], 'int32')
-}
+  return tf.tensor3d(values, [image.height, image.width, numChannels], "int32");
+};
 
-app.post('/nsfw', upload.single('image'), async (req, res) => {
-  if (!req.file) res.status(400).send('Missing image multipart/form-data')
+app.post("/nsfw", upload.single("image"), async (req, res) => {
+  if (!req.file) res.status(400).send("Missing image multipart/form-data");
   else {
-    const image = await convert(req.file.buffer)
-    const predictions = await _model.classify(image)
-    image.dispose()
-    res.json(predictions)
+    const image = await convert(req.file.buffer);
+    const predictions = await _model.classify(image);
+    image.dispose();
+    res.json(predictions);
   }
-})
+});
 
 const load_model = async () => {
-  _model = await nsfw.load()
-}
+  _model = await nsfw.load();
+};
 
 // Keep the model in memory, make sure it's loaded only once
-load_model().then(() => app.listen(8080))
+load_model().then(() => app.listen(8080));
 
 // curl --request POST localhost:8080/nsfw --header 'Content-Type: multipart/form-data' --data-binary 'image=@/full/path/to/picture.jpg'
 ```
@@ -309,11 +334,11 @@ More about NSFWJS and TensorFlow.js - https://youtu.be/uzQwmZwy3yw
 
 The [model was trained in Keras over several days](https://medium.freecodecamp.org/how-to-set-up-nsfw-content-detection-with-machine-learning-229a9725829c) and 60+ Gigs of data. Be sure to [check out the model code](https://github.com/GantMan/nsfw_model) which was trained on data provided by [Alexander Kim's](https://github.com/alexkimxyz) [nsfw_data_scraper](https://github.com/alexkimxyz/nsfw_data_scraper).
 
-#### Open Source
+### Open Source
 
 NSFWJS, as open source, is free to use and always will be :heart:. It's MIT licensed, and we'll always do our best to help and quickly answer issues. If you'd like to get a hold of us, join our [community slack](http://community.infinite.red).
 
-#### Need the experts? Hire Infinite Red for your next project
+### Need the experts? Hire Infinite Red for your next project
 
 If your project's calling for the experts in all things React Native, Infinite Red’s here to help! Our experienced team of software engineers have worked with companies like Microsoft, Zoom, and Mercari to bring even some of the most complex projects to life.
 
@@ -359,6 +384,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://janpoonthong.github.io/portfolio/"><img src="https://avatars.githubusercontent.com/u/56725335?v=4?s=100" width="100px;" alt="jan"/><br /><sub><b>jan</b></sub></a><br /><a href="https://github.com/infinitered/nsfwjs/commits?author=JanPoonthong" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/roerohan"><img src="https://avatars.githubusercontent.com/u/42958812?v=4?s=100" width="100px;" alt="Rohan Mukherjee"/><br /><sub><b>Rohan Mukherjee</b></sub></a><br /><a href="#question-roerohan" title="Answering Questions">💬</a> <a href="#infra-roerohan" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-roerohan" title="Maintenance">🚧</a> <a href="https://github.com/infinitered/nsfwjs/commits?author=roerohan" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://hazya.dev"><img src="https://avatars.githubusercontent.com/u/63403456?v=4?s=100" width="100px;" alt="Hasitha Wickramasinghe"/><br /><sub><b>Hasitha Wickramasinghe</b></sub></a><br /><a href="https://github.com/infinitered/nsfwjs/commits?author=haZya" title="Code">💻</a> <a href="https://github.com/infinitered/nsfwjs/commits?author=haZya" title="Documentation">📖</a> <a href="#example-haZya" title="Examples">💡</a> <a href="#ideas-haZya" title="Ideas, Planning, & Feedback">🤔</a> <a href="#infra-haZya" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/infinitered/nsfwjs/commits?author=haZya" title="Tests">⚠️</a></td>
     </tr>
   </tbody>
 </table>
diff --git a/__tests__/index.ts b/__tests__/index.ts
index 4bb654d3..06b5b2ef 100644
--- a/__tests__/index.ts
+++ b/__tests__/index.ts
@@ -1,41 +1,55 @@
-import * as tf from '@tensorflow/tfjs'
-import { load } from '../src/index'
+import * as tf from "@tensorflow/tfjs";
+import { ModelName, load } from "../src/index";
 
-// Fix for JEST
-const globalAny: any = global
-globalAny.fetch = require('node-fetch')
-const timeoutMS = 10000
+const timeoutMS = 10000;
 
 it(
   "NSFWJS classify doesn't leak",
   async () => {
-    const model = await load()
-    const x = tf.zeros([224, 224, 3]) as tf.Tensor3D
-    const numTensorsBefore = tf.memory().numTensors
-    await model.classify(x)
-    expect(tf.memory().numTensors).toBe(numTensorsBefore)
+    const model = await load();
+    const x = tf.zeros([224, 224, 3]) as tf.Tensor3D;
+    const numTensorsBefore = tf.memory().numTensors;
+    await model.classify(x);
+    expect(tf.memory().numTensors).toBe(numTensorsBefore);
   },
   timeoutMS
-)
+);
 
 it(
   "NSFWJS infer doesn't leak",
   async () => {
-    const model = await load()
-    const x = tf.zeros([224, 224, 3]) as tf.Tensor3D
-    const numTensorsBefore = tf.memory().numTensors
-    model.infer(x)
-    expect(tf.memory().numTensors).toBe(numTensorsBefore + 1)
+    const model = await load();
+    const x = tf.zeros([224, 224, 3]) as tf.Tensor3D;
+    const numTensorsBefore = tf.memory().numTensors;
+    model.infer(x);
+    expect(tf.memory().numTensors).toBe(numTensorsBefore + 1);
   },
   timeoutMS
-)
+);
 
 it(
-  "NSFWJS console warns user if no host model is passed",
+  "NSFWJS console informs user if no 'modelOrUrl' parameter is passed",
   async () => {
-    const consoleWarnSpy = jest.spyOn(console, 'warn')
-    await load()
-    expect(consoleWarnSpy).toBeCalled()
+    const consoleInfoSpy = jest.spyOn(console, "info");
+    await load();
+    expect(consoleInfoSpy).toBeCalledWith(
+      `%cBy not specifying 'modelOrUrl' parameter, you're using the default model: 'MobileNetV2'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model).`,
+      "color: lightblue"
+    );
   },
   timeoutMS
-)
+);
+
+it(
+  "NSFWJS console informs user if no hosted model is passed",
+  async () => {
+    const modelOrUrl: ModelName = "MobileNetV2";
+    const consoleInfoSpy = jest.spyOn(console, "info");
+    await load(modelOrUrl);
+    expect(consoleInfoSpy).toBeCalledWith(
+      `%cYou're using the model: '${modelOrUrl}'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model).`,
