diff --git a/examples/notebooks/Tutorial-MultiModal-DeepDive.ipynb b/examples/notebooks/Tutorial-MultiModal-DeepDive.ipynb
deleted file mode 100644
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--- a/examples/notebooks/Tutorial-MultiModal-DeepDive.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "26705673-b9e8-4d6b-b5b6-a1cf47d1df4d",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "source": [
-    "# TextGrad Tutorials: MultiModal Optimization\n",
-    "\n",
-    "![TextGrad](https://github.com/vinid/data/blob/master/logo_full.png?raw=true)\n",
-    "\n",
-    "An autograd engine -- for textual gradients!\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zou-group/TextGrad/blob/main/examples/notebooks/Prompt-Optimization.ipynb)\n",
-    "[![GitHub license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)\n",
-    "[![Arxiv](https://img.shields.io/badge/arXiv-2406.07496-B31B1B.svg)](https://arxiv.org/abs/2406.07496)\n",
-    "[![Documentation Status](https://readthedocs.org/projects/textgrad/badge/?version=latest)](https://textgrad.readthedocs.io/en/latest/?badge=latest)\n",
-    "[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/textgrad)](https://pypi.org/project/textgrad/)\n",
-    "[![PyPI](https://img.shields.io/pypi/v/textgrad)](https://pypi.org/project/textgrad/)\n",
-    "\n",
-    "**Objectives for this tutorial:**\n",
-    "\n",
-    "* Explore some more MultiModal cases in TextGrad. Using a dataset from the literature.\n",
-    "\n",
-    "**Requirements:**\n",
-    "\n",
-    "* You need to have an OpenAI API key to run this tutorial. This should be set as an environment variable as OPENAI_API_KEY.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f10aa9d1-8482-4db7-97af-fa68782e5a4a",
-   "metadata": {},
-   "source": [
-    "## Image Support in TextGrad\n",
-    "\n",
-    "We currently supports PNG and JPEG images. We have a few examples below to show how to use images in TextGrad. If your image is in a different format you should convert it. Here is an example function that \n",
-    "does that for you. \n",
-    "\n",
-    "The way we support images is through the byte format. This is then converted to a Base64 string and sent to the OpenAI/Anthropic API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "db295b99-e94d-44a9-b904-b1aa9cbb7888",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Some utils to read images\n",
-    "\n",
-    "import io\n",
-    "from PIL import Image\n",
-    "\n",
-    "# \n",
-    "def encode_image(image):\n",
-    "    # Convert RGBA to RGB if necessary\n",
-    "    if image.mode == 'RGBA':\n",
-    "        # Create a new image with a white background\n",
-    "        background = Image.new('RGB', image.size, (255, 255, 255))\n",
-    "        # Paste the image on the background.\n",
-    "        background.paste(image, (0, 0), image)\n",
-    "        image = background\n",
-    "\n",
-    "    # Create a BytesIO object\n",
-    "    buffered = io.BytesIO()\n",
-    "\n",
-    "    # Save your image object to this BytesIO object (in JPEG format)\n",
-    "    image.save(buffered, format=\"JPEG\")\n",
-    "\n",
-    "    # Get the byte data from the BytesIO object\n",
-    "    image_byte_data = buffered.getvalue()\n",
-    "    return image_byte_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "24cd0a8e-6b32-4cea-9e0a-3d95260eea49",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-07-07T15:52:16.587781196Z",
-     "start_time": "2024-07-07T15:52:16.174639147Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import textgrad as tg\n",
-    "\n",
-    "# differently from the past tutorials, we now need a multimodal LLM call instead of a standard one!\n",
-    "from textgrad.autograd import MultimodalLLMCall\n",
-    "from textgrad.loss import ImageQALoss\n",
-    "from datasets import load_dataset\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "89c990a4-4784-4c25-9374-c76552d7f974",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "load_dotenv(\".env\", override=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "403c37ff-618c-4b64-a7fd-bf1f514c79b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tg.set_backward_engine(\"gpt-4o\", override=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "d7e06dda-e86f-4ff1-acb6-625934bb54f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = load_dataset(\"derek-thomas/ScienceQA\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d226b34-167c-4a31-8649-a9e4a026d257",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "cb10a125-ab04-40c1-9875-9876a3d7cc11",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Which solution has a higher concentration of blue particles?\n",
-      "\n",
-      "-neither; their concentrations are the same\n",
-      "-Solution B\n",
-      "-Solution A\n"
-     ]
-    }
-   ],
-   "source": [
-    "target_image = ds[\"train\"][10][\"image\"]\n",
-    "target_question = ds[\"train\"][\"question\"][10]\n",