+      "color: lightblue"
+    );
+  },
+  timeoutMS
+);
diff --git a/__tests__/regressionCheck.ts b/__tests__/regressionCheck.ts
index a0b28b78..f0f37742 100644
--- a/__tests__/regressionCheck.ts
+++ b/__tests__/regressionCheck.ts
@@ -1,23 +1,23 @@
 import * as tf from "@tensorflow/tfjs";
-import { load } from "../src/index";
 import { exec } from "child_process";
-const fs = require("fs");
-const jpeg = require("jpeg-js");
+import fs from "fs";
+import jpeg from "jpeg-js";
+import { load } from "../src/index";
 
 // Fix for JEST
-const globalAny: any = global;
-globalAny.fetch = require("node-fetch");
 const timeoutMS = 10000;
 const NUMBER_OF_CHANNELS = 3;
 
 const readImage = (path: string) => {
   const buf = fs.readFileSync(path);
-  const pixels = jpeg.decode(buf, true);
+  const pixels = jpeg.decode(buf, { useTArray: true });
   return pixels;
 };
 
-// @ts-ignore
-const imageByteArray = (image, numChannels: number) => {
+const imageByteArray = (
+  image: ReturnType<typeof readImage>,
+  numChannels: number
+) => {
   const pixels = image.data;
   const numPixels = image.width * image.height;
   const values = new Int32Array(numPixels * numChannels);
@@ -31,8 +31,10 @@ const imageByteArray = (image, numChannels: number) => {
   return values;
 };
 
-// @ts-ignore
-const imageToInput = (image, numChannels: number) => {
+const imageToInput = (
+  image: ReturnType<typeof readImage>,
+  numChannels: number
+) => {
   const values = imageByteArray(image, numChannels);
   const outShape = [image.height, image.width, numChannels] as [
     number,
@@ -57,11 +59,11 @@ it(
 );
 
 it(
-  "Bundles and minifies",
+  "Builds, bundles and minifies",
   (done) => {
-    const cmd = "yarn scriptbundle && yarn minbundle";
+    const cmd = "yarn bundle";
     exec(cmd, (err) => {
-      if (err) done.fail("Failed to bundle and minify");
+      if (err) done.fail("Failed to build, bundle and minify");
       // All good!
       done();
     });
diff --git a/examples/manual-testing/README.md b/examples/manual_testing/README.md
similarity index 100%
rename from examples/manual-testing/README.md
rename to examples/manual_testing/README.md
diff --git a/examples/manual-testing/data/animations/smile.webp b/examples/manual_testing/data/animations/smile.webp
similarity index 100%
rename from examples/manual-testing/data/animations/smile.webp
rename to examples/manual_testing/data/animations/smile.webp
diff --git a/examples/manual-testing/data/images/cat.svg b/examples/manual_testing/data/images/cat.svg
similarity index 100%
rename from examples/manual-testing/data/images/cat.svg
rename to examples/manual_testing/data/images/cat.svg
diff --git a/examples/manual-testing/data/images/glasses.png b/examples/manual_testing/data/images/glasses.png
similarity index 100%
rename from examples/manual-testing/data/images/glasses.png
rename to examples/manual_testing/data/images/glasses.png
diff --git a/examples/manual-testing/data/images/house.webp b/examples/manual_testing/data/images/house.webp
similarity index 100%
rename from examples/manual-testing/data/images/house.webp
rename to examples/manual_testing/data/images/house.webp
diff --git a/examples/manual-testing/data/images/smile.jpg b/examples/manual_testing/data/images/smile.jpg
similarity index 100%
rename from examples/manual-testing/data/images/smile.jpg
rename to examples/manual_testing/data/images/smile.jpg
diff --git a/examples/manual-testing/data/index.html b/examples/manual_testing/data/index.html
similarity index 89%
rename from examples/manual-testing/data/index.html
rename to examples/manual_testing/data/index.html
index 309af05a..f6c3c118 100644
--- a/examples/manual-testing/data/index.html
+++ b/examples/manual_testing/data/index.html
@@ -90,10 +90,13 @@ <h3>Animations</h3>
       src="https://unpkg.com/@tensorflow/tfjs@1.7.4"
       type="text/javascript"
     ></script>
-    <script src="dist/nsfwjs.min.js"></script>
+    <script src="../../../dist/models/mobilenet_v2_mid/model.min.js" type="text/javascript"></script>
+    <script src="../../../dist/models/mobilenet_v2_mid/group1-shard1of2.min.js" type="text/javascript"></script>
+    <script src="../../../dist/models/mobilenet_v2_mid/group1-shard2of2.min.js" type="text/javascript"></script>
+    <script src="../../../dist/browser/nsfwjs.min.js"></script>
 
     <script>
-      nsfwjs.load().then(model => {
+      nsfwjs.load("MobileNetV2Mid").then(model => {
         const images = document.querySelectorAll('img')
         for (let i = 0; i < images.length; i++) {
           if (images[i].classList[0] === 'images') {
diff --git a/examples/manual-testing/default.conf b/examples/manual_testing/default.conf
similarity index 100%
rename from examples/manual-testing/default.conf
rename to examples/manual_testing/default.conf
diff --git a/examples/manual-testing/docker-compose.yml b/examples/manual_testing/docker-compose.yml
similarity index 100%
rename from examples/manual-testing/docker-compose.yml
rename to examples/manual_testing/docker-compose.yml
diff --git a/examples/minimal_demo/index.html b/examples/minimal_demo/index.html
index 4665a6d2..31bffffd 100644
--- a/examples/minimal_demo/index.html
+++ b/examples/minimal_demo/index.html
@@ -3,10 +3,21 @@
   src="https://unpkg.com/@tensorflow/tfjs@2.6.0"
   type="text/javascript"
 ></script>
-<script src="https://unpkg.com/nsfwjs@2.3.0" type="text/javascript"></script>
-
-<!-- For testing: Load from local bundle `yarn scriptbundle && yarn minbundle` -->
-<!-- <script src="../../dist/nsfwjs.min.js"></script> -->
+<!-- <script src="https://unpkg.com/nsfwjs@2.3.0" type="text/javascript"></script> -->
+<!-- For testing: Load from local bundle `yarn bundle` -->
+<script
+  src="../../dist/models/mobilenet_v2_mid/model.min.js"
+  type="text/javascript"
+></script>
+<script
+  src="../../dist/models/mobilenet_v2_mid/group1-shard1of2.min.js"
+  type="text/javascript"
+></script>
+<script
+  src="../../dist/models/mobilenet_v2_mid/group1-shard2of2.min.js"
+  type="text/javascript"
+></script>
+<script src="../../dist/browser/nsfwjs.min.js" type="text/javascript"></script>
 
 <script>
   // const nsfwjs = require('nsfwjs')
@@ -15,12 +26,16 @@
   // some image here
   img.src = "https://i.imgur.com/Kwxetau.jpg";
 
-  // Load the model.
-  nsfwjs.load().then((model) => {
-    // Classify the image.
-    model.classify(img).then((predictions) => {
-      console.log("Predictions", predictions);
+  img.onload = function () {
+    // Load the model.
+    tf.setBackend("cpu"); //! https://github.com/tensorflow/tfjs/issues/1644
+
+    nsfwjs.load("MobileNetV2Mid").then((model) => {
+      // Classify the image.
+      model.classify(img).then((predictions) => {
+        console.log("Predictions", predictions);
+      });
     });
-  });
+  };
 </script>
 <pre>Checkout console.log output for results!</pre>
diff --git a/examples/nsfw_demo/.gitignore b/examples/nsfw_demo/.gitignore
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/README.md b/examples/nsfw_demo/README.md
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/package.json b/examples/nsfw_demo/package.json
index 2817941c..fc673b64 100644
--- a/examples/nsfw_demo/package.json
+++ b/examples/nsfw_demo/package.json
@@ -22,7 +22,7 @@
   },
   "scripts": {
     "start": "react-app-rewired start",
-    "build": "react-app-rewired --openssl-legacy-provider build",
+    "build": "react-app-rewired build",
     "test": "react-app-rewired test",
     "eject": "react-scripts eject"
   },
diff --git a/examples/nsfw_demo/public/index.html b/examples/nsfw_demo/public/index.html
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/public/manifest.json b/examples/nsfw_demo/public/manifest.json
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/src/App.css b/examples/nsfw_demo/src/App.css
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/src/App.js b/examples/nsfw_demo/src/App.js
old mode 100755
new mode 100644
index 6efe1f1a..2b04d481
--- a/examples/nsfw_demo/src/App.js
+++ b/examples/nsfw_demo/src/App.js
@@ -1,16 +1,16 @@
-import React, { Component } from 'react'
-import logo from './logo.svg'
-import './App.css'
 import * as nsfwjs from 'nsfwjs'
+import React, { Component } from 'react'
 import Dropzone from 'react-dropzone'
 import Webcam from 'react-webcam'
+import './App.css'
+import logo from './logo.svg'
 
 // components
-import Underdrop from './components/Underdrop'
-import Loading from './components/Loading'
-import Header from './components/Header'
 import Footer from './components/Footer'
+import Header from './components/Header'
+import Loading from './components/Loading'
 import Results from './components/Results'
+import Underdrop from './components/Underdrop'
 
 const blurred = { filter: 'blur(30px)', WebkitFilter: 'blur(30px)' }
 const clean = {}
@@ -19,12 +19,6 @@ const dragMessage = 'Drag and drop an image to check'
 const camMessage = 'Cam active'
 const DETECTION_PERIOD = 1000
 
-const availableModels = {
-  mobilenetv2: ['/quant_nsfw_mobilenet/'],
-  mobilenetMid: ['/quant_mid/', { type: 'graph' }],
-  inceptionv3: ['/model/', { size: 299 }],
-}
-
 class App extends Component {
   state = {
     model: null,
@@ -38,7 +32,7 @@ class App extends Component {
     loading: true,
     fileType: null,
     hardReset: false,
-    currentModelName: 'mobilenetMid',
+    currentModelName: 'MobileNetV2Mid',
   }
 
   componentDidMount() {
@@ -49,7 +43,7 @@ class App extends Component {
     this.setState({ currentModelName: value }, this._loadModel)
   }
 
-  _loadModel = () => {
+  _loadModel = async () => {
     this.setState({
       titleMessage: 'Please hold, the model is loading...',
       message: loadingMessage,
@@ -60,8 +54,8 @@ class App extends Component {
       loading: true,
     })
     // Load model from public folder
-    nsfwjs
-      .load(...availableModels[this.state.currentModelName])
+    await nsfwjs
+      .load(this.state.currentModelName)
       .then((model) => {
         this.setState({
           model,
diff --git a/examples/nsfw_demo/src/App.test.js b/examples/nsfw_demo/src/App.test.js
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/src/components/Footer.js b/examples/nsfw_demo/src/components/Footer.js
index d33b8e3a..a9ad76c1 100644
--- a/examples/nsfw_demo/src/components/Footer.js
+++ b/examples/nsfw_demo/src/components/Footer.js
@@ -1,18 +1,18 @@
 import React from 'react'
-import ir from '../ir.svg'
 import Dropdown from 'react-dropdown'
 import 'react-dropdown/style.css'
+import ir from '../ir.svg'
 const options = [
   {
     type: 'group',
     name: 'Mobilenet v2 Model',
     items: [
       {
-        value: 'mobilenetv2',
+        value: 'MobileNetV2',
         label: '90% Accurate - 2.6MB',
       },
       {
-        value: 'mobilenetMid',
+        value: 'MobileNetV2Mid',
         label: '93% Accurate - 4.2MB',
       },
     ],
@@ -22,7 +22,7 @@ const options = [
     name: 'Inception v3 Model',
     items: [
       {
-        value: 'inceptionv3',
+        value: 'InceptionV3',
         label: '93% Accurate - Huge!',
       },
     ],
diff --git a/examples/nsfw_demo/src/index.css b/examples/nsfw_demo/src/index.css
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/src/index.js b/examples/nsfw_demo/src/index.js
old mode 100755
new mode 100644
diff --git a/examples/nsfw_demo/src/serviceWorker.js b/examples/nsfw_demo/src/serviceWorker.js
old mode 100755
new mode 100644
diff --git a/jest.config.js b/jest.config.js
index 3e5ff176..9ef99389 100644
--- a/jest.config.js
+++ b/jest.config.js
@@ -1,5 +1,5 @@
 module.exports = {
-  preset: 'ts-jest',
-  testEnvironment: 'node',
-  testPathIgnorePatterns: ['__mocks__', 'example']
-}
+  preset: "ts-jest",
+  testEnvironment: "node",
+  testPathIgnorePatterns: ["__mocks__", "examples"],
+};
diff --git a/examples/nsfw_demo/public/model/group1-shard1of6 b/models/inception_v3/group1-shard1of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard1of6
rename to models/inception_v3/group1-shard1of6