-    "target_options = ds[\"train\"][\"choices\"][10]\n",
-    "target_options = \"\\n-\".join(target_options)\n",
-    "target_correct_answer = ds[\"train\"][\"answer\"][10]\n",
-    "\n",
-    "question_for_model = f\"{target_question}\\n\\n-{target_options}\"\n",
-    "print(question_for_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "839654b8-91da-43f7-ba61-b9bff9799d07",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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",
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",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=378x249>"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target_image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "122e4cf1-cd3f-4818-aefa-a706d7c9ea83",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7595d976-ed94-4499-9735-02dbb20d3291",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "e00c0fef-d1d7-4b2f-8ea9-5547124cc775",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target_image = encode_image(target_image)\n",
-    "\n",
-    "image_variable = tg.Variable(target_image, role_description=\"image to answer a question about\", requires_grad=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "4b8dbf50-fb97-4432-b544-0092ffd1b187",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Variable(value=Solution B has a higher concentration of blue particles. Both solutions have the same solvent volume (35 mL), but Solution B contains more blue particles than Solution A., role=response from the language model, grads=set())"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question_variable = tg.Variable(question_for_model, role_description=\"question to answer\", requires_grad=False)\n",
-    "response = MultimodalLLMCall(\"gpt-4o\")([image_variable, question_variable])\n",
-    "response"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "d875d5b5-331e-4870-99aa-5eb33667d7da",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Variable(value=The existing answer correctly identifies that Solution B has a higher concentration of blue particles. The reasoning provided is accurate: both solutions have the same solvent volume (35 mL), but Solution B contains more blue particles than Solution A. This indicates a higher concentration of blue particles in Solution B. The answer accurately understands the image and provides appropriate knowledge and reasoning logic to address the question., role=evaluation of the response from the language model, grads=set())"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loss_fn = ImageQALoss(\n",
-    "    evaluation_instruction=\"Please evaluate the existing answer to the visual scientific problem without solving it yourself. Verify that the answer accurately understands the image, provides appropriate knowledge and reasoning logic to address the question.\",\n",
-    "    engine=\"gpt-4o\"\n",
-    ")\n",
-    "loss = loss_fn(question=question_variable, image=image_variable, response=response)\n",
-    "loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb26a96c-a229-4a99-8c31-49de8aabdf40",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e3c5b32e-92fd-40a8-a35b-3d78fcfc313a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8a96941-17b5-49b0-9c3b-e5d9fd6bf229",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85befc59-bb38-463a-a70c-9e005e447689",
-   "metadata": {},
-   "source": [
-    "### Direct PNG"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "34af1d5d-ed95-4676-83d9-9ae27de7d221",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import httpx\n",
-    "\n",
-    "image_url = \"https://d2bzx2vuetkzse.cloudfront.net/fit-in/0x450/images_without_background/45ca6024-4bf0-43b8-9a3a-b4a44ecac0bf.png\"\n",
-    "image_data = httpx.get(image_url).content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c65f1f1b-36b0-47ed-8b28-61fa9269148d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_variable = tg.Variable(image_data, role_description=\"image to answer a question about\", requires_grad=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "ca6a613f-ce72-4e58-b009-49e41af1763a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Variable(value=The image shows a small, brown rodent that appears to be a capybara. Capybaras are the largest rodents in the world and are native to South America. They have a distinctive appearance with a large, barrel-shaped body, short legs, and a blunt snout. This particular capybara is sitting and facing to the right., role=response from the language model, grads=set())"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question_variable = tg.Variable(\"What do you see in this image?\", role_description=\"question\", requires_grad=False)\n",
-    "response = MultimodalLLMCall(\"gpt-4o\")([image_variable, question_variable])\n",
-    "response"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d3218180-eb0f-47d9-a3b7-309f1e994144",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}