diff --git a/examples/nsfw_demo/public/model/group1-shard2of6 b/models/inception_v3/group1-shard2of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard2of6
rename to models/inception_v3/group1-shard2of6
diff --git a/examples/nsfw_demo/public/model/group1-shard3of6 b/models/inception_v3/group1-shard3of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard3of6
rename to models/inception_v3/group1-shard3of6
diff --git a/examples/nsfw_demo/public/model/group1-shard4of6 b/models/inception_v3/group1-shard4of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard4of6
rename to models/inception_v3/group1-shard4of6
diff --git a/examples/nsfw_demo/public/model/group1-shard5of6 b/models/inception_v3/group1-shard5of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard5of6
rename to models/inception_v3/group1-shard5of6
diff --git a/examples/nsfw_demo/public/model/group1-shard6of6 b/models/inception_v3/group1-shard6of6
similarity index 100%
rename from examples/nsfw_demo/public/model/group1-shard6of6
rename to models/inception_v3/group1-shard6of6
diff --git a/examples/nsfw_demo/public/model/model.json b/models/inception_v3/model.json
similarity index 100%
rename from examples/nsfw_demo/public/model/model.json
rename to models/inception_v3/model.json
diff --git a/examples/nsfw_demo/public/quant_nsfw_mobilenet/group1-shard1of1 b/models/mobilenet_v2/group1-shard1of1
similarity index 100%
rename from examples/nsfw_demo/public/quant_nsfw_mobilenet/group1-shard1of1
rename to models/mobilenet_v2/group1-shard1of1
diff --git a/examples/nsfw_demo/public/quant_nsfw_mobilenet/model.json b/models/mobilenet_v2/model.json
similarity index 100%
rename from examples/nsfw_demo/public/quant_nsfw_mobilenet/model.json
rename to models/mobilenet_v2/model.json
diff --git a/examples/nsfw_demo/public/quant_mid/group1-shard1of2.bin b/models/mobilenet_v2_mid/group1-shard1of2
old mode 100755
new mode 100644
similarity index 100%
rename from examples/nsfw_demo/public/quant_mid/group1-shard1of2.bin
rename to models/mobilenet_v2_mid/group1-shard1of2
diff --git a/examples/nsfw_demo/public/quant_mid/group1-shard2of2.bin b/models/mobilenet_v2_mid/group1-shard2of2
old mode 100755
new mode 100644
similarity index 100%
rename from examples/nsfw_demo/public/quant_mid/group1-shard2of2.bin
rename to models/mobilenet_v2_mid/group1-shard2of2
diff --git a/examples/nsfw_demo/public/quant_mid/model.json b/models/mobilenet_v2_mid/model.json
old mode 100755
new mode 100644
similarity index 80%
rename from examples/nsfw_demo/public/quant_mid/model.json
rename to models/mobilenet_v2_mid/model.json
index e16fdbda..701e6967
--- a/examples/nsfw_demo/public/quant_mid/model.json
+++ b/models/mobilenet_v2_mid/model.json
@@ -1 +1 @@
-{"format": "graph-model", "generatedBy": "2.2.0", "convertedBy": "TensorFlow.js Converter v1.7.4r1", "userDefinedMetadata": {"signature": {"inputs": {"input:0": {"name": "input:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "224"}, {"size": "224"}, {"size": "3"}]}}}, "outputs": {"Identity:0": {"name": "Identity:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "5"}]}}}}}, "modelTopology": {"node": [{"name": "input", "op": "Placeholder", "attr": {"shape": {"shape": {"dim": [{"size": "-1"}, {"size": "224"}, {"size": "224"}, {"size": "3"}]}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "3"}, {"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/expand/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1344"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "48"}, {"size": "1"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/depthwise/depthwise_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/project/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "48"}, {"size": "24"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv_1/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "448"}, {"size": "1792"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/project/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "24"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/expand/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "24"}, {"size": "144"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/expand/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "144"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "1344"}, {"size": "1"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": 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"StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/project/Conv2D_weights", "shape": [1, 1, 816, 136], "dtype": "float32", "quantization": {"min": -0.8684946789461024, "scale": 0.007237455657884187, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/project/Conv2D_bn_offset", "shape": [136], "dtype": "float32", "quantization": {"min": -7.277607354930803, "scale": 0.0667670399534936, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/expand/Conv2D_weights", "shape": [1, 1, 136, 816], "dtype": "float32", "quantization": {"min": -0.17607897181136936, "scale": 0.0012667551928875493, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/depthwise/depthwise_bn_offset", "shape": [1344], "dtype": "float32", "quantization": {"min": -8.19228334613875, "scale": 0.07062313229429956, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/expand/Conv2D_bn_offset", "shape": [816], "dtype": "float32", "quantization": {"min": -1.7285994754118077, "scale": 0.015163153293086033, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights", "shape": [3, 3, 816, 1], "dtype": "float32", "quantization": {"min": -7.102395214753993, "scale": 0.06230171241012274, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/depthwise/depthwise_bn_offset", "shape": [816], "dtype": "float32", "quantization": {"min": -9.425552261576934, "scale": 0.05928020290299958, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/project/Conv2D_weights", "shape": [1, 1, 816, 136], "dtype": "float32", "quantization": {"min": -2.549794668309829, "scale": 0.01976585014193666, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/project/Conv2D_bn_offset", "shape": [136], "dtype": "float32", "quantization": {"min": -16.37571430206299, "scale": 0.10703081243178424, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/expand/Conv2D_weights", "shape": [1, 1, 136, 816], "dtype": "float32", "quantization": {"min": -0.2139413893222809, "scale": 0.0017393608887990317, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/project/Conv2D_weights", "shape": [1, 1, 1344, 448], "dtype": "float32", "quantization": {"min": -0.5705690570906097, "scale": 0.004457570758520388, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/expand/Conv2D_bn_offset", "shape": [816], "dtype": "float32", "quantization": {"min": -2.9661566860535564, "scale": 0.01913649474873262, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights", "shape": [3, 3, 816, 1], "dtype": "float32", "quantization": {"min": -1.996049763174618, "scale": 0.014464128718656651, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/depthwise/depthwise_bn_offset", "shape": [816], "dtype": "float32", "quantization": {"min": -6.818963368733724, "scale": 0.05013943653480679, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/project/Conv2D_weights", "shape": [1, 1, 816, 224], "dtype": "float32", "quantization": {"min": -1.0638268611010384, "scale": 0.010036102463217343, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/project/Conv2D_bn_offset", "shape": [448], "dtype": "float32", "quantization": {"min": -8.575921683217965, "scale": 0.07029444002637676, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/project/Conv2D_bn_offset", "shape": [224], "dtype": "float32", "quantization": {"min": -15.910008606256222, "scale": 0.11613144968070235, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/expand/Conv2D_weights", "shape": [1, 1, 224, 1344], "dtype": "float32", "quantization": {"min": -0.2625337182306776, "scale": 0.0021519157232022753, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/hub_input/Mul/y", "shape": [], "dtype": "float32", "quantization": {"min": 2.0, "scale": 1.0, "dtype": "uint8"}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/hub_input/Sub/y", "shape": [], "dtype": "float32", "quantization": {"min": 1.0, "scale": 1.0, "dtype": "uint8"}}, {"name": "Func/StatefulPartitionedCall/input/_262", "shape": [1792, 5], "dtype": "float32", "quantization": {"min": -0.11820775200338925, "scale": 0.0009234980625264785, "dtype": "uint8"}}, {"name": "Func/StatefulPartitionedCall/input/_263", "shape": [5], "dtype": "float32", "quantization": {"min": -0.020723959233830958, "scale": 0.00030034723527291244, "dtype": "uint8"}}]}]}
\ No newline at end of file
diff --git a/package.json b/package.json
index 9970d28a..08bea195 100644
--- a/package.json
+++ b/package.json
@@ -3,19 +3,34 @@
   "version": "3.0.0",
   "unpkg": "dist/nsfwjs.min.js",
   "description": "Detect NSFW content client-side",
-  "main": "dist/index.js",
+  "module": "dist/esm/index.js",
+  "main": "dist/cjs/index.js",
+  "exports": {
+    "import": "./dist/esm/index.js",
+    "require": "./dist/cjs/index.js"
+  },
   "scripts": {
-    "test": "yarn build && jest",
     "lint": "tslint -p . -t verbose",
-    "shipit:precheck": "if [ \"$npm_config_user_agent\" != \"${npm_config_user_agent#*yarn}\" ]; then echo 'Use `npm run shipit` instead of yarn.' && exit 1;  fi",
-    "shipit": "npm run shipit:precheck && yarn build && yarn scriptbundle && yarn minbundle && np",
-    "build": "tsc --skipLibCheck",
+    "build:esm": "tsc -p ./tsconfig.esm.json",
+    "build:cjs": "tsc -p ./tsconfig.cjs.json",
+    "build": "yarn build:esm && yarn build:cjs",
+    "bundle:mobilenet_v2": "node ./scripts/bundle-model.mjs mobilenet_v2 1",
+    "bundle:mobilenet_v2_mid": "node ./scripts/bundle-model.mjs mobilenet_v2_mid 2",
+    "bundle:inception_v3": "node ./scripts/bundle-model.mjs inception_v3 6",
+    "bundle:models": "yarn bundle:mobilenet_v2 && yarn bundle:mobilenet_v2_mid && yarn bundle:inception_v3",
+    "postbuild": "yarn bundle:models",
+    "pretest": "yarn build && node ./scripts/pre-test.mjs",
+    "test": "jest",
+    "posttest": "node ./scripts/post-test.mjs",
     "toc": "doctoc .",
     "contributors:add": "all-contributors add",
     "contributors:generate": "all-contributors generate",
     "prep": "yarn && yarn build && cd examples/nsfw_demo/ && yarn add ../../ && cd -",
-    "scriptbundle": "browserify ./dist/nsfwjs.js --standalone nsfwjs --outfile ./dist/bundle.js",
-    "minbundle": "terser dist/bundle.js --compress --mangle --output dist/nsfwjs.min.js"
+    "scriptbundle": "browserify ./dist/cjs/nsfwjs.js -s nsfwjs -o ./dist/browser/bundle.js",
+    "minbundle": "terser dist/browser/bundle.js -c -m -o dist/browser/nsfwjs.min.js",
+    "bundle": "yarn build && yarn scriptbundle && yarn minbundle",
+    "shipit:precheck": "if [ \"$npm_config_user_agent\" != \"${npm_config_user_agent#*yarn}\" ]; then echo 'Use `npm run shipit` instead of yarn.' && exit 1;  fi",
+    "shipit": "npm run shipit:precheck && yarn bundle && np"
   },
   "repository": {
     "type": "git",
@@ -35,7 +50,6 @@
     "doctoc": "^2.2.0",
     "jest": "^28.1.3",
     "jpeg-js": "^0.4.4",
-    "node-fetch": "^2.6.7",
     "np": "^9.2.0",
     "seedrandom": "~3.0.5",
     "terser": "^5.14.2",
diff --git a/scripts/bundle-model.mjs b/scripts/bundle-model.mjs
new file mode 100644
index 00000000..1443b5c2
--- /dev/null
+++ b/scripts/bundle-model.mjs
@@ -0,0 +1,79 @@
+import { exec } from "child_process";
+import {
+  copyFileSync,
+  mkdirSync,
+  readFileSync,
+  unlinkSync,
+  writeFileSync,
+} from "fs";
+
+// The first argument is the model name, the second argument is the number of shards
+const model = process.argv[2];
+const numShards = +process.argv[3];
+const filenames = ["model"];
+
+for (let i = 1; i <= numShards; i++) {
+  filenames.push(`group1-shard${i}of${numShards}`);
+}
+
+const binToJson = (sourcePath, outputPath) => {
+  // Read the binary file and convert it to Base64
+  const binFile = readFileSync(`${sourcePath}`);
+  const base64String = binFile.toString("base64");
+
+  // Write the Base64 string to a .json file
+  writeFileSync(`${outputPath}.json`, JSON.stringify(base64String));
+};
+
+filenames.forEach((filename) => {
+  const sourcePath = `./models/${model}/${filename}`;
+  const outputDir = `./dist/models/${model}`;
+  const outputPath = `${outputDir}/${filename}`;
+
+  // Create the output directory if it doesn't exist
+  mkdirSync(outputDir, { recursive: true });
+
+  if (filename === "model") {
+    // Copy the model.json file
+    copyFileSync(`${sourcePath}.json`, `${outputPath}.json`);
+  } else {
+    binToJson(sourcePath, outputPath);
+  }
+
+  // Browserify the .json file
+  const identifier = filename.replace(/-/g, "_");
+  exec(
+    `browserify ${outputPath}.json -s ${identifier} -o ${outputPath}.js`,
+    (error, stdout, stderr) => {
+      if (error) {
+        console.log(`Error: ${error.message}`);
+        return;
+      }
+      if (stderr) {
+        console.log(`stderr: ${stderr}`);
+        return;
+      }
+      console.log(`stdout: ${stdout}`);
+
+      // Minify the .js file with Terser
+      exec(
+        `terser ${outputPath}.js -c -m -o ${outputPath}.min.js`,
+        (error, stdout, stderr) => {
+          if (error) {
+            console.log(`Error: ${error.message}`);
+            return;
+          }
+          if (stderr) {
+            console.log(`stderr: ${stderr}`);
+            return;
+          }
+          console.log(`stdout: ${stdout}`);
+
+          // Delete the original .js and .json files
+          unlinkSync(`${outputPath}.js`);
+          unlinkSync(`${outputPath}.json`);
+        }
+      );
+    }
+  );
+});
diff --git a/scripts/post-test.mjs b/scripts/post-test.mjs
new file mode 100644
index 00000000..68799ad9
--- /dev/null
+++ b/scripts/post-test.mjs
@@ -0,0 +1,20 @@
+import { readdirSync, unlinkSync } from "fs";
+import { join } from "path";
+
+const dir = "./models";
+
+// Get the names of all subfolders in the dir
+const models = readdirSync(dir, { withFileTypes: true })
+  .filter((dirent) => dirent.isDirectory())
+  .map((dirent) => dirent.name);
+
+models.forEach((model) => {
+  const modelDir = join(dir, model);
+
+  readdirSync(modelDir, { withFileTypes: true })
+    .filter((dirent) => dirent.isFile() && dirent.name.endsWith(".min.js"))
+    .forEach((dirent) => {
+      const file = join(modelDir, dirent.name);
+      unlinkSync(file);
+    });
+});
diff --git a/scripts/pre-test.mjs b/scripts/pre-test.mjs
new file mode 100644
index 00000000..57c9db3b
--- /dev/null
+++ b/scripts/pre-test.mjs
@@ -0,0 +1,23 @@
+import { copyFileSync, readdirSync } from "fs";
+import { join } from "path";
+
+const srcDir = "./dist/models";
+const destDir = "./models";
+
+// Get the names of all subfolders in the srcDir
+const models = readdirSync(srcDir, { withFileTypes: true })
+  .filter((dirent) => dirent.isDirectory())
+  .map((dirent) => dirent.name);
+
+models.forEach((model) => {
+  const modelSrcDir = join(srcDir, model);
+  const modelDestDir = join(destDir, model);
+
+  readdirSync(modelSrcDir, { withFileTypes: true })
+    .filter((dirent) => dirent.isFile() && dirent.name.endsWith(".min.js"))
+    .forEach((dirent) => {
+      const srcFile = join(modelSrcDir, dirent.name);
+      const destFile = join(modelDestDir, dirent.name);
+      copyFileSync(srcFile, destFile);
+    });
+});
diff --git a/src/index.ts b/src/index.ts
index 1b35344f..2e9ad39f 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -1,18 +1,31 @@
 import * as tf from "@tensorflow/tfjs";
 import { NSFW_CLASSES } from "./nsfw_classes";
 
+declare global {
+  namespace NodeJS {
+    interface Global {
+      [x: string]: any;
+    }
+  }
+}
+
+type IOHandler = tf.io.IOHandler;
+type ModelJSON = tf.io.ModelJSON;
+type ModelArtifacts = tf.io.ModelArtifacts;
+type WeightDataBase64 = { [x: string]: string };
+
 export type frameResult = {
   index: number;
   totalFrames: number;
   predictions: Array<predictionType>;
   image: HTMLCanvasElement | ImageData;
-}
+};
 
 export type classifyConfig = {
   topk?: number;
   fps?: number;
   onFrame?: (result: frameResult) => any;
-}
+};
 
 interface nsfwjsOptions {
   size?: number;
@@ -20,34 +33,189 @@ interface nsfwjsOptions {
 }
 
 export type predictionType = {
-  className: typeof NSFW_CLASSES[keyof typeof NSFW_CLASSES]
-  probability: number
-}
+  className: (typeof NSFW_CLASSES)[keyof typeof NSFW_CLASSES];
+  probability: number;
+};
+
+export type ModelName = "MobileNetV2" | "MobileNetV2Mid" | "InceptionV3";
+
+type ModelConfig = {
+  [key in ModelName]: {
+    path: string;
+    numOfWeightBundles: number;
+    options?: nsfwjsOptions;
+  };
+};
+
+const availableModels: ModelConfig = {
+  MobileNetV2: { path: "mobilenet_v2", numOfWeightBundles: 1 },
+  MobileNetV2Mid: {
+    path: "mobilenet_v2_mid",
+    numOfWeightBundles: 2,
+    options: { type: "graph" },
+  },
+  InceptionV3: {
+    path: "inception_v3",
+    numOfWeightBundles: 6,
+    options: { size: 299 },
+  },
+};
 
-const BASE_PATH =
-  // OLD S3 "https://s3.amazonaws.com/ir_public/nsfwjscdn/TFJS_nsfw_mobilenet/tfjs_quant_nsfw_mobilenet/";
-  "https://d1zv2aa70wpiur.cloudfront.net/tfjs_quant_nsfw_mobilenet/";
+const DEFAULT_MODEL_NAME: ModelName = "MobileNetV2";
 const IMAGE_SIZE = 224; // default to Mobilenet v2
 
-export async function load(base?: string, options: nsfwjsOptions = { size: IMAGE_SIZE }) {
+function isModelName(name?: string): name is ModelName {
+  return !!name && name in availableModels;
+}
+
+async function loadWeights(
+  path: string,
+  numOfWeightBundles: number
+): Promise<WeightDataBase64> {
+  const promises = [...Array(numOfWeightBundles)].map(async (_, index) => {
+    const num = index + 1;
+    const bundle = `group1-shard${num}of${numOfWeightBundles}`;
+    const identifier = bundle.replace(/-/g, "_");
+
+    try {
+      const weight =
+        global[identifier] ||
+        (await import(`../models/${path}/${bundle}.min.js`)).default;
+      return { [bundle]: weight };
+    } catch {
+      throw new Error(
+        `Could not load the weight data. Make sure you are importing the ${bundle}.min.js bundle.`
+      );
+    }
+  });
+
+  const data = await Promise.all(promises);
+  return Object.assign({}, ...data);
+}
+
+async function loadModel(modelName: ModelName | string) {
+  if (!isModelName(modelName)) return modelName;
+  const { path, numOfWeightBundles } = availableModels[modelName];
+  let modelJson;
+
+  try {
+    modelJson =
+      global.model || (await import(`../models/${path}/model.min.js`)).default;
+  } catch {
+    throw new Error(
+      `Could not load the model. Make sure you are importing the model.min.js bundle.`
+    );
+  }
+
+  const weightData = await loadWeights(path, numOfWeightBundles);
+  const handler = new JSONHandler(modelJson, weightData);
+  return handler;
+}
+
+export async function load(modelOrUrl?: ModelName): Promise<NSFWJS>;
+
+export async function load(
+  modelOrUrl?: string,
+  options?: nsfwjsOptions
+): Promise<NSFWJS>;
+
+export async function load(
+  modelOrUrl?: string,
+  options: nsfwjsOptions = { size: IMAGE_SIZE }
+) {
   if (tf == null) {
     throw new Error(
       `Cannot find TensorFlow.js. If you are using a <script> tag, please ` +
         `also include @tensorflow/tfjs on the page before using this model.`
     );
   }
-  if (base === undefined) {
-    console.warn("By passing no model path, you're using the model hosted by Infinite.red - Please download and host the model before releasing this in production. See NSFWJS docs for instructions.");
+  if (modelOrUrl === undefined) {
+    modelOrUrl = DEFAULT_MODEL_NAME;
+    console.info(
+      `%cBy not specifying 'modelOrUrl' parameter, you're using the default model: '${modelOrUrl}'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model).`,
+      "color: lightblue"
+    );
+  } else if (isModelName(modelOrUrl)) {
+    console.info(
+      `%cYou're using the model: '${modelOrUrl}'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model).`,
+      "color: lightblue"
+    );
+    options = availableModels[modelOrUrl].options ?? options;
   }
   // Default size is IMAGE_SIZE - needed if just type option is used
-  options.size = options.size || IMAGE_SIZE;
-  const nsfwnet = new NSFWJS(base ?? BASE_PATH, options);
+  options.size = options?.size || IMAGE_SIZE;
+  const modelUrlOrHandler = await loadModel(modelOrUrl);
+  const nsfwnet = new NSFWJS(modelUrlOrHandler, options);
   await nsfwnet.load();
   return nsfwnet;
 }
 
-interface IOHandler {
-  load: () => any;
+class JSONHandler implements IOHandler {
+  private modelJson: ModelJSON;
+  private weightDataBase64: WeightDataBase64;
+
+  constructor(modelJson: ModelJSON, weightDataBase64: WeightDataBase64) {
+    this.modelJson = modelJson;
+    this.weightDataBase64 = weightDataBase64;
+  }
+
+  arrayBufferFromBase64(base64: string) {
+    const binaryString = Buffer.from(base64, "base64").toString("binary");
+    const len = binaryString.length;
+    const bytes = new Uint8Array(len);
+    for (let i = 0; i < len; i++) {
+      bytes[i] = binaryString.charCodeAt(i);
+    }
+    return bytes.buffer;
+  }
+
+  async load() {
+    const modelArtifacts: ModelArtifacts = {
+      modelTopology: this.modelJson.modelTopology,
+      format: this.modelJson.format,
+      generatedBy: this.modelJson.generatedBy,
+      convertedBy: this.modelJson.convertedBy,
+    };
+
+    if (this.modelJson.weightsManifest != null) {
+      const weightSpecs: ModelArtifacts["weightSpecs"] = [];
+      const weightData: Uint8Array[] = [];
+      for (const group of this.modelJson.weightsManifest) {
+        for (const path of group.paths) {
+          const base64 = this.weightDataBase64[path];
+          if (!base64) {
+            throw new Error(
+              `Could not find the weight data. Make sure you are importing the correct weight bundle for the model: ${path}.min.js.`
+            );
+          }
+          const buffer = this.arrayBufferFromBase64(base64);
+          weightData.push(new Uint8Array(buffer));
+        }
+        weightSpecs.push(...group.weights);
+      }
+      modelArtifacts.weightSpecs = weightSpecs;
+
+      const weightDataConcat = new Uint8Array(
+        weightData.reduce((a, b) => a + b.length, 0)
+      );
+      let offset = 0;
+      for (let i = 0; i < weightData.length; i++) {
+        weightDataConcat.set(weightData[i], offset);
+        offset += weightData[i].byteLength;
+      }
+      modelArtifacts.weightData = weightDataConcat.buffer;
+    }
+
+    if (this.modelJson.trainingConfig != null) {
+      modelArtifacts.trainingConfig = this.modelJson.trainingConfig;
+    }
+
+    if (this.modelJson.userDefinedMetadata != null) {
+      modelArtifacts.userDefinedMetadata = this.modelJson.userDefinedMetadata;
+    }
+
+    return modelArtifacts;
+  }
 }
 
 export class NSFWJS {
@@ -55,45 +223,40 @@ export class NSFWJS {
   public model: tf.LayersModel | tf.GraphModel;
 
   private options: nsfwjsOptions;
-  private pathOrIOHandler: string | IOHandler;
+  private urlOrIOHandler: string | IOHandler;
   private intermediateModels: { [layerName: string]: tf.LayersModel } = {};
   private normalizationOffset: tf.Scalar;
 
-  constructor(
-    modelPathBaseOrIOHandler: string | IOHandler,
-    options: nsfwjsOptions
-  ) {
+  constructor(modelUrlOrIOHandler: string | IOHandler, options: nsfwjsOptions) {
     this.options = options;
     this.normalizationOffset = tf.scalar(255);
+    this.urlOrIOHandler = modelUrlOrIOHandler;
 
     if (
-      typeof modelPathBaseOrIOHandler === "string" &&
-      !modelPathBaseOrIOHandler.startsWith("indexeddb://") &&
-      !modelPathBaseOrIOHandler.startsWith("localstorage://")
+      typeof modelUrlOrIOHandler === "string" &&
+      !modelUrlOrIOHandler.startsWith("indexeddb://") &&
+      !modelUrlOrIOHandler.startsWith("localstorage://") &&
+      !modelUrlOrIOHandler.endsWith("model.json")
     ) {
-      if (modelPathBaseOrIOHandler.endsWith("model.json")) {
-        this.pathOrIOHandler = modelPathBaseOrIOHandler;
-      } else {
-        this.pathOrIOHandler = `${modelPathBaseOrIOHandler}model.json`;
-      }
+      this.urlOrIOHandler = `${modelUrlOrIOHandler}model.json`;
     } else {
-      this.pathOrIOHandler = modelPathBaseOrIOHandler;
+      this.urlOrIOHandler = modelUrlOrIOHandler;
     }
   }
 
   async load() {
     const { size, type } = this.options;
     if (type === "graph") {
-      this.model = await tf.loadGraphModel(this.pathOrIOHandler);
+      this.model = await tf.loadGraphModel(this.urlOrIOHandler);
     } else {
       // this is a Layers Model
-      this.model = await tf.loadLayersModel(this.pathOrIOHandler);
+      this.model = await tf.loadLayersModel(this.urlOrIOHandler);
       this.endpoints = this.model.layers.map((l) => l.name);
     }
 
     // Warmup the model.
     const result = tf.tidy(() =>
-      this.model.predict(tf.zeros([1, size, size, 3]))
+      this.model.predict(tf.zeros([1, size!, size!, 3]))
     ) as tf.Tensor;
     await result.data();
     result.dispose();
@@ -119,8 +282,7 @@ export class NSFWJS {
   ): tf.Tensor {
     if (endpoint != null && this.endpoints.indexOf(endpoint) === -1) {
       throw new Error(
-        `Unknown endpoint ${endpoint}. Available endpoints: ` +
-          `${this.endpoints}.`
+        `Unknown endpoint ${endpoint}. Available endpoints: ${this.endpoints}.`
       );
     }
 
@@ -142,13 +304,13 @@ export class NSFWJS {
         const alignCorners = true;
         resized = tf.image.resizeBilinear(
           normalized,
-          [size, size],
+          [size!, size!],
           alignCorners
         );
       }
 
       // Reshape to a single-element batch so we can pass it to predict.
-      const batched = resized.reshape([1, size, size, 3]);
+      const batched = resized.reshape([1, size!, size!, 3]);
 
       let model: tf.LayersModel | tf.GraphModel;
       if (endpoint == null) {
@@ -207,7 +369,10 @@ async function getTopKClasses(
 ): Promise<Array<predictionType>> {
   const values = await logits.data();
 
-  const valuesAndIndices = [];
+  const valuesAndIndices: {
+    value: (typeof values)[number];
+    index: number;
+  }[] = [];
   for (let i = 0; i < values.length; i++) {
     valuesAndIndices.push({ value: values[i], index: i });
   }
diff --git a/tsconfig.cjs.json b/tsconfig.cjs.json
new file mode 100644
index 00000000..da7a1468
--- /dev/null
+++ b/tsconfig.cjs.json
@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "module": "commonjs",
+    "outDir": "./dist/cjs"
+  }
+}
\ No newline at end of file
diff --git a/tsconfig.esm.json b/tsconfig.esm.json
new file mode 100644
index 00000000..732ed150
--- /dev/null
+++ b/tsconfig.esm.json
@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "module": "esnext",
+    "outDir": "./dist/esm"
+  }
+}
\ No newline at end of file
diff --git a/tsconfig.json b/tsconfig.json
index 7e3db3dc..999fdda6 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -1,26 +1,26 @@
 {
-  "compilerOptions": {
-    "module": "commonjs",
-    "moduleResolution": "node",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": true,
-    "preserveConstEnums": true,
-    "declaration": true,
-    "target": "es5",
-    "lib": ["es2015", "dom"],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "esModuleInterop": true,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "experimentalDecorators": true
-  },
-  "include": ["src/"],
-  "exclude": ["node_modules/", "dist/"]
-}
+    "compilerOptions": {
+      "moduleResolution": "node",
+      "noImplicitAny": true,
+      "sourceMap": true,
+      "removeComments": true,
+      "preserveConstEnums": true,
+      "declaration": true,
+      "target": "es5",
+      "lib": ["es2015", "dom"],
+      "noUnusedLocals": true,
+      "noImplicitReturns": true,
+      "noImplicitThis": true,
+      "alwaysStrict": true,
+      "noUnusedParameters": false,
+      "esModuleInterop": true,
+      "pretty": true,
+      "noFallthroughCasesInSwitch": true,
+      "allowUnreachableCode": false,
+      "experimentalDecorators": true,
+      "skipLibCheck": true
+    },
+    "include": ["src/"],
+    "exclude": ["node_modules/", "dist/"]
+  }
+  
\ No newline at end of file
diff --git a/yarn.lock b/yarn.lock
index 39baac3b..87fcd146 100644
--- a/yarn.lock
+++ b/yarn.lock
@@ -4766,7 +4766,7 @@ new-github-release-url@^2.0.0:
   dependencies:
     type-fest "^2.5.1"
 
-node-fetch@^2.6.0, node-fetch@^2.6.7, node-fetch@~2.6.1:
+node-fetch@^2.6.0, node-fetch@~2.6.1:
   version "2.6.7"
   resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.7.tgz#24de9fba827e3b4ae44dc8b20256a379160052ad"
   integrity sha512-ZjMPFEfVx5j+y2yF35Kzx5sF7kDzxuDj6ziH4FFbOp87zKDZNx8yExJIb05OGF4Nlt9IHFIMBkRl41VdvcNdbQ==